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+++ b/galaxy_micropita/MicroPITA.py	Thu Jun 03 17:07:36 2021 +0000
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+#!/usr/bin/env python
+"""
+Author: Timothy Tickle
+Description: Class to Run analysis for the microPITA paper
+"""
+
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#####################################################################################
+
+__author__ = "Timothy Tickle"
+__copyright__ = "Copyright 2012"
+__credits__ = ["Timothy Tickle"]
+__license__ = "MIT"
+__maintainer__ = "Timothy Tickle"
+__email__ = "ttickle@sph.harvard.edu"
+__status__ = "Development"
+
+import sys
+import argparse
+from src.breadcrumbs.src.AbundanceTable import AbundanceTable
+from src.breadcrumbs.src.ConstantsBreadCrumbs import ConstantsBreadCrumbs
+from src.breadcrumbs.src.Metric import Metric
+from src.breadcrumbs.src.KMedoids import Kmedoids
+from src.breadcrumbs.src.MLPYDistanceAdaptor import MLPYDistanceAdaptor
+from src.breadcrumbs.src.SVM import SVM
+from src.breadcrumbs.src.UtilityMath import UtilityMath
+
+from src.ConstantsMicropita import ConstantsMicropita
+import csv
+import logging
+import math
+import mlpy
+import numpy as np
+import operator
+import os
+import random
+import scipy.cluster.hierarchy as hcluster
+import scipy.spatial.distance
+from types import *
+
+class MicroPITA:
+	"""
+	Selects samples from a first tier of a multi-tiered study to be used in a second tier.
+	Different methods can be used for selection.
+	The expected input is an abundance table (and potentially a text file of targeted features,
+	if using the targeted features option). Output is a list of samples exhibiting the
+	characteristics of interest.
+	"""
+
+	#Constants
+	#Diversity metrics Alpha
+	c_strInverseSimpsonDiversity = Metric.c_strInvSimpsonDiversity
+	c_strChao1Diversity = Metric.c_strChao1Diversity
+
+	#Diversity metrics Beta
+	c_strBrayCurtisDissimilarity = Metric.c_strBrayCurtisDissimilarity
+
+	#Additive inverses of diversity metrics beta
+	c_strInvBrayCurtisDissimilarity = Metric.c_strInvBrayCurtisDissimilarity
+
+	#Technique Names
+	ConstantsMicropita.c_strDiversity2 = ConstantsMicropita.c_strDiversity+"_C"
+
+	#Targeted feature settings
+	c_strTargetedRanked = ConstantsMicropita.c_strTargetedRanked
+	c_strTargetedAbundance = ConstantsMicropita.c_strTargetedAbundance
+
+	#Technique groupings
+#	c_lsDiversityMethods = [ConstantsMicropita.c_strDiversity,ConstantsMicropita.c_strDiversity2]
+
+	#Converts ecology metrics into standardized method selection names
+	dictConvertAMetricDiversity = {c_strInverseSimpsonDiversity:ConstantsMicropita.c_strDiversity, c_strChao1Diversity:ConstantsMicropita.c_strDiversity2}
+#	dictConvertMicroPITAToAMetric = {ConstantsMicropita.c_strDiversity:c_strInverseSimpsonDiversity, ConstantsMicropita.c_strDiversity2:c_strChao1Diversity}
+	dictConvertBMetricToMethod = {c_strBrayCurtisDissimilarity:ConstantsMicropita.c_strRepresentative}
+	dictConvertInvBMetricToMethod = {c_strBrayCurtisDissimilarity:ConstantsMicropita.c_strExtreme}
+
+	#Linkage used in the Hierarchical clustering
+	c_strHierarchicalClusterMethod = 'average'
+
+####Group 1## Diversity
+	#Testing: Happy path Testing (8)
+	def funcGetTopRankedSamples(self, lldMatrix = None, lsSampleNames = None, iTopAmount = None):
+		"""
+		Given a list of lists of measurements, for each list the indices of the highest values are returned. If lsSamplesNames is given
+			it is treated as a list of string names that is in the order of the measurements in each list. Indices are returned or the sample
+			names associated with the indices.
+		
+		:param	lldMatrix:	List of lists [[value,value,value,value],[value,value,value,value]].
+		:type:	List of lists	List of measurements. Each list is a different measurement. Each measurement in positionally related to a sample.
+		:param	lsSampleNames:	List of sample names positionally related (the same) to each list (Optional).
+		:type:	List of strings	List of strings.
+		:param	iTopAmount:	The amount of top measured samples (assumes the higher measurements are better).
+		:type:	integer	Integer amount of sample names/ indices to return.
+		:return	List:	List of samples to be selected.
+		"""
+		topRankListRet = []
+		for rowMetrics in lldMatrix:
+			#Create 2 d array to hold value and index and sort
+			liIndexX = [rowMetrics,range(len(rowMetrics))]
+			liIndexX[1].sort(key = liIndexX[0].__getitem__,reverse = True)
+
+			if lsSampleNames:
+				topRankListRet.append([lsSampleNames[iIndex] for iIndex in liIndexX[1][:iTopAmount]])
+			else:
+				topRankListRet.append(liIndexX[1][:iTopAmount])
+
+		return topRankListRet
+	
+	####Group 2## Representative Dissimilarity
+	#Testing: Happy path tested 1
+	def funcGetCentralSamplesByKMedoids(self, npaMatrix=None, sMetric=None, lsSampleNames=None, iNumberSamplesReturned=0, istmBetaMatrix=None, istrmTree=None, istrmEnvr=None):
+		"""
+		Gets centroid samples by k-medoids clustering of a given matrix.
+		
+		:param	npaMatrix:	Numpy array where row=features and columns=samples
+		:type:	Numpy array	Abundance Data.
+		:param	sMetric:	String name of beta metric used as the distance metric.
+		:type:	String	String name of beta metric.
+		:param	lsSampleNames:	The names of the sample
+		:type:	List	List of strings
+		:param	iNumberSamplesReturned:	Number of samples to return, each will be a centroid of a sample.
+		:type:	Integer	Number of samples to return
+		:return	List:	List of selected samples.
+		:param	istmBetaMatrix: File with beta-diversity matrix
+		:type:	File stream or file path string
+		"""
+
+		#Count of how many rows
+		sampleCount = npaMatrix.shape[0]
+		if iNumberSamplesReturned > sampleCount:
+			logging.error("MicroPITA.funcGetCentralSamplesByKMedoids:: There are not enough samples to return the amount of samples specified. Return sample count = "+str(iNumberSamplesReturned)+". Sample number = "+str(sampleCount)+".")
+			return False
+
+		#If the cluster count is equal to the sample count return all samples
+		if sampleCount == iNumberSamplesReturned:
+			return list(lsSampleNames)
+
+		#Get distance matrix
+		distanceMatrix=scipy.spatial.distance.squareform(Metric.funcReadMatrixFile(istmMatrixFile=istmBetaMatrix,lsSampleOrder=lsSampleNames)[0]) if istmBetaMatrix else Metric.funcGetBetaMetric(npadAbundancies=npaMatrix, sMetric=sMetric, istrmTree=istrmTree, istrmEnvr=istrmEnvr, lsSampleOrder=lsSampleNames)
+		if type(distanceMatrix) is BooleanType:
+			logging.error("MicroPITA.funcGetCentralSamplesByKMedoids:: Could not read in the supplied distance matrix, returning false.")
+			return False
+
+		# Handle unifrac output
+		if sMetric in [Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted]:
+			distanceMatrix = distanceMatrix[0]
+	
+		#Log distance matrix
+		logging.debug("MicroPITA.funcGetCentralSamplesByKMedoids:: Distance matrix for representative selection using metric="+str(sMetric))
+	
+		distance = MLPYDistanceAdaptor(npaDistanceMatrix=distanceMatrix, fIsCondensedMatrix=True)
+	
+		#Create object to determine clusters/medoids
+		medoidsMaker = Kmedoids(k=iNumberSamplesReturned, dist=distance)
+		#medoidsData includes(1d numpy array, medoids indexes; 
+		#			  1d numpy array, non-medoids indexes;
+		#			  1d numpy array, cluster membership for non-medoids;
+		#			  double, cost of configuration)
+		#npaMatrix is samples x rows
+		#Build a matrix of lists of indicies to pass to the distance matrix
+		lliIndicesMatrix = [[iIndexPosition] for iIndexPosition in xrange(0,len(npaMatrix))]
+		medoidsData = medoidsMaker.compute(np.array(lliIndicesMatrix))
+		logging.debug("MicroPITA.funcGetCentralSamplesByKMedoids:: Results from the kmedoid method in representative selection:")
+		logging.debug(str(medoidsData))
+	
+		#If returning the same amount of clusters and samples
+		#Return centroids
+		selectedIndexes = medoidsData[0]
+		return [lsSampleNames[selectedIndexes[index]] for index in xrange(0,iNumberSamplesReturned)]
+	
+	####Group 3## Highest Dissimilarity
+	#Testing: Happy path tested
+	def funcSelectExtremeSamplesFromHClust(self, strBetaMetric, npaAbundanceMatrix, lsSampleNames, iSelectSampleCount, istmBetaMatrix=None, istrmTree=None, istrmEnvr=None):
+		"""
+		Select extreme samples from HClustering.
+		
+		:param	strBetaMetric:	The beta metric to use for distance matrix generation.
+		:type:	String	The name of the beta metric to use.
+		:param	npaAbundanceMatrix:	Numpy array where row=samples and columns=features.
+		:type:	Numpy Array	Abundance data.
+		:param	lsSampleNames:	The names of the sample.
+		:type:	List	List of strings.
+		:param	iSelectSampleCount:	Number of samples to select (return).
+		:type:	Integer	Integer number of samples returned.
+		:return	Samples:	List of samples.
+		:param	istmBetaMatrix: File with beta-diversity matrix
+		:type:	File stream or file path string
+		"""
+	
+		#If they want all the sample count, return all sample names
+		iSampleCount=len(npaAbundanceMatrix[:,0])
+		if iSelectSampleCount==iSampleCount:
+		  return lsSampleNames
+	
+		#Holds the samples to be returned
+		lsReturnSamplesRet = []
+	
+		#Generate beta matrix
+		#Returns condensed matrix
+		tempDistanceMatrix = scipy.spatial.distance.squareform(Metric.funcReadMatrixFile(istmMatrixFile=istmBetaMatrix,lsSampleOrder=lsSampleNames)[0]) if istmBetaMatrix else Metric.funcGetBetaMetric(npadAbundancies=npaAbundanceMatrix, sMetric=strBetaMetric, istrmTree=istrmTree, istrmEnvr=istrmEnvr, lsSampleOrder=lsSampleNames, fAdditiveInverse = True)
+
+		if strBetaMetric in [Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted]:
+			tempDistanceMatrix = tempDistanceMatrix[0]
+
+		if type(tempDistanceMatrix) is BooleanType:
+			logging.error("MicroPITA.funcSelectExtremeSamplesFromHClust:: Could not read in the supplied distance matrix, returning false.")
+			return False
+
+		if istmBetaMatrix:
+			tempDistanceMatrix = 1-tempDistanceMatrix
+
+		#Feed beta matrix to linkage to cluster
+		#Send condensed matrix
+		linkageMatrix = hcluster.linkage(tempDistanceMatrix, method=self.c_strHierarchicalClusterMethod)
+	
+		#Extract cluster information from dendrogram
+		#The linakge matrix is of the form
+		#[[int1 int2 doube int3],...]
+		#int1 and int1 are the paired samples indexed at 0 and up.
+		#each list is an entry for a branch that is number starting with the first
+		#list being sample count index + 1
+		#each list is then named by an increment as they appear
+		#this means that if a number is in the list and is = sample count or greater it is not
+		#terminal and is instead a branch.
+		#This method just takes the lowest metric measurement (highest distance pairs/clusters)
+		#Works much better than the original technique
+		#get total number of samples
+	
+		iCurrentSelectCount = 0
+		for row in linkageMatrix:
+			#Get nodes ofthe lowest pairing (so the furthest apart pair)
+			iNode1 = int(row[0])
+			iNode2 = int(row[1])
+			#Make sure the nodes are a terminal node (sample) and not a branch in the dendrogram
+			#The branching in the dendrogram will start at the number of samples and increment higher.
+			#Add each of the pair one at a time breaking when enough samples are selected.
+			if iNode1<iSampleCount:
+				lsReturnSamplesRet.append(lsSampleNames[iNode1])
+				iCurrentSelectCount = iCurrentSelectCount + 1
+			if iCurrentSelectCount == iSelectSampleCount:
+				break
+			if iNode2<iSampleCount:
+				lsReturnSamplesRet.append(lsSampleNames[iNode2])
+				iCurrentSelectCount = iCurrentSelectCount + 1
+			if iCurrentSelectCount == iSelectSampleCount:
+				break
+	
+		#Return selected samples
+		return lsReturnSamplesRet
+	
+	####Group 4## Rank Average of user Defined Taxa
+		#Testing: Happy Path Tested
+	def funcGetAverageAbundanceSamples(self, abndTable, lsTargetedFeature, fRank=False):
+		"""
+		Averages feature abundance or ranked abundance. Expects a column 0 of taxa id that is skipped.
+		
+		:param	abndTable:	Abundance Table to analyse
+		:type:	AbundanceTable	Abundance Table
+		:param	lsTargetedFeature:	String names
+		:type:	list	list of string names of features (bugs) which are measured after ranking against the full sample
+		:param  fRank:	Indicates to rank the abundance before getting the average abundance of the features (default false)
+		:type:   boolean	Flag indicating ranking abundance before calculating average feature measurement (false= no ranking)
+		:return	List of lists or boolean:	List of lists or False on error. One internal list per sample indicating the sample,
+				feature average abundance or ranked abundance. Lists will already be sorted.
+				For not Ranked [[sample,average abundance of selected feature,1]]
+				For Ranked [[sample,average ranked abundance, average abundance of selected feature]]
+				Error Returns false
+		"""
+		
+		llAbundance = abndTable.funcGetAverageAbundancePerSample(lsTargetedFeature)
+		if not llAbundance:
+			logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not get average abundance, returned false. Make sure the features (bugs) are spelled correctly and in the abundance table.")
+			return False
+		#Add a space for ranking if needed
+		#Not ranked will be [[sSample,average abundance,1]]
+		#(where 1 will not discriminant ties if used in later functions, so this generalizes)
+		#Ranked will be [[sSample, average rank, average abundance]]
+		llRetAbundance = [[llist[0],-1,llist[1]] for llist in llAbundance]
+		#Rank if needed
+		if fRank:
+			abndRanked = abndTable.funcRankAbundance()
+			if abndRanked == None:
+				logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not rank the abundance table, returned false.")
+				return False
+			llRetRank = abndRanked.funcGetAverageAbundancePerSample(lsTargetedFeature)
+			if not llRetRank:
+				logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not get average ranked abundance, returned false. Make sure the features (bugs) are spelled correctly and in the abundance table.")
+				return False
+			dictRanks = dict(llRetRank)
+			llRetAbundance = [[a[0],dictRanks[a[0]],a[2]] for a in llRetAbundance]
+			
+		#Sort first for ties and then for the main feature
+ 		if not fRank or ConstantsMicropita.c_fBreakRankTiesByDiversity:
+			llRetAbundance = sorted(llRetAbundance, key = lambda sampleData: sampleData[2], reverse = not fRank)
+		if fRank:
+			llRetAbundance = sorted(llRetAbundance, key = lambda sampleData: sampleData[1], reverse = not fRank)
+		return llRetAbundance
+	
+	#Testing: Happy Path Tested
+	def funcSelectTargetedTaxaSamples(self, abndMatrix, lsTargetedTaxa, iSampleSelectionCount, sMethod = ConstantsMicropita.lsTargetedFeatureMethodValues[0]):
+	  """
+	  Selects samples with the highest ranks or abundance of targeted features.
+	  If ranked, select the highest abundance for tie breaking
+	
+	  :param	abndMatrix:	Abundance table to analyse 
+	  :type:	AbundanceTable	Abundance table
+	  :param	lsTargetedTaxa:	List of features
+	  :type:	list	list of strings
+	  :param	iSampleSelectionCount:	Number of samples to select
+	  :type:	integer	integer
+	  :param	sMethod:	Method to select targeted features
+	  :type:	string	String (Can be values found in ConstantsMicropita.lsTargetedFeatureMethodValues)
+	  :return	List of strings:	List of sample names which were selected
+	  List of strings	Empty list is returned on an error.
+	  """
+	
+	  #Check data
+	  if(len(lsTargetedTaxa) < 1):
+		logging.error("MicroPITA.funcSelectTargetedTaxaSamples. Taxa defined selection was requested but no features were given.")
+		return []
+
+	  lsTargetedSamples = self.funcGetAverageAbundanceSamples(abndTable=abndMatrix, lsTargetedFeature=lsTargetedTaxa,
+	  	fRank=sMethod.lower() == self.c_strTargetedRanked.lower())
+	  #If an error occured or the key word for the method was not recognized
+	  if lsTargetedSamples == False: 
+		  logging.error("MicroPITA.funcSelectTargetedTaxaSamples:: Was not able to select for the features given. So targeted feature selection was performed. Check to make sure the features are spelled correctly and exist in the abundance file.")
+		  return []
+	
+	  #Select from results
+	  return [sSample[0] for sSample in lsTargetedSamples[:iSampleSelectionCount]]
+	
+	####Group 5## Random
+	#Testing: Happy path Tested
+	def funcGetRandomSamples(self, lsSamples=None, iNumberOfSamplesToReturn=0):
+		"""
+		Returns random sample names of the number given. No replacement.
+		
+		:param	lsSamples:	List of sample names 
+		:type:	list	list of strings
+		:param	iNumberOfSamplesToReturn:	Number of samples to select
+		:type:	integer	integer.
+		:return	List:	List of selected samples (strings).
+		"""
+
+		#Input matrix sample count
+		sampleCount = len(lsSamples)
+
+		#Return the full matrix if they ask for a return matrix where length == original
+		if(iNumberOfSamplesToReturn >= sampleCount):
+			return lsSamples
+	
+		#Get the random indices for the sample (without replacement)
+		liRandomIndices = random.sample(range(sampleCount), iNumberOfSamplesToReturn)
+	
+		#Create a boolean array of if indexes are to be included in the reduced array
+                return [sSample for iIndex, sSample in enumerate(lsSamples) if iIndex in liRandomIndices]
+
+	#Happy path tested (case 3)
+	def funcGetAveragePopulation(self, abndTable, lfCompress):
+		"""
+		Get the average row per column in the abndtable.
+
+		:param abndTable: AbundanceTable of data to be averaged
+		:type: AbudanceTable
+		:param lfCompress: List of boolean flags (false means to remove sample before averaging
+		:type: List of floats
+		:return List of doubles: 
+		"""
+		if sum(lfCompress) == 0:
+			return []
+
+		#Get the average populations
+		lAverageRet = []
+
+		for sFeature in abndTable.funcGetAbundanceCopy():
+			sFeature = list(sFeature)[1:]
+			sFeature=np.compress(lfCompress,sFeature,axis=0)
+			lAverageRet.append(sum(sFeature)/float(len(sFeature)))
+		return lAverageRet
+
+	#Happy path tested (2 cases)
+	def funcGetDistanceFromAverage(self, abndTable,ldAverage,lsSamples,lfSelected):
+		"""
+		Given an abundance table and an average sample, this returns the distance of each sample
+		(measured using brays-curtis dissimilarity) from the average.
+		The distances are reduced by needing to be in the lsSamples and being a true in the lfSelected
+		(which is associated with the samples in the order of the samples in the abundance table;
+		use abundancetable.funcGetSampleNames() to see the order if needed).
+
+		:param abndTable: Abundance table holding the data to be analyzed.
+		:type: AbundanceTable
+		:param ldAverage: Average population (Average features of the abundance table of samples)
+		:type: List of doubles which represent the average population
+		:param lsSamples: These are the only samples used in the analysis
+		:type: List of strings (sample ids)
+		:param lfSelected: Samples to be included in the analysis
+		:type: List of boolean (true means include)
+		:return: List of distances (doubles)
+		"""
+		#Get the distance from label 1 of all samples in label0 splitting into selected and not selected lists
+		ldSelectedDistances = []
+
+		for sSampleName in [sSample for iindex, sSample in enumerate(lsSamples) if lfSelected[iindex]]:
+			#Get the sample measurements
+			ldSelectedDistances.append(Metric.funcGetBrayCurtisDissimilarity(np.array([abndTable.funcGetSample(sSampleName),ldAverage]))[0])
+		return ldSelectedDistances
+
+	#Happy path tested (1 case)
+	def funcMeasureDistanceFromLabelToAverageOtherLabel(self, abndTable, lfGroupOfInterest, lfGroupOther):
+		"""
+		Get the distance of samples from one label from the average sample of not the label.
+		Note: This assumes 2 classes.  
+
+		:param abndTable: Table of data to work out of.
+		:type: Abundace Table
+		:param lfGroupOfInterest: Boolean indicator of the sample being in the first group.
+		:type: List of floats, true indicating an individual in the group of interest.
+		:param lfGroupOther:	Boolean indicator of the sample being in the other group.
+		:type:	List of floats, true indicating an individual in the 
+		:return List of List of doubles: [list of tuples (string sample name,double distance) for the selected population, list of tuples for the not selected population]
+		"""
+		#Get all sample names
+		lsAllSamples = abndTable.funcGetSampleNames()
+
+		#Get average populations
+		lAverageOther = self.funcGetAveragePopulation(abndTable=abndTable, lfCompress=lfGroupOther)
+
+		#Get the distance from the average of the other label (label 1)
+		ldSelectedDistances = self.funcGetDistanceFromAverage(abndTable=abndTable, ldAverage=lAverageOther,
+			lsSamples=lsAllSamples, lfSelected=lfGroupOfInterest)
+
+		return zip([lsAllSamples[iindex] for iindex, fGroup in enumerate(lfGroupOfInterest) if fGroup],ldSelectedDistances)
+
+	#Happy path tested (1 test case)
+	def funcPerformDistanceSelection(self, abndTable, iSelectionCount, sLabel, sValueOfInterest):
+		"""
+		Given metadata, metadata of one value (sValueOfInterest) is measured from the average (centroid) value of another label group.
+		An iSelectionCount of samples is selected from the group of interest closest to and furthest from the centroid of the other group.
+
+		:params  abndTable: Abundance of measurements
+		:type: AbundanceTable
+		:params iSelectionCount: The number of samples selected per sample.
+		:type: Integer Integer greater than 0
+		:params sLabel: ID of the metadata which is the supervised label
+		:type: String
+		:params sValueOfInterest: Metadata value in the sLabel metadta row of the abundance table which defines the group of interest.
+		:type: String found in the abundance table metadata row indicated by sLabel.
+		:return list list of tuples (samplename, distance) [[iSelectionCount of tuples closest to the other centroid], [iSelectionCount of tuples farthest from the other centroid], [all tuples of samples not selected]]
+		"""
+
+		lsMetadata = abndTable.funcGetMetadata(sLabel)
+		#Other metadata values
+		lsUniqueOtherValues = list(set(lsMetadata)-set(sValueOfInterest))
+
+		#Get boolean indicator of values of interest
+		lfLabelsInterested = [sValueOfInterest == sValue for sValue in lsMetadata]
+
+                #Get the distances of the items of interest from the other metadata values
+		dictDistanceAverages = {}
+                for sOtherLabel in lsUniqueOtherValues:
+			#Get boolean indicator of labels not of interest 
+			lfLabelsOther = [sOtherLabel == sValue for sValue in lsMetadata]
+
+			#Get the distances of data from two different groups to the average of the other
+			ldValueDistances = dict(self.funcMeasureDistanceFromLabelToAverageOtherLabel(abndTable, lfLabelsInterested, lfLabelsOther))
+
+			for sKey in ldValueDistances:
+				dictDistanceAverages[sKey] = ldValueDistances[sKey] + dictDistanceAverages[sKey] if sKey in dictDistanceAverages else ldValueDistances[sKey]
+
+		#Finish average by dividing by length of lsUniqueOtherValues
+		ltpleAverageDistances = [(sKey, dictDistanceAverages[sKey]/float(len(lsUniqueOtherValues))) for sKey in dictDistanceAverages]
+
+                #Sort to extract extremes
+                ltpleAverageDistances = sorted(ltpleAverageDistances,key=operator.itemgetter(1))
+
+		#Get the closest and farthest distances
+		ltupleDiscriminantSamples = ltpleAverageDistances[:iSelectionCount]
+		ltupleDistinctSamples = ltpleAverageDistances[iSelectionCount*-1:]
+
+		#Remove the selected samples from the larger population of distances (better visualization)
+		ldSelected = [tpleSelected[0] for tpleSelected in ltupleDiscriminantSamples+ltupleDistinctSamples]
+
+		#Return discriminant tuples, distinct tuples, other tuples
+		return [ltupleDiscriminantSamples, ltupleDistinctSamples,
+			   [tplData for tplData in ltpleAverageDistances if tplData[0] not in ldSelected]]
+
+	#Run the supervised method surrounding distance from centroids
+	#Happy path tested (3 test cases)
+	def funcRunSupervisedDistancesFromCentroids(self, abundanceTable, fRunDistinct, fRunDiscriminant,
+						xOutputSupFile, xPredictSupFile, strSupervisedMetadata,
+						iSampleSupSelectionCount, lsOriginalSampleNames, lsOriginalLabels, fAppendFiles = False):
+		"""
+		Runs supervised methods based on measuring distances of one label from the centroid of another. NAs are evaluated as theirown group.
+
+		:param	abundanceTable:	AbundanceTable
+		:type:	AbudanceTable	Data to analyze
+		:param	fRunDistinct:	Run distinct selection method
+		:type:	Boolean	boolean (true runs method)
+		:param	fRunDiscriminant:	Run discriminant method
+		:type:	Boolean	boolean (true runs method)
+		:param	xOutputSupFile:	File output from supervised methods detailing data going into the method.
+		:type:	String or FileStream
+		:param	xPredictSupFile:	File output from supervised methods distance results from supervised methods.
+		:type:	String or FileStream
+		:param strSupervisedMetadata:	The metadata that will be used to group samples.
+		:type:	String
+		:param	iSampleSupSelectionCount:	Number of samples to select
+		:type:	Integer	int sample selection count
+		:param lsOriginalSampleNames:	List of the sample names, order is important and should be preserved from the abundanceTable.
+		:type:	List of samples	
+		:param	fAppendFiles:	Indicates that output files already exist and appending is occuring.
+		:type:	Boolean
+		:return	Selected Samples:	A dictionary of selected samples by selection ID
+		Dictionary	{"Selection Method":["SampleID","SampleID"...]}
+		"""
+		#Get labels and run one label against many
+		lstrMetadata = abundanceTable.funcGetMetadata(strSupervisedMetadata)
+		dictlltpleDistanceMeasurements = {}
+		for sMetadataValue in set(lstrMetadata):
+
+			#For now perform the selection here for the label of interest against the other labels
+			dictlltpleDistanceMeasurements.setdefault(sMetadataValue,[]).extend(self.funcPerformDistanceSelection(abndTable=abundanceTable,
+				iSelectionCount=iSampleSupSelectionCount, sLabel=strSupervisedMetadata, sValueOfInterest=sMetadataValue))
+
+		#Make expected output files for supervised methods
+		#1. Output file which is similar to an input file for SVMs
+		#2. Output file that is similar to the probabilitic output of a SVM (LibSVM)
+		#Manly for making output of supervised methods (Distance from Centroid) similar
+		#MicropitaVis needs some of these files
+		if xOutputSupFile:
+			if fAppendFiles:
+				SVM.funcUpdateSVMFileWithAbundanceTable(abndAbundanceTable=abundanceTable, xOutputSVMFile=xOutputSupFile,
+					lsOriginalLabels=lsOriginalLabels, lsSampleOrdering=lsOriginalSampleNames)
+			else:
+				SVM.funcConvertAbundanceTableToSVMFile(abndAbundanceTable=abundanceTable, xOutputSVMFile=xOutputSupFile,
+					sMetadataLabel=strSupervisedMetadata, lsOriginalLabels=lsOriginalLabels, lsSampleOrdering=lsOriginalSampleNames)
+
+		#Will contain the samples selected to return
+		#One or more of the methods may be active so this is why I am extending instead of just returning the result of each method type
+		dictSelectedSamplesRet = dict()
+		for sKey, ltplDistances in dictlltpleDistanceMeasurements.items():
+			if fRunDistinct:
+				dictSelectedSamplesRet.setdefault(ConstantsMicropita.c_strDistinct,[]).extend([ltple[0] for ltple in ltplDistances[1]])
+			if fRunDiscriminant:
+				dictSelectedSamplesRet.setdefault(ConstantsMicropita.c_strDiscriminant,[]).extend([ltple[0] for ltple in ltplDistances[0]])
+
+		if xPredictSupFile:
+			dictFlattenedDistances = dict()
+			[dictFlattenedDistances.setdefault(sKey, []).append(tple)
+				for sKey, lltple in dictlltpleDistanceMeasurements.items()
+				for ltple in lltple for tple in ltple]
+			if fAppendFiles:
+				self._updatePredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xOutputSupFile,
+					dictltpleDistanceMeasurements=dictFlattenedDistances, abundanceTable=abundanceTable, lsOriginalSampleNames=lsOriginalSampleNames)
+			else:
+				self._writeToPredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xOutputSupFile,
+					dictltpleDistanceMeasurements=dictFlattenedDistances, abundanceTable=abundanceTable, lsOriginalSampleNames=lsOriginalSampleNames)
+		return dictSelectedSamplesRet
+
+	#Two happy path test cases
+	def _updatePredictFile(self, xPredictSupFile, xInputLabelsFile, dictltpleDistanceMeasurements, abundanceTable, lsOriginalSampleNames):
+		"""
+		Manages updating the predict file.
+
+		:param	xPredictSupFile: File that has predictions (distances) from the supervised method.
+		:type:	FileStream or String file path
+		:param	xInputLabelsFile: File that as input to the supervised methods.
+		:type:	FileStream or String file path
+		:param	dictltpleDistanceMeasurements: 
+		:type:	Dictionary of lists of tuples {"labelgroup":[("SampleName",dDistance)...], "labelgroup":[("SampleName",dDistance)...]}
+		"""
+
+		if not isinstance(xPredictSupFile, str):
+			xPredictSupFile.close()
+			xPredictSupFile = xPredictSupFile.name
+		csvr = open(xPredictSupFile,'r')
+
+		f = csv.reader(csvr,delimiter=ConstantsBreadCrumbs.c_strBreadCrumbsSVMSpace)
+		lsHeader = f.next()[1:]
+		dictlltpleRead = dict([(sHeader,[]) for sHeader in lsHeader])
+
+		#Read data in 
+		iSampleIndex = 0
+		for sRow in f:
+			sLabel = sRow[0]
+			[dictlltpleRead[lsHeader[iDistanceIndex]].append((lsOriginalSampleNames[iSampleIndex],dDistance)) for iDistanceIndex, dDistance in enumerate(sRow[1:])
+				if not dDistance == ConstantsMicropita.c_sEmptyPredictFileValue]
+			iSampleIndex += 1
+
+		#Combine dictltpleDistanceMeasurements with new data
+		#If they share a key then merge keeping parameter data
+		#If they do not share the key, keep the full data
+		dictNew = {}
+		for sKey in dictltpleDistanceMeasurements.keys():
+			lsSamples = [tple[0] for tple in dictltpleDistanceMeasurements[sKey]]
+			dictNew[sKey] = dictltpleDistanceMeasurements[sKey]+[tple for tple in dictlltpleRead[sKey] if tple[0] not in lsSamples] if sKey in dictlltpleRead.keys() else dictltpleDistanceMeasurements[sKey]
+                for sKey in dictlltpleRead:
+			if sKey not in dictltpleDistanceMeasurements.keys():
+				dictNew[sKey] = dictlltpleRead[sKey]
+
+		#Call writer
+		self._writeToPredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xInputLabelsFile,
+			dictltpleDistanceMeasurements=dictNew, abundanceTable=abundanceTable,
+			lsOriginalSampleNames=lsOriginalSampleNames, fFromUpdate=True)
+
+	#2 happy path test cases
+        def _writeToPredictFile(self, xPredictSupFile, xInputLabelsFile, dictltpleDistanceMeasurements, abundanceTable, lsOriginalSampleNames, fFromUpdate=False):
+		"""
+		Write to the predict file.
+
+		:param	xPredictSupFile: File that has predictions (distances) from the supervised method.
+		:type:	FileStream or String file path
+		:param	xInputLabelsFile: File that as input to the supervised methods.
+		:type:	FileStream or String file path
+		:param	dictltpleDistanceMeasurements: 
+		:type:	Dictionary of lists of tuples {"labelgroup":[("SampleName",dDistance)...], "labelgroup":[("SampleName",dDistance)...]}
+		:param	abundanceTable: An abundance table of the sample data.
+		:type:	AbundanceTable
+		:param	lsOriginalSampleNames: Used if the file is being updated as the sample names so that it may be passed in and consistent with other writing.
+			Otherwise will use the sample names from the abundance table.
+		:type:	List of strings
+		:param	fFromUpdate:	Indicates if this is part of an update to the file or not.
+		:type:	Boolean
+		"""
+
+		xInputLabelsFileName = xInputLabelsFile
+		if not isinstance(xInputLabelsFile,str):
+			xInputLabelsFileName = xInputLabelsFile.name
+		f = csv.writer(open(xPredictSupFile,"w") if isinstance(xPredictSupFile, str) else xPredictSupFile,delimiter=ConstantsBreadCrumbs.c_strBreadCrumbsSVMSpace)
+
+		lsAllSampleNames = abundanceTable.funcGetSampleNames()
+		lsLabels = SVM.funcReadLabelsFromFile(xSVMFile=xInputLabelsFileName, lsAllSampleNames= lsOriginalSampleNames if fFromUpdate else lsAllSampleNames,
+						isPredictFile=False)
+		dictLabels = dict([(sSample,sLabel) for sLabel in lsLabels.keys() for sSample in lsLabels[sLabel]])
+
+		#Dictionay keys will be used to order the predict file
+		lsMeasurementKeys = dictltpleDistanceMeasurements.keys()
+		#Make header
+		f.writerow(["labels"]+lsMeasurementKeys)
+
+		#Reformat dictionary to make it easier to use
+		for sKey in dictltpleDistanceMeasurements:
+			dictltpleDistanceMeasurements[sKey] = dict([ltpl for ltpl in dictltpleDistanceMeasurements[sKey]])
+
+		for sSample in lsOriginalSampleNames:
+			#Make body of file
+			f.writerow([dictLabels.get(sSample,ConstantsMicropita.c_sEmptyPredictFileValue)]+
+				[str(dictltpleDistanceMeasurements[sKey].get(sSample,ConstantsMicropita.c_sEmptyPredictFileValue))
+				for sKey in lsMeasurementKeys])
+
+	def _funcRunNormalizeSensitiveMethods(self, abndData, iSampleSelectionCount, dictSelectedSamples, lsAlphaMetrics, lsBetaMetrics, lsInverseBetaMetrics,
+												fRunDiversity, fRunRepresentative, fRunExtreme, strAlphaMetadata=None,
+												istmBetaMatrix=None, istrmTree=None, istrmEnvr=None, fInvertDiversity=False):
+		"""
+		Manages running methods that are sensitive to normalization. This is called twice, once for the set of methods which should not be normalized and the other
+		for the set that should be normalized.
+	
+		:param	abndData:	Abundance table object holding the samples to be measured.
+		:type:	AbundanceTable
+		:param	iSampleSelectionCount	The number of samples to select per method.
+		:type:	Integer
+		:param	dictSelectedSamples	Will be added to as samples are selected {"Method:["strSelectedSampleID","strSelectedSampleID"...]}.
+		:type:	Dictionary
+		:param	lsAlphaMetrics:	List of alpha metrics to use on alpha metric dependent assays (like highest diversity).
+		:type:	List of strings
+		:param	lsBetaMetrics:	List of beta metrics to use on beta metric dependent assays (like most representative).
+		:type:	List of strings
+		:param	lsInverseBetaMetrics:	List of inverse beta metrics to use on inverse beta metric dependent assays (like most dissimilar).
+		:type:	List of strings
+		:param	fRunDiversity:	Run Diversity based methods (true indicates run).
+		:type:	Boolean	
+		:param	fRunRepresentative:	Run Representative based methods (true indicates run).
+		:type:	Boolean	
+		:param	fRunExtreme:	Run Extreme based methods (true indicates run).
+		:type:	Boolean	
+		:param	istmBetaMatrix:	File that has a precalculated beta matrix
+		:type:	File stream or File path string
+		:return	Selected Samples:	Samples selected by methods.
+				Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Sample ids/names
+		lsSampleNames = abndData.funcGetSampleNames()
+	
+		#Generate alpha metrics and get most diverse
+		if fRunDiversity:
+
+			#Get Alpha metrics matrix
+			internalAlphaMatrix = None
+			#Name of technique
+			strMethod = [strAlphaMetadata] if strAlphaMetadata else lsAlphaMetrics
+
+			#If given an alpha-diversity metadata
+			if strAlphaMetadata:
+				internalAlphaMatrix = [[float(strNum) for strNum in abndData.funcGetMetadata(strAlphaMetadata)]]
+			else:
+				#Expects Observations (Taxa (row) x sample (column))
+				#Returns [[metric1-sample1, metric1-sample2, metric1-sample3],[metric1-sample1, metric1-sample2, metric1-sample3]]
+				internalAlphaMatrix = Metric.funcBuildAlphaMetricsMatrix(npaSampleAbundance = abndData.funcGetAbundanceCopy()
+							if not abndData.funcIsSummed()
+							else abndData.funcGetFeatureAbundanceTable(abndData.funcGetTerminalNodes()).funcGetAbundanceCopy(),
+							lsSampleNames = lsSampleNames, lsDiversityMetricAlpha = lsAlphaMetrics)
+	
+			if internalAlphaMatrix:
+				#Invert measurments
+				if fInvertDiversity:
+					lldNewDiversity = []
+					for lsLine in internalAlphaMatrix:
+						lldNewDiversity.append([1/max(dValue,ConstantsMicropita.c_smallNumber) for dValue in lsLine])
+					internalAlphaMatrix = lldNewDiversity
+				#Get top ranked alpha diversity by most diverse
+				#Expects [[sample1,sample2,sample3...],[sample1,sample2,sample3..],...]
+				#Returns [[sampleName1, sampleName2, sampleNameN],[sampleName1, sampleName2, sampleNameN]]
+				mostDiverseAlphaSamplesIndexes = self.funcGetTopRankedSamples(lldMatrix=internalAlphaMatrix, lsSampleNames=lsSampleNames, iTopAmount=iSampleSelectionCount)
+
+				#Add to results
+				for index in xrange(0,len(strMethod)):
+					strSelectionMethod = self.dictConvertAMetricDiversity.get(strMethod[index],ConstantsMicropita.c_strDiversity+"="+strMethod[index])
+					dictSelectedSamples.setdefault(strSelectionMethod,[]).extend(mostDiverseAlphaSamplesIndexes[index])
+
+		logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Selected Samples 1b")
+		logging.info(dictSelectedSamples)
+	
+		#Generate beta metrics and 
+		if fRunRepresentative or fRunExtreme:
+
+			#Abundance matrix transposed
+			npaTransposedAbundance = UtilityMath.funcTransposeDataMatrix(abndData.funcGetAbundanceCopy(), fRemoveAdornments=True)
+	
+			#Get center selection using clusters/tiling
+			#This will be for beta metrics in normalized space
+			if fRunRepresentative:
+
+				if istmBetaMatrix:
+					#Get representative dissimilarity samples
+					medoidSamples=self.funcGetCentralSamplesByKMedoids(npaMatrix=npaTransposedAbundance, sMetric=ConstantsMicropita.c_custom, lsSampleNames=lsSampleNames, iNumberSamplesReturned=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+
+					if medoidSamples:
+						dictSelectedSamples.setdefault(ConstantsMicropita.c_strRepresentative+"="+ConstantsMicropita.c_custom,[]).extend(medoidSamples)
+				else:
+					logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Performing representative selection on normalized data.")
+					for bMetric in lsBetaMetrics:
+
+						#Get representative dissimilarity samples
+						medoidSamples=self.funcGetCentralSamplesByKMedoids(npaMatrix=npaTransposedAbundance, sMetric=bMetric, lsSampleNames=lsSampleNames, iNumberSamplesReturned=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+
+						if medoidSamples:
+							dictSelectedSamples.setdefault(self.dictConvertBMetricToMethod.get(bMetric,ConstantsMicropita.c_strRepresentative+"="+bMetric),[]).extend(medoidSamples)
+
+			#Get extreme selection using clusters, tiling
+			if fRunExtreme:
+				logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Performing extreme selection on normalized data.")
+				if istmBetaMatrix:
+
+					#Samples for representative dissimilarity
+					#This involves inverting the distance metric,
+					#Taking the dendrogram level of where the number cluster == the number of samples to select
+					#Returning a repersentative sample from each cluster
+					extremeSamples = self.funcSelectExtremeSamplesFromHClust(strBetaMetric=ConstantsMicropita.c_custom, npaAbundanceMatrix=npaTransposedAbundance, lsSampleNames=lsSampleNames, iSelectSampleCount=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+	
+					#Add selected samples
+					if extremeSamples:
+						dictSelectedSamples.setdefault(ConstantsMicropita.c_strExtreme+"="+ConstantsMicropita.c_custom,[]).extend(extremeSamples)
+
+				else:
+					#Run KMedoids with inverse custom distance metric in normalized space
+					for bMetric in lsInverseBetaMetrics:
+
+						#Samples for representative dissimilarity
+						#This involves inverting the distance metric,
+						#Taking the dendrogram level of where the number cluster == the number of samples to select
+						#Returning a repersentative sample from each cluster
+						extremeSamples = self.funcSelectExtremeSamplesFromHClust(strBetaMetric=bMetric, npaAbundanceMatrix=npaTransposedAbundance, lsSampleNames=lsSampleNames, iSelectSampleCount=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+	
+						#Add selected samples
+						if extremeSamples:
+							dictSelectedSamples.setdefault(self.dictConvertInvBMetricToMethod.get(bMetric,ConstantsMicropita.c_strExtreme+"="+bMetric),[]).extend(extremeSamples)
+
+		logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Selected Samples 2,3b")
+		logging.info(dictSelectedSamples)
+		return dictSelectedSamples
+
+	def funcRun(self, strIDName, strLastMetadataName, istmInput,
+					  ostmInputPredictFile, ostmPredictFile, ostmCheckedFile, ostmOutput,
+					  cDelimiter, cFeatureNameDelimiter, strFeatureSelection,
+					  istmFeatures, iCount, lstrMethods, strLastRowMetadata = None, strLabel = None, strStratify = None,
+					  strCustomAlpha = None, strCustomBeta = None, strAlphaMetadata = None, istmBetaMatrix = None, istrmTree = None, istrmEnvr = None, 
+					  iMinSeqs = ConstantsMicropita.c_liOccurenceFilter[0], iMinSamples = ConstantsMicropita.c_liOccurenceFilter[1], fInvertDiversity = False):
+		"""
+		Manages the selection of samples given different metrics.
+
+		:param	strIDName: Sample Id metadata row
+		:type:	String
+		:param	strLastMetadataName: The id of the metadata positioned last in the abundance table.
+		:type:	String	String metadata id.
+		:param	istmInput: File to store input data to supervised methods.
+		:type:	FileStream of String file path
+		:param	ostmInputPredictFile: File to store distances from supervised methods.
+		:type:	FileStream or String file path
+		:param	ostmCheckedFile: File to store the AbundanceTable data after it is being checked.
+		:type:	FileStream or String file path
+		:param	ostmOutPut: File to store sample selection by methods of interest.
+		:type:	FileStream or String file path
+		:param	cDelimiter: Delimiter of abundance table.
+		:type:	Character Char (default TAB).
+		:param	cFeatureNameDelimiter: Delimiter of the name of features (for instance if they contain consensus lineages indicating clades).
+		:type:	Character (default |).
+		:param	stFeatureSelectionMethod: Which method to use to select features in a targeted manner (Using average ranked abundance or average abundance).
+		:type:	String (specific values indicated in ConstantsMicropita.lsTargetedFeatureMethodValues).
+		:param	istmFeatures: File which holds the features of interest if using targeted feature methodology.
+		:type:	FileStream or String file path
+		:param	iCount:	Number of samples to select in each methods, supervised methods select this amount per label if possible.
+		:type:	Integer	integer.
+		:param	lstrMethods: List of strings indicating selection techniques.
+		:type:	List of string method names
+		:param	strLabel: The metadata used for supervised labels.
+		:type:	String
+		:param	strStratify: The metadata used to stratify unsupervised data.
+		:type:	String
+		:param	strCustomAlpha: Custom alpha diversity metric
+		:type:	String
+		:param	strCustomBeta: Custom beta diversity metric
+		:type:	String
+		:param	strAlphaMetadata: Metadata id which is a diveristy metric to use in highest diversity sampling
+		:type:	String
+		:param	istmBetaMatrix: File containing precalculated beta-diversity matrix for representative sampling
+		:type:	FileStream or String file path
+		:param	istrmTree: File containing tree for phylogentic beta-diversity analysis
+		:type:	FileStream or String file path
+		:param	istrmEnvr: File containing environment for phylogentic beta-diversity analysis
+		:type:	FileStream or String file path
+		:param	iMinSeqs: Minimum sequence in the occurence filter which filters all features not with a minimum number of sequences in each of a minimum number of samples.
+		:type:	Integer
+		:param	iMinSamples: Minimum sample count for the occurence filter.
+		:type:	Integer
+		:param	fInvertDiversity: When true will invert diversity measurements before using.
+		:type:	boolean
+		:return	Selected Samples:	Samples selected by methods.
+				Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Holds the top ranked samples from different metrics
+		#dict[metric name] = [samplename,samplename...]
+		selectedSamples = dict()
+	
+		#If a target feature file is given make sure that targeted feature is in the selection methods, if not add
+		if ConstantsMicropita.c_strFeature in lstrMethods:
+		  if not istmFeatures:
+			logging.error("MicroPITA.funcRun:: Did not receive both the Targeted feature file and the feature selection method. MicroPITA did not run.")
+			return False
+
+		#Diversity metrics to run
+		#Use custom metrics if specified
+                #Custom beta metrics set to normalized only, custom alpha metrics set to count only
+		diversityMetricsAlpha = [] if strCustomAlpha or strAlphaMetadata else [MicroPITA.c_strInverseSimpsonDiversity]
+		diversityMetricsBeta = [] if istmBetaMatrix else [strCustomBeta] if strCustomBeta else [MicroPITA.c_strBrayCurtisDissimilarity]
+#		inverseDiversityMetricsBeta = [MicroPITA.c_strInvBrayCurtisDissimilarity]
+		diversityMetricsAlphaNoNormalize = [strAlphaMetadata] if strAlphaMetadata else [strCustomAlpha] if strCustomAlpha else []
+		diversityMetricsBetaNoNormalize = []
+#		inverseDiversityMetricsBetaNoNormalize = []
+
+		#Targeted taxa
+		userDefinedTaxa = []
+	
+		#Perform different flows flags
+		c_RUN_MAX_DIVERSITY_1 = ConstantsMicropita.c_strDiversity in lstrMethods
+		c_RUN_REPRESENTIVE_DISSIMILARITY_2 = ConstantsMicropita.c_strRepresentative in lstrMethods
+		c_RUN_MAX_DISSIMILARITY_3 = ConstantsMicropita.c_strExtreme in lstrMethods
+		c_RUN_RANK_AVERAGE_USER_4 = False
+		if ConstantsMicropita.c_strFeature in lstrMethods:
+			c_RUN_RANK_AVERAGE_USER_4 = True
+			if not istmFeatures:
+				logging.error("MicroPITA.funcRun:: No taxa file was given for taxa selection.") 
+				return False
+			#Read in taxa list, break down to lines and filter out empty strings
+			userDefinedTaxa = filter(None,(s.strip( ) for s in istmFeatures.readlines()))
+		c_RUN_RANDOM_5 = ConstantsMicropita.c_strRandom in lstrMethods
+		c_RUN_DISTINCT = ConstantsMicropita.c_strDistinct in lstrMethods
+		c_RUN_DISCRIMINANT = ConstantsMicropita.c_strDiscriminant in lstrMethods
+
+		#Read in abundance data
+		#Abundance is a structured array. Samples (column) by Taxa (rows) with the taxa id row included as the column index=0
+		#Abundance table object to read in and manage data
+		totalAbundanceTable = AbundanceTable.funcMakeFromFile(xInputFile=istmInput, lOccurenceFilter = [iMinSeqs, iMinSamples],
+								cDelimiter=cDelimiter, sMetadataID=strIDName, sLastMetadataRow=strLastRowMetadata,
+								sLastMetadata=strLastMetadataName, cFeatureNameDelimiter=cFeatureNameDelimiter, xOutputFile=ostmCheckedFile)
+		if not totalAbundanceTable:
+			logging.error("MicroPITA.funcRun:: Could not read in the abundance table. Analysis was not performed."+
+				" This often occurs when the Last Metadata is not specified correctly."+
+				" Please check to make sure the Last Metadata selection is the row of the last metadata,"+
+				" all values after this selection should be microbial measurements and should be numeric.")
+			return False
+
+		lsOriginalLabels = SVM.funcMakeLabels(totalAbundanceTable.funcGetMetadata(strLabel)) if strLabel else strLabel
+
+		dictTotalMetadata = totalAbundanceTable.funcGetMetadataCopy()
+		logging.debug("MicroPITA.funcRun:: Received metadata=" + str(dictTotalMetadata))
+		#If there is only 1 unique value for the labels, do not run the Supervised methods
+		if strLabel and ( len(set(dictTotalMetadata.get(strLabel,[]))) < 2 ):
+			logging.error("The label " + strLabel + " did not have 2 or more values. Labels found=" + str(dictTotalMetadata.get(strLabel,[])))
+			return False
+
+		#Run unsupervised methods###
+		#Stratify the data if need be and drop the old data
+		lStratifiedAbundanceTables = totalAbundanceTable.funcStratifyByMetadata(strStratify) if strStratify else [totalAbundanceTable]
+
+		#For each stratified abundance block or for the unstratfified abundance
+		#Run the unsupervised blocks
+		fAppendSupFiles = False
+		for stratAbundanceTable in lStratifiedAbundanceTables:
+			logging.info("MicroPITA.funcRun:: Running abundance block:"+stratAbundanceTable.funcGetName())
+
+ 			###NOT SUMMED, NOT NORMALIZED			
+			#Only perform if the data is not yet normalized
+			if not stratAbundanceTable.funcIsNormalized( ):
+				#Need to first work with unnormalized data
+				if c_RUN_MAX_DIVERSITY_1 or c_RUN_REPRESENTIVE_DISSIMILARITY_2 or c_RUN_MAX_DISSIMILARITY_3:
+
+					self._funcRunNormalizeSensitiveMethods(abndData=stratAbundanceTable, iSampleSelectionCount=iCount,
+													 dictSelectedSamples=selectedSamples, lsAlphaMetrics=diversityMetricsAlphaNoNormalize,
+													 lsBetaMetrics=diversityMetricsBetaNoNormalize,
+													 lsInverseBetaMetrics=diversityMetricsBetaNoNormalize,
+													 fRunDiversity=c_RUN_MAX_DIVERSITY_1,fRunRepresentative=c_RUN_REPRESENTIVE_DISSIMILARITY_2,
+													 fRunExtreme=c_RUN_MAX_DISSIMILARITY_3, strAlphaMetadata=strAlphaMetadata, 
+                                                                                                         istrmTree=istrmTree, istrmEnvr=istrmEnvr, fInvertDiversity=fInvertDiversity)
+
+
+			#Generate selection by the rank average of user defined taxa
+			#Expects (Taxa (row) by Samples (column))
+			#Expects a column 0 of taxa id that is skipped
+			#Returns [(sample name,average,rank)]
+			#SUMMED AND NORMALIZED
+			stratAbundanceTable.funcSumClades()
+			#Normalize data at this point
+			stratAbundanceTable.funcNormalize()
+			if c_RUN_RANK_AVERAGE_USER_4:
+				selectedSamples[ConstantsMicropita.c_strFeature] = self.funcSelectTargetedTaxaSamples(abndMatrix=stratAbundanceTable,
+						lsTargetedTaxa=userDefinedTaxa, iSampleSelectionCount=iCount, sMethod=strFeatureSelection)
+				logging.info("MicroPITA.funcRun:: Selected Samples Rank")
+				logging.info(selectedSamples)
+
+ 			###SUMMED AND NORMALIZED analysis block
+			#Diversity based metric will move reduce to terminal taxa as needed
+			if c_RUN_MAX_DIVERSITY_1 or c_RUN_REPRESENTIVE_DISSIMILARITY_2 or c_RUN_MAX_DISSIMILARITY_3:
+
+				self._funcRunNormalizeSensitiveMethods(abndData=stratAbundanceTable, iSampleSelectionCount=iCount,
+												 dictSelectedSamples=selectedSamples, lsAlphaMetrics=diversityMetricsAlpha,
+												 lsBetaMetrics=diversityMetricsBeta,
+												 lsInverseBetaMetrics=diversityMetricsBeta,
+												 fRunDiversity=c_RUN_MAX_DIVERSITY_1,fRunRepresentative=c_RUN_REPRESENTIVE_DISSIMILARITY_2,
+												 fRunExtreme=c_RUN_MAX_DISSIMILARITY_3,
+                                                                                                 istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr, fInvertDiversity=fInvertDiversity)
+
+			#5::Select randomly
+			#Expects sampleNames = List of sample names [name, name, name...]
+			if(c_RUN_RANDOM_5):
+				#Select randomly from sample names
+				selectedSamples[ConstantsMicropita.c_strRandom] = self.funcGetRandomSamples(lsSamples=stratAbundanceTable.funcGetSampleNames(), iNumberOfSamplesToReturn=iCount)
+				logging.info("MicroPITA.funcRun:: Selected Samples Random")
+				logging.info(selectedSamples)
+
+			#Perform supervised selection
+			if c_RUN_DISTINCT or c_RUN_DISCRIMINANT:
+ 				if strLabel:
+					dictSelectionRet = self.funcRunSupervisedDistancesFromCentroids(abundanceTable=stratAbundanceTable,
+								fRunDistinct=c_RUN_DISTINCT, fRunDiscriminant=c_RUN_DISCRIMINANT,
+								xOutputSupFile=ostmInputPredictFile,xPredictSupFile=ostmPredictFile,
+								strSupervisedMetadata=strLabel, iSampleSupSelectionCount=iCount,
+								lsOriginalSampleNames = totalAbundanceTable.funcGetSampleNames(),
+								lsOriginalLabels = lsOriginalLabels,
+								fAppendFiles=fAppendSupFiles)
+
+					[selectedSamples.setdefault(sKey,[]).extend(lValue) for sKey,lValue in dictSelectionRet.items()]
+
+					if not fAppendSupFiles:
+						fAppendSupFiles = True
+					logging.info("MicroPITA.funcRun:: Selected Samples Unsupervised")
+					logging.info(selectedSamples)
+		return selectedSamples
+	
+	#Testing: Happy path tested
+	@staticmethod
+	def funcWriteSelectionToFile(dictSelection,xOutputFilePath):
+		"""
+		Writes the selection of samples by method to an output file.
+		
+		:param	dictSelection:	The dictionary of selections by method to be written to a file.
+		:type:	Dictionary	The dictionary of selections by method {"method":["sample selected","sample selected"...]}
+		:param	xOutputFilePath:	FileStream or String path to file inwhich the dictionary is written.
+		:type:	String	FileStream or String path to file
+		"""
+	
+		if not dictSelection:
+			return
+
+		#Open file
+		f = csv.writer(open(xOutputFilePath,"w") if isinstance(xOutputFilePath, str) else xOutputFilePath, delimiter=ConstantsMicropita.c_outputFileDelim )
+
+		#Create output content from dictionary
+		for sKey in dictSelection:
+			f.writerow([sKey]+dictSelection[sKey])
+			logging.debug("MicroPITA.funcRun:: Selected samples output to file:"+str(dictSelection[sKey]))
+	
+	#Testing: Happy Path tested
+	@staticmethod
+	def funcReadSelectionFileToDictionary(xInputFile):
+		"""
+		Reads in an output selection file from micropita and formats it into a dictionary.
+		
+		:param	xInputFile:	String path to file or file stream to read and translate into a dictionary.
+									{"method":["sample selected","sample selected"...]}
+		:type:	FileStream or String Path to file
+		:return	Dictionary:	Samples selected by methods.
+					Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Open file
+		istmReader = csv.reader(open(xInputFile,'r') if isinstance(xInputFile, str) else xInputFile, delimiter = ConstantsMicropita.c_outputFileDelim)
+
+		#Dictionary to hold selection data
+		return dict([(lsLine[0], lsLine[1:]) for lsLine in istmReader])
+
+#Set up arguments reader
+argp = argparse.ArgumentParser( prog = "MicroPITA.py", 
+	description = """Selects samples from abundance tables based on various selection schemes.""" )
+
+args = argp.add_argument_group( "Common", "Commonly modified options" )
+args.add_argument(ConstantsMicropita.c_strCountArgument,"--num", dest="iCount", metavar = "samples", default = 10, type = int, help = ConstantsMicropita.c_strCountHelp)
+args.add_argument("-m","--method", dest = "lstrMethods", metavar = "method", default = [], help = ConstantsMicropita.c_strSelectionTechniquesHelp,
+	choices = ConstantsMicropita.c_lsAllMethods, action = "append")
+
+args = argp.add_argument_group( "Custom", "Selecting and inputing custom metrics" )
+args.add_argument("-a","--alpha", dest = "strAlphaDiversity", metavar = "AlphaDiversity", default = None, help = ConstantsMicropita.c_strCustomAlphaDiversityHelp,  choices = Metric.setAlphaDiversities)
+args.add_argument("-b","--beta", dest = "strBetaDiversity", metavar = "BetaDiversity", default = None, help = ConstantsMicropita.c_strCustomBetaDiversityHelp,  choices = list(Metric.setBetaDiversities)+[Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted])
+args.add_argument("-q","--alphameta", dest = "strAlphaMetadata", metavar = "AlphaDiversityMetadata", default = None, help = ConstantsMicropita.c_strCustomAlphaDiversityMetadataHelp)
+args.add_argument("-x","--betamatrix", dest = "istmBetaMatrix", metavar = "BetaDiversityMatrix", default = None, help = ConstantsMicropita.c_strCustomBetaDiversityMatrixHelp)
+args.add_argument("-o","--tree", dest = "istrmTree", metavar = "PhylogeneticTree", default = None, help = ConstantsMicropita.c_strCustomPhylogeneticTreeHelp)
+args.add_argument("-i","--envr", dest = "istrmEnvr", metavar = "EnvironmentFile", default = None, help = ConstantsMicropita.c_strCustomEnvironmentFileHelp)
+args.add_argument("-f","--invertDiversity", dest = "fInvertDiversity", action="store_true", default = False, help = ConstantsMicropita.c_strInvertDiversityHelp)
+
+args = argp.add_argument_group( "Miscellaneous", "Row/column identifiers and feature targeting options" )
+args.add_argument("-d",ConstantsMicropita.c_strIDNameArgument, dest="strIDName", metavar="sample_id", help= ConstantsMicropita.c_strIDNameHelp)
+args.add_argument("-l",ConstantsMicropita.c_strLastMetadataNameArgument, dest="strLastMetadataName", metavar = "metadata_id", default = None,
+				  help= ConstantsMicropita.c_strLastMetadataNameHelp)
+args.add_argument("-r",ConstantsMicropita.c_strTargetedFeatureMethodArgument, dest="strFeatureSelection", metavar="targeting_method", default=ConstantsMicropita.lsTargetedFeatureMethodValues[0],
+				  choices=ConstantsMicropita.lsTargetedFeatureMethodValues, help= ConstantsMicropita.c_strTargetedFeatureMethodHelp)
+args.add_argument("-t",ConstantsMicropita.c_strTargetedSelectionFileArgument, dest="istmFeatures", metavar="feature_file", type=argparse.FileType("rU"), help=ConstantsMicropita.c_strTargetedSelectionFileHelp)
+args.add_argument("-w",ConstantsMicropita.c_strFeatureMetadataArgument, dest="strLastFeatureMetadata", metavar="Last_Feature_Metadata", default=None, help=ConstantsMicropita.c_strFeatureMetadataHelp)
+
+args = argp.add_argument_group( "Data labeling", "Metadata IDs for strata and supervised label values" )
+args.add_argument("-e",ConstantsMicropita.c_strSupervisedLabelArgument, dest="strLabel", metavar= "supervised_id", help=ConstantsMicropita.c_strSupervisedLabelHelp)
+args.add_argument("-s",ConstantsMicropita.c_strUnsupervisedStratifyMetadataArgument, dest="strUnsupervisedStratify", metavar="stratify_id", 
+				  help= ConstantsMicropita.c_strUnsupervisedStratifyMetadataHelp)
+
+args = argp.add_argument_group( "File formatting", "Rarely modified file formatting options" )
+args.add_argument("-j",ConstantsMicropita.c_strFileDelimiterArgument, dest="cFileDelimiter", metavar="column_delimiter", default="\t", help=ConstantsMicropita.c_strFileDelimiterHelp) 
+args.add_argument("-k",ConstantsMicropita.c_strFeatureNameDelimiterArgument, dest="cFeatureNameDelimiter", metavar="taxonomy_delimiter", default="|", help=ConstantsMicropita.c_strFeatureNameDelimiterHelp) 
+
+args = argp.add_argument_group( "Debugging", "Debugging options - modify at your own risk!" )
+args.add_argument("-v",ConstantsMicropita.c_strLoggingArgument, dest="strLogLevel", metavar = "log_level", default="WARNING", 
+				  choices=ConstantsMicropita.c_lsLoggingChoices, help= ConstantsMicropita.c_strLoggingHelp)
+args.add_argument("-c",ConstantsMicropita.c_strCheckedAbundanceFileArgument, dest="ostmCheckedFile", metavar = "output_qc", type = argparse.FileType("w"), help = ConstantsMicropita.c_strCheckedAbundanceFileHelp)
+args.add_argument("-g",ConstantsMicropita.c_strLoggingFileArgument, dest="ostmLoggingFile", metavar = "output_log", type = argparse.FileType("w"), help = ConstantsMicropita.c_strLoggingFileHelp)
+args.add_argument("-u",ConstantsMicropita.c_strSupervisedInputFile, dest="ostmInputPredictFile", metavar = "output_scaled", type = argparse.FileType("w"), help = ConstantsMicropita.c_strSupervisedInputFileHelp)
+args.add_argument("-p",ConstantsMicropita.c_strSupervisedPredictedFile, dest="ostmPredictFile", metavar = "output_labels", type = argparse.FileType("w"), help = ConstantsMicropita.c_strSupervisedPredictedFileHelp)
+
+argp.add_argument("istmInput", metavar = "input.pcl/biome", type = argparse.FileType("rU"), help = ConstantsMicropita.c_strAbundanceFileHelp,
+	default = sys.stdin)
+argp.add_argument("ostmOutput", metavar = "output.txt", type = argparse.FileType("w"), help = ConstantsMicropita.c_strGenericOutputDataFileHelp,
+	default = sys.stdout)
+
+__doc__ = "::\n\n\t" + argp.format_help( ).replace( "\n", "\n\t" ) + __doc__
+
+def _main( ):
+	args = argp.parse_args( )
+
+	#Set up logger
+	iLogLevel = getattr(logging, args.strLogLevel.upper(), None)
+	logging.basicConfig(stream = args.ostmLoggingFile if args.ostmLoggingFile else sys.stderr, filemode = 'w', level=iLogLevel)
+
+	#Run micropita
+	logging.info("MicroPITA:: Start microPITA")
+	microPITA = MicroPITA()
+
+	#Argparse will append to the default but will not remove the default so I do this here
+	if not len(args.lstrMethods):
+		args.lstrMethods = [ConstantsMicropita.c_strRepresentative]
+
+	dictSelectedSamples = microPITA.funcRun(
+		strIDName		= args.strIDName,
+		strLastMetadataName	= args.strLastMetadataName,
+		istmInput		= args.istmInput,
+		ostmInputPredictFile	= args.ostmInputPredictFile,
+		ostmPredictFile		= args.ostmPredictFile,
+		ostmCheckedFile		= args.ostmCheckedFile,
+		ostmOutput		= args.ostmOutput,
+		cDelimiter		= args.cFileDelimiter,
+		cFeatureNameDelimiter	= args.cFeatureNameDelimiter,
+		istmFeatures		= args.istmFeatures,
+		strFeatureSelection	= args.strFeatureSelection,
+		iCount			= args.iCount,
+		strLastRowMetadata	= args.strLastFeatureMetadata,
+		strLabel		= args.strLabel,
+		strStratify		= args.strUnsupervisedStratify,
+		strCustomAlpha		= args.strAlphaDiversity,
+		strCustomBeta		= args.strBetaDiversity,
+		strAlphaMetadata	= args.strAlphaMetadata,
+		istmBetaMatrix		= args.istmBetaMatrix,
+		istrmTree		= args.istrmTree,
+		istrmEnvr		= args.istrmEnvr,
+		lstrMethods		= args.lstrMethods,
+		fInvertDiversity	= args.fInvertDiversity
+	)
+
+	if not dictSelectedSamples:
+		logging.error("MicroPITA:: Error, did not get a result from analysis.")
+		return -1
+	logging.info("End microPITA")
+
+	#Log output for debugging
+	logging.debug("MicroPITA:: Returned the following samples:"+str(dictSelectedSamples))
+
+	#Write selection to file
+	microPITA.funcWriteSelectionToFile(dictSelection=dictSelectedSamples, xOutputFilePath=args.ostmOutput)
+
+if __name__ == "__main__":
+	_main( )

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/README.txt	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,185 @@
+# Using microPITA commandline #
+
+These common commands can be used on the default data set obtained when downloading microPITA, simply cut and paste them into a commandline in the downloaded microPITA directory.
+
+
+## Expected input file. ##
+
+I. PCL file or BIOM file
+
+BIOM file definition:
+For BIOM file definition please see http://biom-format.org/
+
+PCL file definition:
+Although some defaults can be changed, microPITA expects a PCL file as an input file. Several PCL files are supplied by default in the input directory. A PCL file is a TEXT delimited file similar to an excel spread sheet with the following characteristics.
+
+1. Rows represent metadata and features (bugs), columns represent samples.
+2. The first row by default should be the sample ids.
+3. Metadata rows should be next.
+4. Lastly, rows containing features (bugs) measurements (like abundance) should be after metadata rows.
+5. The first column should contain the ID describing the column. For metadata this may be, for example, "Age" for a row containing the age of the patients donating the samples. For measurements, this should be the feature name (bug name).
+5. By default the file is expected to be TAB delimited.
+6. If a consensus lineage or hierarchy of taxonomy is contained in the feature name, the default delimiter between clades is the pipe ("|").
+
+II. Targeted feature file
+If using the targeted feature methodology, you will need to provide a txt file listing the feature(s) of interest. Each feature should be on it's own line and should be written as found in the input PCL file.
+
+
+## Basic unsupervised methods ##
+Please note, all calls to microPITA should work interchangeably with PCL or BIOM files. BIOM files do not require the --lastmeta or --id arguments.
+
+There are four unsupervised methods which can be performed:
+diverse (maximum diversity), extreme (most dissimilar), representative (representative dissimilarity) and features (targeted feature).
+
+The first three methods are performed as follows (selecting a default 10 samples):
+
+> python MicroPITA.py --lastmeta Label -m representative input/Test.pcl output.txt
+> python MicroPITA.py -m representative input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label -m diverse input/Test.pcl output.txt
+> python MicroPITA.py -m diverse input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label -m extreme input/Test.pcl output.txt
+> python MicroPITA.py -m extreme input/Test.biom output.txt
+
+Each of the previous methods are made up of the following pieces:
+1. python MicroPITA.py to call the microPITA script.
+2. --lastmeta which indicates the keyword (first column value) of the last row that contains metadata (PCL input only).
+3. -m which indicates the method to use in selection.
+4. input/Test.pcl or input/Test.biom which is the first positional argument indicating an input file
+5. output.txt which is the second positional argument indicating the location to write to the output file.
+
+Selecting specific features has additional arguments to consider --targets (required) and --feature_method (optional).
+
+> python MicroPITA.py --lastmeta Label -m features --targets input/TestFeatures.taxa input/Test.pcl output.txt
+> python MicroPITA.py -m features --targets input/TestFeatures.taxa input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label -m features --feature_method abundance --targets input/TestFeatures.taxa input/Test.pcl output.txt
+> python MicroPITA.py -m features --feature_method abundance --targets input/TestFeatures.taxa input/Test.biom output.txt
+
+These additional arguments are described as:
+1. --targets The path to the file that has the features (bugs or clades) of interest. Make sure they are written as they appear in your input file!
+2. --feature_method is the method of selection used and can be based on ranked abundance ("rank") or abundance ("abundance"). The default value is rank.
+To differentiate the methods, rank tends to select samples in which the feature dominates the samples regardless of it's abundance.
+Abundance tends to select samples in which the feature is most abundant without a guarantee that the feature is the most abundant feature in the sample. 
+
+
+## Basic supervised methods ##
+
+Two supervised methods are also available:
+distinct and discriminant
+
+These methods require an additional argument --label which is the first column keyword of the row used to classify samples for the supervised methods.
+These methods can be performed as follows:
+
+> python MicroPITA.py --lastmeta Label --label Label -m distinct input/Test.pcl output.txt
+> python MicroPITA.py --label Label -m distinct input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label --label Label -m discriminant input/Test.pcl output.txt
+> python MicroPITA.py --label Label -m discriminant input/Test.biom output.txt
+
+
+## Custom alpha- and beta-diversities ##
+
+The default alpha diversity for the maximum diversity sampling method is inverse simpson; the default beta-diversity for representative and most dissimilar
+selection is bray-curtis dissimilarity. There are several mechanisms that allow one to change this. You may: 
+
+1. Choose from a selection of alpha-diveristy metrics.
+Note when supplying an alpha diversity. This will affect the maximum diveristy sampling method only. Please make sure to use a diversity metric where the larger number indicates a higher diversity. If this is not the case make sure to use the -f or --invertDiversity flag to invert the metric. The inversion is multiplicative (1/alpha-metric).
+
+> python MicroPITA.py --lastmeta Label -m diverse -a simpson input/Test.pcl output.txt
+> python MicroPITA.py -m diverse -a simpson input/Test.biom output.txt
+
+A case where inverting the metric is needed.
+
+> python MicroPITA.py --lastmeta Label -m diverse -a dominance -f input/Test.pcl output.txt
+> python MicroPITA.py -m diverse -a dominance -f input/Test.biom output.txt
+
+2. Choose from a selection of beta-diversity metrics.
+Note when supplying a beta-diversity. This will effect both the representative and most dissimilar sampling methods. The metric as given will be used for the representative method while 1-beta-metric is used for the most dissimilar.
+
+> python MicroPITA.py --lastmeta Label -m representative -b euclidean input/Test.pcl output.txt
+> python MicroPITA.py -m representative -b euclidean input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label -m extreme -b euclidean input/Test.pcl output.txt
+> python MicroPITA.py -m extreme -b euclidean input/Test.biom output.txt
+
+Note for using Unifrac. Both Weighted and Unweighted unifrac are available for use. Make sure to supply the associated tree (-o, --tree) and environment files 
+(-i,--envr) as well as indicate using Unifrac with (-b,--beta)
+
+> python MicroPITA.py --lastmeta Label -m extreme -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
+> python MicroPITA.py -m extreme -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
+> python MicroPITA.py --lastmeta Label -m extreme -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
+> python MicroPITA.py -m extreme -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
+> python MicroPITA.py --lastmeta Label -m representative -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
+> python MicroPITA.py -m representative -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
+> python MicroPITA.py --lastmeta Label -m representative -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
+> python MicroPITA.py -m representative -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
+
+3. Supply your own custom alpha-diversity per sample as a metadata (row) in your pcl file.
+
+> python MicroPITA.py --lastmeta Label -m diverse -q alpha_custom input/Test.pcl output.txt
+> python MicroPITA.py -m diverse -q alpha_custom input/Test2.biom output.txt
+
+4. Supply your own custom beta diversity as a matrix (provided in a seperate file).
+
+> python MicroPITA.py --lastmeta Label -m representative -x input/Test_Matrix.txt input/Test.pcl output.txt
+> python MicroPITA.py -m representative -x input/Test_Matrix.txt input/Test.biom output.txt
+> python MicroPITA.py --lastmeta Label -m extreme -x input/Test_Matrix.txt input/Test.pcl output.txt
+> python MicroPITA.py -m extreme -x input/Test_Matrix.txt input/Test.biom output.txt
+
+
+## Changing defaults ##
+
+Sample Selection:
+To change the number of selected samples for any method use the -n argument. This example selects 6 representative samples instead of the default 10.
+
+> python MicroPITA.py --lastmeta Label -m representative -n 6 input/Test.pcl output.txt
+> python MicroPITA.py -m representative -n 6 input/Test.biom output.txt
+
+When using a supervised method this indicates how many samples will be selected per class of sample. For example if you are performing supervised selection of 6 samples (-n 6) on a dataset with 2 classes (values) in it's label row, you will get 6 x 2 = 12 samples. If a class does not have 6 samples in it, you will get the max possible for that class. In a scenario where you are selecting 6 samples (-n 6) and have two classes but one class has only 3 samples then you will get 6 + 3 = 9 selected samples.
+
+Stratification:
+To stratify any method use the --stratify argument which is the first column keyword of the metadata row used to stratify samples before selection occurs. (Selection will occur independently within each strata). This example stratifies diverse selection by the "Label".
+
+> python MicroPITA.py --lastmeta Label --stratify Label -m representative input/Test.pcl output.txt
+> python MicroPITA.py --stratify Label -m representative input/Test.biom output.txt
+
+> python MicroPITA.py --lastmeta Label --label Label --stratify StratifyLabel -m distinct input/Test.pcl output.txt
+> python MicroPITA.py --label Label --stratify StratifyLabel -m distinct input/Test2.biom output.txt 
+
+Changing PCL file defaults:
+Some PCL files have feature metadata. These are columns of data that comment on bug features (rows) in the file. An example of this could be a certain taxonomy clade for different bug features. If this type of data exists please use -w or --lastFeatureMetadata to indicate the last column of feature metadata before the first column which is a sample. For an example please look in docs for PCL-Description.txt. This only applys to PCL files.
+
+> python MicroPITA.py --lastmeta Label -m representative -w taxonomy_5 input/FeatureMetadata.pcl output.txt
+
+MicroPITA assumes the first row of the input file is the sample IDs, if it is not you may use --id to indicate the row.
+--id expects the entry in the first column of your input file that matches the row used as Sample Ids. See the input file and the following command as an example.
+This only applys to PCL files.
+
+> python MicroPITA.py --id Sample --lastmeta Label -m representative input/Test.pcl output.txt
+
+MicroPITA assumes the input file is TAB delimited, we strongly recommend you use this convention. If not, you can use --delim to change the delimiter used to read in the file.
+Here is an example of reading the comma delimited file micropita/input/CommaDelim.pcl
+This only applys to PCL files.
+
+> python MicroPITA.py --delim , --lastmeta Label -m representative input/CommaDelim.pcl output.txt
+
+MicroPITA assumes the input file has feature names in which, if the name contains the consensus lineage or full taxonomic hierarchy, it is delimited with a pipe "|". We strongly recommend you use this default. The delimiter of the feature name can be changed using --featdelim. Here is an example of reading in a file with periods as the delimiter.
+This only applys to PCL files.
+
+> python MicroPITA.py --featdelim . --lastmeta Label -m representative input/PeriodDelim.pcl output.txt
+
+
+## Dependencies ##
+Please note the following dependencies need to be installed for micropita to run.
+1. Python 2.x		http://www.python.org/download/
+2. blist		http://pypi.python.org/pypi/blist/
+3. NumPy		http://numpy.scipy.org/
+4. SciPy		http://www.scipy.org/
+5. PyCogent		http://pycogent.sourceforge.net/install.html
+6. mlpy			http://mlpy.sourceforge.net/
+7. mpi4py		http://mpi4py.scipy.org/
+8. biome support 	http://biom-format.org/
+
+This covers how to use microPITA. Thank you for using this software and good luck with all your endeavors!

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/datatypes_conf.xml	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<datatypes>
+    <registration>
+        <datatype extension="micropita" type="galaxy.datatypes.data:Text" subclass="true" display_in_upload="true"/>
+    </registration>
+</datatypes>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/input/FeatureMetadata.pcl	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,8 @@
+ID	taxonomy_0	taxonomy_1	taxonomy_2	taxonomy_3	taxonomy_4	taxonomy_5	70009986	70009894	70008980	70009988	70003470	70007472	70003744	70007476	70003274	70476
+TID	NA	NA	NA	NA	NA	NA	CohortA	CohortA	CohortB	CohortB	CohortB	CohortA	CohortA	CohortB	CohortA	CohortB
+Label	NA	NA	NA	NA	NA	NA	L_Antecubital_fossa	R_Retroauricular_crease	Subgingival_plaque	R_Antecubital_fossa	L_Retroauricular_crease	R_Retroauricular_crease	L_Antecubital_fossa	R_Antecubital_fossa	L_Antecubital_fossa	R_Antecubital_fossa
+72	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	unclassified	1.23	0	12	0	6	0	2	1	2	1
+4904	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	unclassified	0	10	43	6	0	23	0	6	5	53
+1361	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	unclassified	3	0	29	0	45	0	1	1	1	3
+3417	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	unclassified	0	45	34	3	0	0	0	4	0	3
+1368	Bacteria	Firmicutes	Bacilli	Bacillales	Bacillaceae	unclassified	5	0	2	0	6	0	1	1	3	1

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/input/Test.biom	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,1 @@
+{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BreadCrumbs","date": "2013-10-07T16:48:52.647220","matrix_type": "sparse","matrix_element_type": "float","shape": [224, 48],"data": 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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/input/Test2.biom	Thu Jun 03 17:07:36 2021 +0000
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null},{"id": "Root|Taxa_49", "metadata": null},{"id": "Root|Taxa_50", "metadata": null},{"id": "Root|Taxa_51", "metadata": null},{"id": "Root|Taxa_52", "metadata": null},{"id": "Root|Taxa_53", "metadata": null},{"id": "Root|Taxa_54", "metadata": null},{"id": "Root|Taxa_55", "metadata": null},{"id": "Root|Taxa_56", "metadata": null},{"id": "Root|Taxa_57", "metadata": null},{"id": "Root|Taxa_58", "metadata": null},{"id": "Root|Taxa_59", "metadata": null},{"id": "Root|Taxa_60", "metadata": null},{"id": "Root|Taxa_61", "metadata": null},{"id": "Root|Taxa_62", "metadata": null},{"id": "Root|Taxa_63", "metadata": null},{"id": "Root|Taxa_64", "metadata": null},{"id": "Root|Taxa_65", "metadata": null},{"id": "Root|Taxa_66", "metadata": null},{"id": "Root|Taxa_67", "metadata": null},{"id": "Root|Taxa_68", "metadata": null},{"id": "Root|Taxa_69", "metadata": null},{"id": "Root|Taxa_70", "metadata": null},{"id": "Root|Taxa_71", "metadata": null},{"id": "Root|Taxa_72", "metadata": null},{"id": "Root|Taxa_73", "metadata": null},{"id": "Root|Taxa_74", "metadata": null},{"id": "Root|Taxa_75", "metadata": null},{"id": "Root|Taxa_76", "metadata": null},{"id": "Root|Taxa_77", "metadata": null},{"id": "Root|Taxa_78", "metadata": null},{"id": "Root|Taxa_79", "metadata": null},{"id": "Root|Taxa_80", "metadata": null},{"id": "Root|Taxa_81", "metadata": null},{"id": "Root|Taxa_82", "metadata": null},{"id": "Root|Taxa_83", "metadata": null},{"id": "Root|Taxa_84", "metadata": null},{"id": "Root|Taxa_85", "metadata": null},{"id": "Root|Taxa_86", "metadata": null},{"id": "Root|Taxa_87", "metadata": null},{"id": "Root|Taxa_88", "metadata": null},{"id": "Root|Taxa_89", "metadata": null},{"id": "Root|Taxa_90", "metadata": null},{"id": "Root|Taxa_91", "metadata": null},{"id": "Root|Taxa_92", "metadata": null},{"id": "Root|Taxa_93", "metadata": null},{"id": "Root|Taxa_94", "metadata": null},{"id": "Root|Taxa_95", "metadata": null},{"id": "Root|Taxa_96", "metadata": null},{"id": "Root|Taxa_97", "metadata": null},{"id": "Root|Taxa_98", "metadata": null},{"id": "Root|Taxa_99", "metadata": null},{"id": "Root|Taxa_100", "metadata": null},{"id": "Root|Taxa_101", "metadata": null},{"id": "Root|Taxa_102", "metadata": null},{"id": "Root|Taxa_103", "metadata": null},{"id": "Root|Taxa_104", "metadata": null},{"id": "Root|Taxa_105", "metadata": null},{"id": "Root|Taxa_106", "metadata": null},{"id": "Root|Taxa_107", "metadata": null},{"id": "Root|Taxa_108", "metadata": null},{"id": "Root|Taxa_109", "metadata": null},{"id": "Root|Taxa_110", "metadata": null},{"id": "Root|Taxa_111", "metadata": null},{"id": "Root|Taxa_112", "metadata": null},{"id": "Root|Taxa_113", "metadata": null},{"id": "Root|Taxa_114", "metadata": null},{"id": "Root|Taxa_115", "metadata": null},{"id": "Root|Taxa_116", "metadata": null},{"id": "Root|Taxa_117", "metadata": null},{"id": "Root|Taxa_118", "metadata": null},{"id": "Root|Taxa_119", "metadata": null},{"id": "Root|Taxa_120", "metadata": null},{"id": "Root|Taxa_121", "metadata": null},{"id": "Root|Taxa_122", "metadata": null},{"id": "Root|Taxa_123", "metadata": null},{"id": "Root|Taxa_124", "metadata": null},{"id": "Root|Taxa_125", "metadata": null},{"id": "Root|Taxa_126", "metadata": null},{"id": "Root|Taxa_127", "metadata": null},{"id": "Root|Taxa_128", "metadata": null},{"id": "Root|Taxa_129", "metadata": null},{"id": "Root|Taxa_130", "metadata": null},{"id": "Root|Taxa_131", "metadata": null},{"id": "Root|Taxa_132", "metadata": null},{"id": "Root|Taxa_133", "metadata": null},{"id": "Root|Taxa_134", "metadata": null},{"id": "Root|Taxa_135", "metadata": null},{"id": "Root|Taxa_136", "metadata": null},{"id": "Root|Taxa_137", "metadata": null},{"id": "Root|Taxa_138", "metadata": null},{"id": "Root|Taxa_139", "metadata": null},{"id": "Root|Taxa_140", "metadata": null},{"id": "Root|Taxa_141", "metadata": null},{"id": "Root|Taxa_142", "metadata": null},{"id": "Root|Taxa_143", "metadata": null},{"id": "Root|Taxa_144", "metadata": null},{"id": "Root|Taxa_145", "metadata": null},{"id": "Root|Taxa_146", "metadata": null},{"id": "Root|Taxa_147", "metadata": null},{"id": "Root|Taxa_148", "metadata": null},{"id": "Root|Taxa_149", "metadata": null},{"id": "Root|Taxa_150", "metadata": null},{"id": "Root|Taxa_151", "metadata": null},{"id": "Root|Taxa_152", "metadata": null},{"id": "Root|Taxa_153", "metadata": null},{"id": "Root|Taxa_154", "metadata": null},{"id": "Root|Taxa_155", "metadata": null},{"id": "Root|Taxa_156", "metadata": null},{"id": "Root|Taxa_157", "metadata": null},{"id": "Root|Taxa_158", "metadata": null},{"id": "Root|Taxa_159", "metadata": null},{"id": "Root|Taxa_160", "metadata": null},{"id": "Root|Taxa_161", "metadata": null},{"id": "Root|Taxa_162", "metadata": null},{"id": "Root|Taxa_163", "metadata": null},{"id": "Root|Taxa_164", "metadata": null},{"id": "Root|Taxa_165", "metadata": null},{"id": "Root|Taxa_166", "metadata": null},{"id": "Root|Taxa_167", "metadata": null},{"id": "Root|Taxa_168", "metadata": null},{"id": "Root|Taxa_169", "metadata": null},{"id": "Root|Taxa_170", "metadata": null},{"id": "Root|Taxa_171", "metadata": null},{"id": "Root|Taxa_172", "metadata": null},{"id": "Root|Taxa_173", "metadata": null},{"id": "Root|Taxa_174", "metadata": null},{"id": "Root|Taxa_175", "metadata": null},{"id": "Root|Taxa_176", "metadata": null},{"id": "Root|Taxa_177", "metadata": null},{"id": "Root|Taxa_178", "metadata": null},{"id": "Root|Taxa_179", "metadata": null},{"id": "Root|Taxa_180", "metadata": null},{"id": "Root|Taxa_181", "metadata": null},{"id": "Root|Taxa_182", "metadata": null},{"id": "Root|Taxa_183", "metadata": null},{"id": "Root|Taxa_184", "metadata": null},{"id": "Root|Taxa_185", "metadata": null},{"id": "Root|Taxa_186", "metadata": null},{"id": "Root|Taxa_187", "metadata": null},{"id": "Root|Taxa_188", "metadata": null},{"id": "Root|Taxa_189", "metadata": null},{"id": "Root|Taxa_190", "metadata": null},{"id": "Root|Taxa_191", "metadata": null},{"id": "Root|Taxa_192", "metadata": null},{"id": "Root|Taxa_193", "metadata": null},{"id": "Root|Taxa_194", "metadata": null},{"id": "Root|Taxa_195", "metadata": null},{"id": "Root|Taxa_196", "metadata": null},{"id": "Root|Taxa_197", "metadata": null},{"id": "Root|Taxa_198", "metadata": null},{"id": "Root|Taxa_199", "metadata": null},{"id": "Root|Taxa_200", "metadata": null},{"id": "Root|Taxa_201", "metadata": null},{"id": "Root|Taxa_202", "metadata": null},{"id": "Root|Taxa_203", "metadata": null},{"id": "Root|Taxa_204", "metadata": null},{"id": "Root|Taxa_205", "metadata": null},{"id": "Root|Taxa_206", "metadata": null},{"id": "Root|Taxa_207", "metadata": null},{"id": "Root|Taxa_208", "metadata": null},{"id": "Root|Taxa_209", "metadata": null},{"id": "Root|Taxa_210", "metadata": null},{"id": "Root|Taxa_211", "metadata": null},{"id": "Root|Taxa_212", "metadata": null},{"id": "Root|Taxa_213", "metadata": null},{"id": "Root|Taxa_214", "metadata": null},{"id": "Root|Taxa_215", "metadata": null},{"id": "Root|Taxa_216", "metadata": null},{"id": "Root|Taxa_217", "metadata": null},{"id": "Root|Taxa_218", "metadata": null},{"id": "Root|Taxa_219", "metadata": null},{"id": "Root|Taxa_220", "metadata": null},{"id": "Root|Taxa_221", "metadata": null},{"id": "Root|Taxa_222", "metadata": null},{"id": "Root|Taxa_223", "metadata": null}],"columns": [{"id": "Sample_0_D", "metadata": {"Sample": "Sample_0_D", "alpha_custom": "1", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_0_D", "Label": "Class-Two"}},{"id": "Sample_1_D", "metadata": {"Sample": "Sample_1_D", "alpha_custom": "0.99", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_1_D", "Label": "Class-One"}},{"id": "Sample_2_D", "metadata": {"Sample": "Sample_2_D", "alpha_custom": "0.98", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_2_D", "Label": "Class-Two"}},{"id": "Sample_3_D", "metadata": {"Sample": "Sample_3_D", "alpha_custom": "0.97", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_3_D", "Label": "Class-One"}},{"id": "Sample_4_D", "metadata": {"Sample": "Sample_4_D", "alpha_custom": "0.96", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_4_D", "Label": "Class-One"}},{"id": "Sample_5_D", "metadata": {"Sample": "Sample_5_D", "alpha_custom": "0.95", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_5_D", "Label": "Class-One"}},{"id": "Sample_6_D", "metadata": {"Sample": "Sample_6_D", "alpha_custom": "0.94", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_6_D", "Label": "Class-One"}},{"id": "Sample_7_D", "metadata": {"Sample": "Sample_7_D", "alpha_custom": "0.93", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_7_D", "Label": "Class-Two"}},{"id": "Sample_8_D", "metadata": {"Sample": "Sample_8_D", "alpha_custom": "0.92", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_8_D", "Label": "Class-Two"}},{"id": "Sample_9_D", "metadata": {"Sample": "Sample_9_D", "alpha_custom": "0.91", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_9_D", "Label": "Class-One"}},{"id": "Sample_10_D", "metadata": {"Sample": "Sample_10_D", "alpha_custom": "0.9", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_10_D", "Label": "Class-Two"}},{"id": "Sample_11_D", "metadata": {"Sample": "Sample_11_D", "alpha_custom": "0.89", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_11_D", "Label": "Class-One"}},{"id": "Sample_12_D", "metadata": {"Sample": "Sample_12_D", "alpha_custom": "0.88", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_12_D", "Label": "Class-One"}},{"id": "Sample_13_D", "metadata": {"Sample": "Sample_13_D", "alpha_custom": "0.87", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_13_D", "Label": "Class-Two"}},{"id": "Sample_14_D", "metadata": {"Sample": "Sample_14_D", "alpha_custom": "0.86", "StratifyLabel": "1", "Group": "Complex", "ID": "Sample_14_D", "Label": "Class-Two"}},{"id": "Sample_15_D", "metadata": {"Sample": "Sample_15_D", "alpha_custom": "0.85", "StratifyLabel": "2", "Group": "Complex", "ID": "Sample_15_D", "Label": "Class-Two"}},{"id": "Sample_16_R", "metadata": {"Sample": "Sample_16_R", "alpha_custom": "0.84", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity_Feature", "ID": "Sample_16_R", "Label": "Class-Two"}},{"id": "Sample_17_R", "metadata": {"Sample": "Sample_17_R", "alpha_custom": "0.83", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity_Feature", "ID": "Sample_17_R", "Label": "Class-Two"}},{"id": "Sample_18_R", "metadata": {"Sample": "Sample_18_R", "alpha_custom": "0.82", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity_Feature", "ID": "Sample_18_R", "Label": "Class-Two"}},{"id": "Sample_19_R", "metadata": {"Sample": "Sample_19_R", "alpha_custom": "0.81", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_19_R", "Label": "Class-Two"}},{"id": "Sample_20_R", "metadata": {"Sample": "Sample_20_R", "alpha_custom": "0.8", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity", "ID": "Sample_20_R", "Label": "Class-One"}},{"id": "Sample_21_R", "metadata": {"Sample": "Sample_21_R", "alpha_custom": "0.79", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_21_R", "Label": "Class-One"}},{"id": "Sample_22_R", "metadata": {"Sample": "Sample_22_R", "alpha_custom": "0.78", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity", "ID": "Sample_22_R", "Label": "Class-One"}},{"id": "Sample_23_R", "metadata": {"Sample": "Sample_23_R", "alpha_custom": "0.77", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_23_R", "Label": "Class-One"}},{"id": "Sample_24_R", "metadata": {"Sample": "Sample_24_R", "alpha_custom": "0.76", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity", "ID": "Sample_24_R", "Label": "Class-One"}},{"id": "Sample_25_R", "metadata": {"Sample": "Sample_25_R", "alpha_custom": "0.75", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_25_R", "Label": "Class-Two"}},{"id": "Sample_26_R", "metadata": {"Sample": "Sample_26_R", "alpha_custom": "0.74", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity", "ID": "Sample_26_R", "Label": "Class-One"}},{"id": "Sample_27_R", "metadata": {"Sample": "Sample_27_R", "alpha_custom": "0.73", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_27_R", "Label": "Class-Two"}},{"id": "Sample_28_R", "metadata": {"Sample": "Sample_28_R", "alpha_custom": "0.72", "StratifyLabel": "1", "Group": "Moderate_Dissimilarity", "ID": "Sample_28_R", "Label": "Class-Two"}},{"id": "Sample_29_R", "metadata": {"Sample": "Sample_29_R", "alpha_custom": "0.71", "StratifyLabel": "2", "Group": "Moderate_Dissimilarity", "ID": "Sample_29_R", "Label": "Class-One"}},{"id": "Sample_30_E", "metadata": {"Sample": "Sample_30_E", "alpha_custom": "0.7", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_30_E", "Label": "Class-Two"}},{"id": "Sample_31_E", "metadata": {"Sample": "Sample_31_E", "alpha_custom": "0.69", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_31_E", "Label": "Class-Two"}},{"id": "Sample_32_E", "metadata": {"Sample": "Sample_32_E", "alpha_custom": "0.68", "StratifyLabel": "1", "Group": "High_Dissimilarity_Feature", "ID": "Sample_32_E", "Label": "Class-Two"}},{"id": "Sample_33_E", "metadata": {"Sample": "Sample_33_E", "alpha_custom": "0.67", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_33_E", "Label": "Class-Two"}},{"id": "Sample_34_E", "metadata": {"Sample": "Sample_34_E", "alpha_custom": "0.66", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_34_E", "Label": "Class-One"}},{"id": "Sample_35_E", "metadata": {"Sample": "Sample_35_E", "alpha_custom": "0.65", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_35_E", "Label": "Class-One"}},{"id": "Sample_36_E", "metadata": {"Sample": "Sample_36_E", "alpha_custom": "0.64", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_36_E", "Label": "Class-One"}},{"id": "Sample_37_E", "metadata": {"Sample": "Sample_37_E", "alpha_custom": "0.63", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_37_E", "Label": "Class-One"}},{"id": "Sample_38_E", "metadata": {"Sample": "Sample_38_E", "alpha_custom": "0.62", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_38_E", "Label": "Class-One"}},{"id": "Sample_39_E", "metadata": {"Sample": "Sample_39_E", "alpha_custom": "0.61", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_39_E", "Label": "Class-Two"}},{"id": "Sample_40_E", "metadata": {"Sample": "Sample_40_E", "alpha_custom": "0.6", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_40_E", "Label": "Class-One"}},{"id": "Sample_41_E", "metadata": {"Sample": "Sample_41_E", "alpha_custom": "0.59", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_41_E", "Label": "Class-Two"}},{"id": "Sample_42_E", "metadata": {"Sample": "Sample_42_E", "alpha_custom": "0.58", "StratifyLabel": "1", "Group": "High_Dissimilarity", "ID": "Sample_42_E", "Label": "Class-Two"}},{"id": "Sample_43_E", "metadata": {"Sample": "Sample_43_E", "alpha_custom": "0.57", "StratifyLabel": "2", "Group": "High_Dissimilarity", "ID": "Sample_43_E", "Label": "Class-One"}},{"id": "Sample_44_T", "metadata": {"Sample": "Sample_44_T", "alpha_custom": "0.56", "StratifyLabel": "1", "Group": "Targeted_Feature", "ID": "Sample_44_T", "Label": "Class-Two"}},{"id": "Sample_45_T", "metadata": {"Sample": "Sample_45_T", "alpha_custom": "0.55", "StratifyLabel": "2", "Group": "Targeted_Feature", "ID": "Sample_45_T", "Label": "Class-Two"}},{"id": "Sample_46_T", "metadata": {"Sample": "Sample_46_T", "alpha_custom": "0.54", "StratifyLabel": "1", "Group": "Targeted_Feature", "ID": "Sample_46_T", "Label": "Class-Two"}},{"id": "Sample_47_T", "metadata": {"Sample": "Sample_47_T", "alpha_custom": "0.53", "StratifyLabel": "2", "Group": "Targeted_Feature", "ID": "Sample_47_T", "Label": "Class-Two"}}]}
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/micropita.xml	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,208 @@
+<tool id="micropita" name="Run" version="1.0.1">
+<code file="micropita_format_input_selector.py"/> 
+<description>micropita</description>
+<command interpreter="python">micropita_prepare.py 
+--lastmeta $cls_x 
+-m $cond.method_sel
+-n $selected_samples
+--input $inp_data
+--output $out_file1
+--stratify_value $cls_s
+
+#if $cond.method_sel  == "features":
+	--feature_method $cond.feature_method
+	--targets $cond.cls_f
+#end if
+#if $cond.method_sel  == "distinct" or   $cond.method_sel  == "discriminant" :
+	--label_value $cond.cls_L
+#end if
+
+</command>
+  <inputs>
+	<param format="micropita" name="inp_data" type="data" label="Input file"/>
+
+	<param name="cls_x" type="select" label="Last metadata row (Use 'Label' for demo data)"  multiple="False" size ="70"  dynamic_options="get_cols(inp_data,'0')"/>
+
+
+	<param name="cond" type="data_column" data_ref="inp_data" accept_default="true" /> 
+		<conditional name="cond" type="data_column" data_ref="inp_data" accept_default="true">
+		      	<param name="method_sel" type="select" data_ref="inp_data" label="Select method">
+		         	<option value="representative" selected="True">Representative</option>
+				<option value="diverse">Diverse</option>
+				<option value="extreme" >Extreme</option>
+				<option value="features" >Features</option>
+				<option value="distinct" >Distinct (Supervised)</option>
+				<option value="discriminant" >Discriminant (Supervised)</option>
+			</param>
+		     	<when value="representative">
+			</when>
+		      	<when value="diverse">
+		       	</when>
+		      	<when value="extreme">
+		       	</when>
+		      	<when value="features">
+				<param name="cls_f" type="select" label="Targeted feature(s)"  multiple="True" size ="70"  dynamic_options="get_cols_features(inp_data,'0',cls_x)"/>
+				<param name="feature_method" type="select" format="text">
+					<label>Selection type</label>
+						<option value="rank">Rank</option>
+						<option value="abundance">Abundance</option>
+				</param>
+		       	</when>
+		      	<when value="distinct">
+				<param name="cls_L" type="select" label="Label (Use 'Group' for demo data)"  multiple="False" size ="70"  dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
+		       	</when>
+	
+		      	<when value="discriminant">
+				<param name="cls_L" type="select" label="Label (Use 'Group' for demo data)"  multiple="False" size ="70"  dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
+		       	</when>
+
+               	</conditional>
+
+	
+	<param name="cls_s" type="select" label="Stratify by (optional) (Use 'StratifyLabel' for demo data)"  multiple="False" size ="70"  dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
+
+	<param name="selected_samples" type="integer" size="4" value="10"  label="Number of samples to select"/>
+
+	
+
+  </inputs>
+
+  <outputs>
+    <data format="text" name="out_file1" />
+  </outputs>
+  <requirements>
+    <requirement type="set_environment">micropita_SCRIPT_PATH</requirement>
+  </requirements>
+  <tests>
+       <test>
+             <param name="inp_data" value="micropita_input"  ftype="micropita"  />
+             <param name="cls_x" value="5" />
+             <param name="cls_s" value="5" />
+             <param name="selected_samples" value="10" />
+             <param name="cond.method_sel" value="representative" />
+             <output name="out_file1" file="micropita_output"  />
+                <assert_contents>
+                   <has_text text="representative	Sample_22_R	Sample_20_R" />
+                </assert_contents>
+       </test>
+   </tests>
+  <help>
+	  
+
+microbiome: Picking Interesting Taxonomic Abundance
+---------------------------------------------------
+
+
+
+microPITA is a computational tool enabling sample selection in tiered studies. Using tiered-study designs can more efficiently allocate resources, reducing study costs, and maximizing the use of samples. From a survey study, selection of samples can be performed to target various microbial communities including:
+
+1. Samples with the most diverse community (maximum diversity);
+2. Samples dominated by specific microbes (targeted feature);
+3. Samples with microbial communities representative of the survey (representative dissimilarity);
+4. Samples with the most extreme microbial communities in the survey (most dissimilar);
+5. Given a phenotype (like disease state), samples at the border of phenotypes (discriminant) or samples typical of each phenotype (distinct). 
+
+Additionally, methods can leverage clinical metadata by stratifying samples into groups in which samples are subsequently selected. This enables the use of microPITA in cohort studies.
+
+
+.. image:: https://github/biobakery/galaxy_micropita/wiki/HMPStool10PCoA.png
+    :height: 500        
+    :width: 600  
+
+MicroPITA unsupervised method selection in the HMP 16S Gut Microbiome. Selection of 10 samples using targeted feature targeting *Bacteroides* (blue), maximum diversity (orange), representative dissimilarity (purple), and most dissimilar (pink) using Principle Covariance Analysis (PCoA) for ordination. Targeted feature selects samples dominated by *Bacteroides* (upper left) while maximum diversity select more diverse samples away from *Bacteroides* dominant samples. Representative selection selects samples covering the range of samples in the PCoA plot focusing on the higher density central region while maximum dissimilarity selects samples at the periphery of the plot.
+
+
+Intructions to run:
+-------------------
+
+Before running microPita,  you must upload your data using Glaxay's **Get Data -  Upload File**
+Please make sure that you choose **File Format Micropita**
+An example can be found at https://bytebucket.org/biobakery/micropita/wiki/micropita_sample_PCL.txt
+
+Required inputs
+---------------
+
+microPITA requires an input pcl file of metadata and microbial community measurements. Although some defaults can be changed, microPITA expects a PCL file as an input file. A PCL file is a text delimited file similar to an excel spread sheet with the following characteristics.
+
+1. **Rows** represent metadata and features (bugs), **columns** represent samples.
+2. The **first row** by default should be the sample ids.
+3. Metadata rows should be next.
+4. Lastly, rows containing features (bugs) measurements (like abundance) should be after metadata rows.
+5. The **first column** should contain the ID describing the column. For metadata this may be, for example, "Age" for a row containing the age of the patients donating the samples. For measurements, this should be the feature name (bug name).
+6. The file is expected to be TAB delimited.
+7. If a consensus lineage or hierarchy of taxonomy is contained in the feature name, the default delimiter between clades is the pipe ("|").
+
+**Note** MAC users, please save file as windows formatted text.
+
+.. image:: https://bytebucket.org/biobakery/galaxy_micropita/wiki/pcl_diagram.png
+    :height: 500        
+    :width: 600  
+
+Outputs
+-------
+
+The Run MicroPITA module will create one output text file. The output will consist of one line starting with a key word for the selection method and then followed by selected samples delimited by tabs. An example of 6 samples selected by the representative:
+
+representative	sample_1	sample_2	sample_3	sample_4	sample_5	sample_6
+
+
+	  
+	  
+Run microPITA
+-------------
+
+A brief description of the Run micropita module.
+
+**Input file:**
+This should be populated by the Load microPITA module.
+
+**Last metadata row:**
+The row on the input pcl file that is the last metadata. All microbial measurements should follow this row.
+
+**Select method:**
+Select which method to use for sample selection. Selection methods include:
+
+1. Representative. Samples with microbial communities representative of the survey (representative dissimilarity);
+2. Diverse. Samples with the most diverse community (maximum diversity);
+3. Extreme. Samples with the most extreme microbial communities in the survey (most dissimilar);
+4. Features. Samples dominated by specific microbes (targeted feature);
+5. Distinct. Given a phenotype (like disease state), samples typical of each phenotype (Distinct). 
+6. Discriminant. Given a phenotype (like disease state), samples at the border of phenotypes (Discriminant). 
+
+**Targeted feature(s):** (visible with Features method selection only)
+Select 1 or more features to target in sample selection.
+
+**Selection type:** (visible with Features method selection only)
+Rank or Abundance. 
+
+1. Rank indicates selecting samples that have the highest rank of the Targeted features(s), this tends to select sample in which these feature dominant the sample.
+2. Abundance indicates selecting samples that have the highest average abundance of the Targeted features(s), this selects samples where features are most abundant but not necessarily dominant in the community.
+
+**Label:** (visible with supervised method selection only)
+The row which contains the label used to classify the samples from supervised methods.
+
+**Stratify by (optional):**
+The row which contains the groupings the samples will first be placed in before running the selection method on each group. If no grouping is selected, selection methods will be performed on the data set as a whole.
+
+**Number of samples to select:**
+The number of samples to select. If samples are stratified, this is per stratification (or group). If supervised methods are used, this is the number of samples selected per classification group (as defined by the label).
+
+For more information please visit http://huttenhower.sph.harvard.edu/micropita
+
+
+Acknowledgments
+---------------
+Special thanks to Eric Franzosa for developing the above PCL figure!
+
+Citation and Contacts
+---------------------
+
+For more information please visit http://huttenhower.sph.harvard.edu/micropita
+When using MicroPITA please cite:
+Tickle T, Segata N, Waldron L, Weingart G, Huttenhower C. Two-stage microbial community experimental design. (Under review)
+
+Please feel free to contact us at ttickle@hsph.harvard.edu for any questions or comments!
+  
+	  
+ </help>
+</tool>

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/micropita_format_input_selector.py	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,138 @@
+#!/usr/bin/env python
+
+"""
+Author: George Weingart
+Description: Dynamically read columns from input file for UI
+"""
+
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#####################################################################################
+
+__author__ = "George Weingart"
+__copyright__ = "Copyright 2012"
+__credits__ = ["George Weingart"]
+__license__ = "MIT"
+__maintainer__ = "George Weingart"
+__email__ = "george.weingart@gmail.com"
+__status__ = "Development"
+
+import sys,string,time
+from pprint import pprint
+
+def red(st,l):
+	if len(st) <= l: return st 
+	l1,l2 = l/2,l/2
+	return st[:l1]+".."+st[len(st)-l2:]
+
+def get_cols(data,full_names):
+	if data == "": return []
+	max_len =32 
+        fname = data.dataset.file_name
+	input_file = open(fname,'rU')
+	input_lines = input_file.readlines()
+	input_file.close()
+	table_lines = []
+	for x in input_lines:
+		first_column = x.split('\t')[0]
+		table_lines.append(first_column)
+
+	opt = []
+	rc = ''
+	lines = []
+        try:
+		lines = [(red((rc+v.split()[0]),max_len),'%d' % (i+1),False) for i,v in enumerate(table_lines) if v]
+
+	except:
+		l1 = '*ALL*'
+		l2 = 1
+		l3 = False
+		MyList = [l1,l2,l3]
+		lines.append(MyList)
+	return opt+lines
+
+def get_cols_add_line(data,full_names,lastmeta):
+	if data == "": return []
+	display_to = 1
+	try:
+		display_to = int(lastmeta)
+	except:		
+		pass
+
+	max_len = 32 
+        fname = data.dataset.file_name
+	input_file = open(fname,'rU')
+	input_lines = input_file.readlines()
+	input_file.close()
+	table_lines = []
+	for x in input_lines:
+		first_column = x.split('\t')[0]
+		table_lines.append(first_column)
+	table_lines.insert(0,'-')
+	if  not display_to == 1:
+		del  table_lines[display_to + 1:]
+
+
+	opt = []
+	rc = ''
+	lines = []
+        try:
+		lines = [(red((rc+v.split()[0]),max_len),'%d' % (i+1),False) for i,v in enumerate(table_lines) if v]
+
+	except:
+		l1 = '*ALL*'
+		l2 = 1
+		l3 = False
+		MyList = [l1,l2,l3]
+		lines.append(MyList)
+	return opt+lines
+
+def get_cols_features(data,full_names,lastmeta):
+	if data == "": return []
+	display_from = 1
+	try:
+		display_from = int(lastmeta)
+	except:		
+		pass
+	max_len = 32 
+        fname = data.dataset.file_name
+	input_file = open(fname,'rU')
+
+	input_lines = input_file.readlines()
+	input_file.close()
+	table_lines = []
+	for x in input_lines:
+		first_column = x.split('\t')[0]
+		table_lines.append(first_column)
+	
+	opt = []
+	rc = ''
+	del table_lines[:display_from]
+	lines = []
+        try:
+		lines = [(red((rc+v.split()[0]),max_len),'%d' % (i+1),False) for i,v in enumerate(table_lines) if v]
+
+	except:
+		l1 = '*ALL*'
+		l2 = 1
+		l3 = False
+		MyList = [l1,l2,l3]
+		lines.append(MyList)
+	return opt+lines

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/micropita_galaxy_ReadMe.txt	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,34 @@
+#Installation instructions for microPITA in a galaxy environment.
+These instructions require the Mercurial versioning system, galaxy, and an internet connection.
+
+#For general reference about microPita please refer to:
+```
+https://bitbucket.org/biobakery/micropita
+```
+
+
+
+
+#Installation Instructions
+In the  "galaxy-dist/tools" directory install micropita by typing in a terminal:
+```
+hg clone https://bitbucket.org/biobakery/micropita
+```
+
+
+Update member tool_conf.xml  in the galaxy directory adding the following: 
+```
+  <section name="micropita" id="micropita">
+    <tool file="micropita/galaxy/micropita.xml"/>
+  </section>
+```
+
+Update member datatypes_conf.xml  in the galaxy directory adding the following:
+```
+	<datatype extension="micropita" type="galaxy.datatypes.data:Text" subclass="true" display_in_upload="true"/>
+```
+
+Copy the 2 *.png  members   to /galaxy/static/images
+
+Recycle galaxy
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/micropita_prepare.py	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,203 @@
+#!/usr/bin/env python
+
+"""
+Author: George Weingart
+Description: Prepare parameters to call micropita
+"""
+
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#####################################################################################
+
+__author__ = "George Weingart"
+__copyright__ = "Copyright 2012"
+__credits__ = ["George Weingart"]
+__license__ = "MIT"
+__maintainer__ = "George Weingart"
+__email__ = "george.weingart@gmail.com"
+__status__ = "Development"
+
+import argparse
+from cStringIO import StringIO
+import sys,string,time
+import os
+from time import gmtime, strftime
+from pprint import pprint
+import subprocess  
+import blist
+import shlex
+import tempfile
+
+##################################################################################
+#   Modification by George Weingart    5/6/2014                                  #
+#   Using subprocess to invoke the calls to Micropita                            #
+#   and allocating the temporary file using trmpfile                             #
+##################################################################################
+
+
+
+
+##################################################################################
+#  Decode Parms                                                                  #
+##################################################################################
+def read_params(x):
+	parser = argparse.ArgumentParser(description='Micropita Annotate Argparser')
+	parser.add_argument('--input', action="store",dest='inputname')
+	parser.add_argument('--output', action="store",dest='outputname')
+	parser.add_argument('-m', action="store",dest='MParameter')
+	parser.add_argument('-n', action="store",dest='NSamples')
+	parser.add_argument('--lastmeta', action="store",dest='lastmeta')
+	parser.add_argument('--stratify_value', action="store",dest='stratify_value')
+
+
+	try:
+		parser.add_argument('--feature_method', action="store",dest='feature_method')
+	except:
+		pass
+	try:
+		parser.add_argument('--targets', action="store",dest='targets')
+	except:
+		pass
+	try:
+		parser.add_argument('--label_value', action="store",dest='label_value')
+	except:
+		pass
+	return  parser
+	
+
+##################################################################################
+#  Main Program                                                                  #
+##################################################################################
+parser = read_params( sys.argv )
+results = parser.parse_args()
+#root_dir = os.environ.get('micropita_SCRIPT_PATH')
+root_dir = os.path.dirname(os.path.realpath(__file__))  #Find the current directory where the program resides  GW 20160810
+
+
+fname =  results.inputname
+input_file = open(fname,'rU')
+input_lines = input_file.readlines()
+input_file.close()
+table_lines = []
+for x in input_lines:
+	first_column = x.split('\t')[0]
+	table_lines.append(first_column)
+
+
+
+FileTimeStamp =  strftime("%Y%m%d%H%M%S", gmtime())
+LastMetaInt = 0
+if results.lastmeta and not results.lastmeta == "None":
+	LastMetaInt = int(results.lastmeta) - 1
+
+StratifyValueInt = 0
+if  results.stratify_value and not   results.stratify_value == "None":
+	StratifyValueInt = int(results.stratify_value) - 2 
+
+LabelValueInt = 0
+if results.label_value and not results.label_value == "None":
+	LabelValueInt = int(results.label_value) - 1
+
+stratify_string = ""
+q = '"'
+if  not results.stratify_value == '1':
+	stratify_string = " --stratify " + q + table_lines[StratifyValueInt] + q + " "
+
+if results.MParameter == "features":
+	TBTargets = list()
+	TableTargets = results.targets.split(',')
+	for t in TableTargets:
+		tb_entry = int(t) + LastMetaInt 
+		TBTargets.append(int(tb_entry))
+
+
+	OutTargetsFile  = tempfile.NamedTemporaryFile('w', delete=False )   
+	TempTargetsFileName = OutTargetsFile.name 
+	indx = -1
+	for  c in table_lines:
+		indx+=1
+		if  indx in TBTargets:
+			OutputString = table_lines[indx] + "\n"
+			OutTargetsFile.write(OutputString)
+	OutTargetsFile.close()
+	os_command = "python " + \
+		root_dir + \
+		"/MicroPITA.py "+\
+		"--lastmeta " + table_lines[LastMetaInt]+ " " +\
+		"--feature_method " + results.feature_method + " " + \
+		"--target " + TempTargetsFileName + " " +\
+		"-m " + results.MParameter + " " + \
+		"-n " + results.NSamples + " " +\
+	stratify_string + " " +\
+	results.inputname + " " +\
+	results.outputname
+	#print os_command
+	os.system(os_command)
+	argsx = shlex.split(os_command)					#Split the command
+	try:
+			subprocess.check_call(argsx , shell=False)
+	except:
+			print "The call to micropita failed============="
+	sys.exit(0)
+
+
+
+if results.MParameter == "representative"\
+or results.MParameter == "diverse"\
+or results.MParameter == "extreme": 
+		os_command = "python " + \
+		root_dir +  \
+		"/MicroPITA.py "+\
+		"--lastmeta " + table_lines[LastMetaInt]+ " " +\
+		"-m " + results.MParameter + " " + \
+		"-n " + results.NSamples + " " +\
+		stratify_string + " " + \
+		results.inputname + " " +\
+		results.outputname
+		argsx = shlex.split(os_command)					#Split the command
+		try:
+				###os.system(os_command)
+				subprocess.check_call(argsx , shell=False)
+		except:
+				print "The call to micropita failed============="
+		sys.exit(0)
+ 
+	
+	
+	
+if results.MParameter == "distinct"\
+or results.MParameter == "discriminant": 
+	os_command = "python " + \
+		root_dir + \
+		"/MicroPITA.py "+\
+		"--lastmeta " + table_lines[LastMetaInt]+ " " +\
+		"--label " + table_lines[LastMetaInt]+ " " +\
+		"-m " + results.MParameter + " " + \
+		"-n " + results.NSamples + " " +\
+		stratify_string + " " + \
+	results.inputname + " " +\
+	results.outputname
+	#print os_command
+	argsx = shlex.split(os_command)					#Split the command
+	try:
+			subprocess.check_call(argsx , shell=False)
+	except:
+			print "The call to micropita failed============="
+	sys.exit(0)

Binary file galaxy_micropita/pcl_diagram.png has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/readme.md	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,29 @@
+Installation instructions for micropita in a galaxy environment.
+
+micropita is installed in Galaxy using the Admin tool of Galaxy.
+
+In the 
+```
+Admin tool
+```
+select 
+```
+Search and browse tool sheds
+```
+select the tool shed and enter
+```
+micropita
+```
+select
+```
+preview and install
+```
+
+NOTE:  You have to be an administrator in your Galaxy instance to install.
+
+For general reference about micropita refer to: 
+```
+https://bitbucket.org/biobakery/micropita
+```
+
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/README.md	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,106 @@
+# BreadCrumbs #
+
+BreadCrumbs is an unofficial collection of scripts and code intended to consolidate functions for tool development and contain scripts for command line access to commonly used functions. Breadcrumbs tends to include functionality associated with metagenomics analysis but you never know what you will find!
+
+
+## Dependencies: ##
+
+1. Cogent 		https://pypi.python.org/pypi/cogent
+2. MatplotLib 		http://matplotlib.org/downloads.html
+3. Mercurial 		http://mercurial.selenic.com/ (optional for downloading)
+4. Numpy 		http://www.numpy.org/
+5. Python 2.x 		http://www.python.org/download/
+6. SciPy 		http://www.scipy.org/install.html
+7. biom support 	http://biom-format.org/
+
+
+## How to download ##
+
+To download BreadCrumbs from BitBucket use the command:
+
+> hg clone https://bitbucket.org/timothyltickle/breadcrumbs
+
+To update BreadCrumbs, in the BreadCrumbs directory use the 2 commands sequentially:
+
+> hg pull  
+> hg update
+
+
+## Scripts: ##
+
+Scripts are included to expose core functionality through the command line. Currently these scripts center on manipulating and visualizing abundance tables.  
+A quick description of the scripts include:
+
+* *Hclust.py* Flexible script to create a visualization of hierarchical clustering of abundance tables (or other matrices).
+
+* *scriptBiplotTSV.R* Allows one to plot a tsv file as a biplot using nonmetric multidimensional scaling.
+
+* *scriptPlotFeature.py* Allows one to plot a histogram, boxplot, or scatter plot of a bug or metadata in an abundance table. Will work on any row in a matrix.
+
+* *scriptManipulateTable.py* Allows one to perform common functions on an abundance table including, summing, normalizing, filtering, stratifying tables.
+
+* *scriptPcoa.py* Allows one to plot a principle covariance analysis (PCoA) plot of an abundance table.
+
+* *scriptConvertBetweenBIOMAndPCL.py* Allows one to convert between BIOM and PCL file formats.
+
+
+## Programming Classes: ##
+
+Brief descriptions of classes are as follows. More detailed descriptions are given in the classes themselves.
+
+* *AbundanceTable* Data structure to contain and perform operations on an abundance table.
+
+* *BoxPlot* Wrapper to plot box plots.
+
+* *CClade* Helper object used in hierarchical summing and normalization
+
+* *Cladogram* Object that manipulated an early dendrogram visualization. Deprecated, should use the GraPhlan visualization tool on bitbucket instead.
+
+* *CommandLine* Collection of code to work with command line. Deprecated. Should use sfle calls.
+
+* *ConstantsBreadCrumbs* Contains generic constants.
+
+* *ConstantsFiguresBreadCrumbs* Contains constants associated with formatting figures.
+
+* *KMedoids* Code from MLPY which performs KMedoids sample selection.
+
+* *MLPYDistanceAdaptor* Used to allow custom distance matrices to be used by KMedoids.
+
+* *Metric* Difference functions associated with distance and diversity metrics.
+
+* *PCoA* Functionality surrounding the plotting of a PCoA
+
+* *PlotMatrix* Allows on to plot a matrix of numbers.
+
+* *SVM* Support Vector Machine associated scripts.
+
+* *Utility* Generic functions
+
+* *UtilityMath* Generic math related functions
+
+* *ValidateData* Collection of functions to validate data types when needed.
+
+
+## Demo input files: ##
+
+* *fastunifrac_Ley_et_al_NRM_2_sample_id_map.txt* Example Unifrac Id mapping file (source http://bmf2.colorado.edu/fastunifrac/tutorial.psp)
+
+* *GreenGenesCore-May09.ref.tre* Example Greengenes core set reference for Unifrac demo (source http://bmf2.colorado.edu/fastunifrac/tutorial.psp)
+
+* *Test.pcl* Example file / Test PCL file to run scripts on.
+
+* *Test.biom* Example file / Test BIOM file to run scripts on.
+
+* *Test_no_metadata.pcl* Example file / Test PCL file to run scripts on which does not have metadata.
+
+* *Test_no_metadata.biom* Example file / Test BIOM file to run scripts on which does not have metadata.
+
+* *Test-biplot.tsv* Example file / Test file for the scriptBiplotTSV.R
+
+
+## Contributing Authors: ##
+Timothy Tickle, George Weingart, Nicola Segata, Curtis Huttenhower
+
+
+## Contact: ##
+Please feel free to contact ttickle@hsph.harvard.edu with questions.

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/GreenGenesCore-May09.ref.tre	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,2 @@
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4:0.05186,72513:0.04854)Eubacterium_minutum:0.05485):0.00251,78354:0.09813):0.00506):0.01151,(43267:0.03431,43339:0.02984)p-648-a5:0.03065):0.00412,(224206:0.05621,52379:0.02581):0.00585):0.00492,(176276:0.04919,180402:0.08475):0.02528)Mogibacterium:0.00328,14149:0.04659):0.01154,14158:0.04785):0.01407,14148:0.04439):0.01575,((14178:0.05921,86132:0.08475)Eubacteriaceae_bacterium_P4P_50_P4:0.02659,51973:0.04677)Frigovirgula_patagoniensis:0.03577):0.01340,(((((((((((184671:0.03223,185952:0.06955):0.03890,174734:0.03814):0.00611,162021:0.10553):0.00174,199413:0.03647):0.00339,44695:0.02258):0.01065,14228:0.03679):0.00740,135311:0.03095)Clostridium_bifermentans:0.01809,(14188:0.05519,33116:0.03782):0.01009)Peptostreptococcus_anaerobius:0.02389,((14169:0.01939,224798:0.02661)Clostridium_formicaceticum:0.01643,14174:0.04194)Clostridium_felsineum:0.01156):0.00497,105499:0.05280):0.01078,51694:0.04479):0.00988):0.00495,(((((107660:0.03445,156470:0.03635):0.01491,113661:0.10053):0.00758,((14135:0.02263,85424:0.02182):0.00903,218303:0.05058):0.00331):0.00658,103172:0.04687):0.00414,((14132:0.02754,41951:0.02924):0.01902,85846:0.06877):0.00671):0.00571):0.00739,(14245:0.04523,28721:0.04958):0.02424):0.00414,((106726:0.01990,33448:0.04443):0.01577,132641:0.05979):0.02418):0.00995,((((((((70787:0.07674,83665:0.06371):0.00682,87735:0.06306):0.00248,84198:0.10092):0.01020,99351:0.08710):0.00589,((75475:0.04919,83175:0.06616):0.00337,99981:0.07509):0.00084)Peptoniphilus:0.00582,(14275:0.03873,68341:0.04116):0.01663):0.00991,(((((114836:0.05697,38592:0.04190):0.01049,103341:0.04113):0.00906,14263:0.02823):0.00816,((223099:0.05244,81585:0.06134)KSC6-33:0.03044,35017:0.04516):0.00916):0.00572,99474:0.05323):0.00582):0.00741,157172:0.06785):0.00921)Peptostreptococcaceae:0.01803,99126:0.05263):0.00250,((((14240:0.03476,46867:0.04843):0.00501,157142:0.08592)Acetobacterium:0.01511,89746:0.04358):0.01492,((135410:0.07745,145515:0.04754):0.01447,104008:0.04793)Rs-H81:0.02385)Eubacteriaceae:0.01657):0.01910):0.00911,((((((((((((((((((((((146761:0.04573,185374:0.05089):0.00438,176925:0.07222):0.00437,146392:0.04400):0.00261,144247:0.07075):0.00353,(114807:0.05488,150154:0.06299):0.00507):0.00248,((142033:0.08737,169989:0.08778)Rs-H35:0.02868,14181:0.09106)Rs-G08:0.02208):0.01334,111211:0.05650):0.00501,(158015:0.03871,183202:0.07385):0.01517):0.01243,165063:0.09445):0.01612,((((220301:0.05124,224204:0.05032):0.00504,78199:0.07074):0.00669,68349:0.07270):0.00503,(111555:0.05675,148781:0.05464):0.02038):0.00659):0.00580,(110842:0.05004,146376:0.05551):0.00168)Clostridiales_bacterium_JN18_A56_K:0.02146,114843:0.07506)Clostridiales_bacterium_JN18_A89_K*:0.03037,(((((108716:0.03150,110296:0.03067):0.00661,(180895:0.07301,190523:0.08085)B380:0.02724):0.00330,188183:0.02545):0.00656,(((180585:0.04333,191738:0.04153):0.03596,107464:0.03632)LG58:0.01244,110847:0.02665):0.00082):0.00327,111250:0.02906)BCf4-07:0.03363):0.01164,(101433:0.05452,99789:0.03482):0.01174):0.00658,14133:0.03955):0.00328,(17311:0.04191,34789:0.07775):0.01806):0.00246,204369:0.05760)Catabacter:0.00582,(144429:0.04248,178394:0.04580)LQ86:0.02108):0.00578,((((181180:0.03556,192164:0.03986):0.01571,189012:0.18718):0.04486,111274:0.06860)RL302_aal95f07:0.02520,(195106:0.06820,195548:0.05768):0.02318)ELAND_60:0.01172):0.01164,210805:0.05663):0.02176,169056:0.06184):0.00333,((109644:0.04204,16087:0.05497):0.01347,93487:0.09620):0.00849):0.02064)Clostridia:0.01402,(((45167:0.05220,93440:0.06746):0.02634,(62673:0.05242,62675:0.05726):0.01686)Anaerobrancaceae:0.01584,136803:0.06543):0.00084):0.01151,((((((((((((((((((((((((((174092:0.07052,190255:0.06525):0.00631,178986:0.02549):0.06521,164187:0.05843):0.00255,(130403:0.00969,133930:0.06866)Lactobacillus_iners:0.02937):0.00986,((21096:0.00968,88892:0.01373)Lactobacillus_delbrueckii:0.04072,51715:0.01868)Lactobacillus:0.01068):0.00737,((178564:0.05532,192175:0.05820):0.01158,193690:0.01951):0.05646):0.00912,177499:0.03958):0.00822,167772:0.07068):0.00502,(((((((131877:0.05148,187332:0.02037):0.05949,15143:0.02670):0.00166,233451:0.03073):0.00246,21772:0.02121)Lactobacillus_reuteri:0.01966,107485:0.01777)Lactobacillus_fermentum:0.03436,(15152:0.02116,15156:0.00810)Pediococcus:0.01964):0.00413,(((108027:0.01777,15164:0.02829):0.00165,58041:0.04355):0.00164,(15179:0.03554,26176:0.04619):0.01088):0.00573):0.00574):0.00327,(((96727:0.03877,97946:0.02097):0.00827,111332:0.02346):0.00408,130744:0.03640):0.01885):0.00904,(107854:0.04694,15221:0.01131)Lactobacillus_casei:0.02150)Lactobacillaceae:0.01712,((((((((((146681:0.10535,14917:0.06866):0.00869,178759:0.06864):0.01089,(110120:0.05740,15693:0.06853):0.01189)NB1-n:0.01741,(153403:0.05008,155994:0.04834):0.01587):0.00500,(134601:0.05893,182108:0.03407):0.01666)Turicibacter_sanguinis:0.01741,50458:0.06159)Turicibacter:0.02012,((15061:0.02984,48276:0.03145):0.01159,28361:0.04176):0.00494):0.00247,48237:0.06063):0.00334,((114822:0.03871,15241:0.04293):0.01498,55895:0.05161):0.02155)Aerococcaceae:0.02060,(((140940:0.04608,151398:0.04450):0.00336,(172032:0.08646,172558:0.01714)aab18f10:0.08576):0.00664,16452:0.02751)Carnobacteriaceae:0.00163):0.00328):0.01148,(((107061:0.06058,136001:0.07411):0.00264,(170087:0.12267,62895:0.01132):0.00258):0.00572,164676:0.07205):0.00251):0.00164,((((((((((((185556:0.03401,196718:0.03144):0.02002,190336:0.06463):0.04930,(205757:0.00242,221080:0.03405):0.00000):0.00165,100119:0.02504):0.00082,55342:0.02777):0.00491,50448:0.01212)Streptococcus:0.01218,15483:0.05063):0.02577,108351:0.02264)Streptococcaceae:0.01319,78015:0.05177):0.00499,110020:0.06031):0.02163,((((15110:0.02661,67688:0.02683):0.01816,67686:0.01545):0.00977,(216931:0.06396,76923:0.01696)Weissella:0.01585):0.01559,134006:0.10280)Leuconostoc:0.02214):0.02667,81912:0.04839):0.00919)Lactobacillales:0.02697,53770:0.02991):0.00994,(((((((142023:0.01278,165813:0.10801):0.00700,146872:0.00809):0.00489,217912:0.02429):0.00164,14894:0.00890):0.01138,(165963:0.04456,173296:0.08860):0.00173):0.02287,64393:0.01618):0.00247,(145617:0.04773,28012:0.03236):0.02598)Staphylococcaceae:0.02625):0.00329,((((((((((14990:0.02668,156286:0.05209):0.00668,147938:0.06066):0.00000,141968:0.02393):0.00898,203909:0.05963):0.00414,48079:0.03756)Planococcaceae:0.02144,(218836:0.04195,224949:0.04034):0.01399):0.00740,(104534:0.08808,216781:0.04021):0.01241):0.01233,(((((14540:0.06446,83619:0.07674):0.01620,151888:0.03630)Geobacillus_debilis:0.02483,58677:0.01855):0.02548,58675:0.03163):0.00742,14575:0.02184):0.00899):0.00245,(((129409:0.07486,211845:0.06089):0.00427,175339:0.06494):0.01507,(173100:0.08799,40011:0.05551)Bacillus_cereus:0.02957):0.00332):0.00327,(((((191576:0.03747,38338:0.06088):0.00762,128618:0.00081):0.01299,14754:0.00405):0.00244,165114:0.09306):0.01267,190564:0.07201)Bacillus:0.01922):0.00984):0.00245,(((137867:0.04693,139299:0.01537)Sporolactobacillus:0.07146,54543:0.02344):0.01084,(206482:0.02689,36300:0.04372)Marinococcus:0.05558):0.01573):0.00164,(((14604:0.01774,14611:0.03079):0.00000,106243:0.02021):0.00570,128093:0.04298)Halobacillus:0.00578):0.00326,15026:0.04839):0.00414,((((112113:0.01535,26206:0.02742):0.01148,22975:0.04040):0.00248,144022:0.03231)Thermoactinomycetaceae:0.02383,14468:0.04927):0.00915):0.00246,((((((((((132693:0.02423,187176:0.02549):0.01154,39053:0.06490):0.00334,(104054:0.01856,163693:0.07342):0.00588):0.00245,(((14405:0.06835,51767:0.02989):0.01762,110470:0.11516):0.00511,91160:0.03808):0.00412):0.00814,96700:0.01776):0.00328,141630:0.04710):0.00165,80984:0.01614):0.01383,(167450:0.08174,210271:0.05013):0.00171):0.00821,137955:0.06710):0.01672,((209244:0.08319,67396:0.06036):0.00345,103446:0.01292)Brevibacillus_brevis:0.05032)Paenibacillaceae:0.00831):0.00820,(109064:0.05908,182726:0.06818):0.02380)Bacilli:0.00828,(((((157680:0.02636,167279:0.04113):0.00249,212471:0.05973)Bacillales:0.04476,14331:0.05897):0.00505,107003:0.03745):0.00659,14348:0.05259)Alicyclobacillus:0.03231):0.00249,(((((((((((((((((114509:0.04443,23473:0.06202):0.02025,(114581:0.06726,43971:0.05782):0.00766)BCf4-19:0.03071,((32823:0.08844,77895:0.03554):0.00768,78268:0.05959):0.00249)BCf7-05:0.04661,15854:0.06063):0.00000,(145549:0.06306,15811:0.05331):0.01954):0.00498,15832:0.05174):0.02155,(((106362:0.07404,15860:0.07820)Mycoplasma_haemocanis:0.03614,15859:0.12355):0.07434,15837:0.07416)Mycoplasma_haemofelis:0.03190)Mycoplasma_haemomuris:0.07254,((102371:0.07934,160113:0.10434):0.04939,(214741:0.10008,219640:0.08536)Elev_16S_1754:0.09807):0.01214):0.00171,((((((103186:0.04766,220788:0.07345)Mycoplasma_ovipneumoniae:0.03152,70042:0.03551):0.02070,(137538:0.05811,58394:0.05565):0.00254):0.00494,((15899:0.06618,79153:0.05950):0.01194,23718:0.03629):0.01399):0.00495,36302:0.06250):0.00249,(108671:0.05726,155625:0.07748):0.01534)Mycoplasma:0.06595)Mycoplasmatales:0.00596,((((150616:0.08814,163407:0.04194):0.05544,101955:0.04194):0.01755,206850:0.06295):0.00669,29463:0.06371)Spiroplasma_citri:0.01501):0.02505,((((((104358:0.06761,210102:0.06093):0.01582,42538:0.07041)adhufec202:0.02750,42395:0.05105):0.00693,((191556:0.08588,52403:0.04268):0.00932,182398:0.06327):0.00666):0.00248,(232434:0.04424,50779:0.04403)p-3870-23G5:0.01467):0.01075,15698:0.08221)RF39:0.04804):0.00677,(((((((55569:0.04681,62523:0.03468)Candidatus_Phytoplasma:0.02501,15696:0.08198):0.00422,166536:0.07168):0.00755,(15695:0.04518,30323:0.05165):0.00588):0.02546,(15708:0.07512,179979:0.05259):0.01360):0.00579,15703:0.07314):0.01758,(15704:0.08602,62666:0.08071):0.01901)Acholeplasmatales:0.00837):0.00416,((((((((((185382:0.10415,193776:0.04835):0.03098,174166:0.02969):0.00261,190225:0.10348):0.00625,50433:0.01306):0.00244,(((176560:0.04153,184230:0.01610):0.01043,(182229:0.04657,199714:0.06037):0.00798):0.00686,175094:0.08149):0.02124)Eubacterium_biforme:0.02469,((((((130091:0.05206,230759:0.05911):0.00254,98789:0.04504):0.00826,132422:0.05345):0.00749,((188898:0.04770,197123:0.05424):0.01675,115098:0.02183)M1_h03_1:0.04025)M3_e09_3:0.02907,49369:0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+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test-Biplot.tsv	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,9 @@
+TID	Continuous	STSite	Bacteria|Firmicutes|Clostridia|Clostridiales|Clostridiaceae|Clostridium|72	Bacteria|unclassified|4904	Bacteria|Firmicutes|Bacilli|Lactobacillales|Lactobacillaceae|Lactobacillus|1361	Bacteria|3417	Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368
+700098986	1	L_Antecubital_fossa	1	0	3	0	5
+700098984	2	R_Retroauricular_crease	0	10	0	45	0
+700098980	4	Subgingival_plaque	12	43	29	34	2
+700098988	5	R_Antecubital_fossa	0	6	0	3	0
+700037470	6	L_Retroauricular_crease	6	0	45	0	6
+700037472	7	R_Retroauricular_crease	0	23	0	0	0
+700037474	8	L_Antecubital_fossa	2	0	1	0	1
+700037476	9	R_Antecubital_fossa	1	1	1	1	1

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test-BiplotNA.tsv	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,9 @@
+TID	Continuous	STSite	Bacteria|Firmicutes|Clostridia|Clostridiales|Clostridiaceae|Clostridium|72	Bacteria|unclassified|4904	Bacteria|Firmicutes|Bacilli|Lactobacillales|Lactobacillaceae|Lactobacillus|1361	Bacteria|3417	Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368
+700098986	1	L_Antecubital_fossa	1	0	3	0	5
+700098984	2	NA	0	10	0	45	0
+700098980	4	Subgingival_plaque	12	43	29	34	2
+700098988	5	R_Antecubital_fossa	0	6	0	3	0
+700037470	6	L_Retroauricular_crease	6	0	45	0	6
+700037472	7	R_Retroauricular_crease	0	23	0	0	0
+700037474	8	NA	2	0	1	0	1
+700037476	9	R_Antecubital_fossa	1	1	1	1	1

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test-comma.biom	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,1 @@
+{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BreadCrumbs","date": "2013-09-08T17:12:24.307906","matrix_type": "sparse","matrix_element_type": "float","shape": [5, 8],"data": [[0,0,1.0],[0,2,12.0],[0,4,6.0],[0,6,2.0],[0,7,1.0],[1,1,10.0],[1,2,43.0],[1,3,6.0],[1,5,23.0],[1,7,1.0],[2,0,3.0],[2,2,29.0],[2,4,45.0],[2,6,1.0],[2,7,1.0],[3,1,45.0],[3,2,34.0],[3,3,3.0],[3,7,1.0],[4,0,5.0],[4,2,2.0],[4,4,6.0],[4,6,1.0],[4,7,1.0]],"rows": [{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368", "metadata": null}],"columns": [{"id": "700098986", "metadata": {"TID": "700098986", "STSite": "L_Antecubital_fossa", "ID": "700098986"}},{"id": "700098984", "metadata": {"TID": "700098984", "STSite": "R_Retroauricular_crease", "ID": "700098984"}},{"id": "700098980", "metadata": {"TID": "700098980", "STSite": "Subgingival_plaque", "ID": "700098980"}},{"id": "700098988", "metadata": {"TID": "700098988", "STSite": "R_Antecubital_fossa", "ID": "700098988"}},{"id": "700037470", "metadata": {"TID": "700037470", "STSite": "L_Retroauricular_crease", "ID": "700037470"}},{"id": "700037472", "metadata": {"TID": "700037472", "STSite": "R_Retroauricular_crease", "ID": "700037472"}},{"id": "700037474", "metadata": {"TID": "700037474", "STSite": "L_Antecubital_fossa", "ID": "700037474"}},{"id": "700037476", "metadata": {"TID": "700037476", "STSite": "R_Antecubital_fossa", "ID": "700037476"}}]}
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test-comma.pcl	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,8 @@
+ID,700098986,700098984,700098980,700098988,700037470,700037472,700037474,700037476
+TID,700098986,700098984,700098980,700098988,700037470,700037472,700037474,700037476
+STSite,L_Antecubital_fossa,R_Retroauricular_crease,Subgingival_plaque,R_Antecubital_fossa,L_Retroauricular_crease,R_Retroauricular_crease,L_Antecubital_fossa,R_Antecubital_fossa
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72,1.0,0.0,12.0,0.0,6.0,0.0,2.0,1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904,0.0,10.0,43.0,6.0,0.0,23.0,0.0,1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361,3.0,0.0,29.0,0.0,45.0,0.0,1.0,1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417,0.0,45.0,34.0,3.0,0.0,0.0,0.0,1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368,5.0,0.0,2.0,0.0,6.0,0.0,1.0,1.0

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test.biom	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,1 @@
+{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BreadCrumbs","date": "2013-09-04T12:40:01.356447","matrix_type": "sparse","matrix_element_type": "float","shape": [5, 8],"data": [[0,0,1.2300000190734863],[0,2,12.0],[0,4,6.0],[0,6,2.0],[0,7,1.0],[1,1,10.0],[1,2,43.0],[1,3,6.0],[1,5,23.0],[1,7,1.0],[2,0,3.0],[2,2,29.0],[2,4,45.0],[2,6,1.0],[2,7,1.0],[3,1,45.0],[3,2,34.0],[3,3,3.0],[3,7,1.0],[4,0,5.0],[4,2,2.0],[4,4,6.0],[4,6,1.0],[4,7,1.0]],"rows": [{"id": "72", "metadata": {"taxonomy": ["Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Bacillaceae", "unclassified"]}},{"id": "4904", "metadata": {"taxonomy": ["Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Bacillaceae", "unclassified"]}},{"id": "1361", "metadata": {"taxonomy": ["Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Bacillaceae", "unclassified"]}},{"id": "3417", "metadata": {"taxonomy": ["Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Bacillaceae", "unclassified"]}},{"id": "1368", "metadata": {"taxonomy": ["Bacteria", "Firmicutes", "Bacilli", "Bacillales", "Bacillaceae", "unclassified"]}}],"columns": [{"id": "700098986", "metadata": {"TID": "700098986", "STSite": "L_Antecubital_fossa", "ID": "700098986"}},{"id": "700098984", "metadata": {"TID": "700098984", "STSite": "R_Retroauricular_crease", "ID": "700098984"}},{"id": "700098980", "metadata": {"TID": "700098980", "STSite": "Subgingival_plaque", "ID": "700098980"}},{"id": "700098988", "metadata": {"TID": "700098988", "STSite": "R_Antecubital_fossa", "ID": "700098988"}},{"id": "700037470", "metadata": {"TID": "700037470", "STSite": "L_Retroauricular_crease", "ID": "700037470"}},{"id": "700037472", "metadata": {"TID": "700037472", "STSite": "R_Retroauricular_crease", "ID": "700037472"}},{"id": "700037474", "metadata": {"TID": "700037474", "STSite": "L_Antecubital_fossa", "ID": "700037474"}},{"id": "700037476", "metadata": {"TID": "700037476", "STSite": "R_Antecubital_fossa", "ID": "700037476"}}]}
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test.pcl	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,8 @@
+ID	700098986	700098984	700098980	700098988	700037470	700037472	700037474	700037476
+TID	700098986	700098984	700098980	700098988	700037470	700037472	700037474	700037476
+STSite	L_Antecubital_fossa	R_Retroauricular_crease	Subgingival_plaque	R_Antecubital_fossa	L_Retroauricular_crease	R_Retroauricular_crease	L_Antecubital_fossa	R_Antecubital_fossa
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72	1.23	0.0	12.0	0.0	6.0	0.0	2.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904	0.0	10.0	43.0	6.0	0.0	23.0	0.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361	3.0	0.0	29.0	0.0	45.0	0.0	1.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417	0.0	45.0	34.0	3.0	0.0	0.0	0.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368	5.0	0.0	2.0	0.0	6.0	0.0	1.0	1.0

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test.tsv	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,9 @@
+TID	STSite	Bacteria|Firmicutes|Clostridia|Clostridiales|Clostridiaceae|Clostridium|72	Bacteria|unclassified|4904	Bacteria|Firmicutes|Bacilli|Lactobacillales|Lactobacillaceae|Lactobacillus|1361	Bacteria|3417	Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368
+700098986	L_Antecubital_fossa	1	0	3	0	5
+700098984	R_Retroauricular_crease	0	10	0	45	0
+700098980	Subgingival_plaque	12	43	29	34	2
+700098988	R_Antecubital_fossa	0	6	0	3	0
+700037470	L_Retroauricular_crease	6	0	45	0	6
+700037472	R_Retroauricular_crease	0	23	0	0	0
+700037474	L_Antecubital_fossa	2	0	1	0	1
+700037476	R_Antecubital_fossa	1	1	1	1	1

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/TestForConversion.biom	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,1 @@
+{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BreadCrumbs","date": "2013-09-17T04:25:03.670076","matrix_type": "sparse","matrix_element_type": "float","shape": [5, 8],"data": [[0,0,1.2300000190734863],[0,2,12.0],[0,4,6.0],[0,6,2.0],[0,7,1.0],[1,1,10.0],[1,2,43.0],[1,3,6.0],[1,5,23.0],[1,7,1.0],[2,0,3.0],[2,2,29.0],[2,4,45.0],[2,6,1.0],[2,7,1.0],[3,1,45.0],[3,2,34.0],[3,3,3.0],[3,7,1.0],[4,0,5.0],[4,2,2.0],[4,4,6.0],[4,6,1.0],[4,7,1.0]],"rows": [{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368", "metadata": null}],"columns": [{"id": "700098986", "metadata": {"TID": "700098986", "STSite": "L_Antecubital_fossa", "ID": "700098986"}},{"id": "700098984", "metadata": {"TID": "700098984", "STSite": "R_Retroauricular_crease", "ID": "700098984"}},{"id": "700098980", "metadata": {"TID": "700098980", "STSite": "Subgingival_plaque", "ID": "700098980"}},{"id": "700098988", "metadata": {"TID": "700098988", "STSite": "R_Antecubital_fossa", "ID": "700098988"}},{"id": "700037470", "metadata": {"TID": "700037470", "STSite": "L_Retroauricular_crease", "ID": "700037470"}},{"id": "700037472", "metadata": {"TID": "700037472", "STSite": "R_Retroauricular_crease", "ID": "700037472"}},{"id": "700037474", "metadata": {"TID": "700037474", "STSite": "L_Antecubital_fossa", "ID": "700037474"}},{"id": "700037476", "metadata": {"TID": "700037476", "STSite": "R_Antecubital_fossa", "ID": "700037476"}}]}
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test_no_metadata.biom	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,1 @@
+{"id": "None","format": "Biological Observation Matrix 1.0.0","format_url": "http://biom-format.org","type": "OTU table","generated_by": "BreadCrumbs","date": "2013-09-08T21:45:51.197864","matrix_type": "sparse","matrix_element_type": "float","shape": [5, 8],"data": [[0,0,1.2300000190734863],[0,2,12.0],[0,4,6.0],[0,6,2.0],[0,7,1.0],[1,1,10.0],[1,2,43.0],[1,3,6.0],[1,5,23.0],[1,7,1.0],[2,0,3.0],[2,2,29.0],[2,4,45.0],[2,6,1.0],[2,7,1.0],[3,1,45.0],[3,2,34.0],[3,3,3.0],[3,7,1.0],[4,0,5.0],[4,2,2.0],[4,4,6.0],[4,6,1.0],[4,7,1.0]],"rows": [{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417", "metadata": null},{"id": "Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368", "metadata": null}],"columns": [{"id": "700098986", "metadata": {"ID": "700098986"}},{"id": "700098984", "metadata": {"ID": "700098984"}},{"id": "700098980", "metadata": {"ID": "700098980"}},{"id": "700098988", "metadata": {"ID": "700098988"}},{"id": "700037470", "metadata": {"ID": "700037470"}},{"id": "700037472", "metadata": {"ID": "700037472"}},{"id": "700037474", "metadata": {"ID": "700037474"}},{"id": "700037476", "metadata": {"ID": "700037476"}}]}
\ No newline at end of file

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/Test_no_metadata.pcl	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,6 @@
+ID	700098986	700098984	700098980	700098988	700037470	700037472	700037474	700037476
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|72	1.23	0.0	12.0	0.0	6.0	0.0	2.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|4904	0.0	10.0	43.0	6.0	0.0	23.0	0.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1361	3.0	0.0	29.0	0.0	45.0	0.0	1.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|3417	0.0	45.0	34.0	3.0	0.0	0.0	0.0	1.0
+Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|unclassified|1368	5.0	0.0	2.0	0.0	6.0	0.0	1.0	1.0

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/fastunifrac_Ley_et_al_NRM_2_sample_id_map-colors.txt	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,8 @@
+Group1:Nse#L#150,Nse#L#151,Nse#L#152,Nse#L#153,Nse#L#98
+Group2:Nso#106,Nso#107,Nso#108,Nso#1209,Nso#65
+Group3:Nw#L#119,Nw#L#160,Nw#L#5,Nw#R#189,Nw#R#50
+Group4:Sse#1224,Sse#M#14,Sse#M#169,Sse#M#62,Sse#M#63,Sse#M#64,Sse#M#75
+Group5:Swb#M#137,Swb#M#154,Swb#M#155,Swb#M#156,Swb#M#157
+Group6:Sws#M#1227,Sws#M#1230,Sws#M#1234,Sws#M#163,Sws#M#83
+Group7:Tg#1238,Tg#1249,Tg#1251,Tg#1252
+Group8:Vg#h#1038,Vg#h#1039,Vg#h#1043,Vg#h#1061,Vg#h#1104,Vg#o#1124,Vg#o#1128,Vg#o#1132,Vg#o#1153,Vg#o#1160,Vg#oh#1051,Vg#oh#1055,Vg#oh#1070

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/galaxy_micropita/src/breadcrumbs/demo_input/fastunifrac_Ley_et_al_NRM_2_sample_id_map.txt	Thu Jun 03 17:07:36 2021 +0000
@@ -0,0 +1,4844 @@
+150394	Tg#1249	1
+150394	Tg#1251	2
+215260	Nso#65	1
+215260	Nso#1209	4
+16073	Vg#h#1061	1
+16072	Vg#h#1104	1
+16076	Vg#o#1128	2
+16076	Vg#oh#1070	84
+16076	Vg#oh#1055	1
+16076	Vg#o#1132	2
+16076	Tg#1249	2
+16076	Vg#o#1153	2
+35238	Nse#L#151	1
+35238	Nw#R#50	2
+35238	Nso#106	1
+16074	Vg#h#1043	2
+16074	Vg#oh#1070	1
+16074	Vg#oh#1051	1
+16074	Vg#h#1038	6
+16074	Vg#o#1132	2
+101767	Nw#L#5	1
+101767	Nso#108	1
+114078	Nso#65	1
+131443	Vg#h#1039	1
+144338	Tg#1249	1
+111532	Nso#108	1
+111532	Nso#107	7
+169721	Vg#oh#1055	26
+169721	Vg#oh#1051	33
+103892	Sse#M#62	1
+214891	Nso#65	1
+214891	Nso#1209	1
+170442	Sse#M#62	1
+58397	Tg#1252	1
+58397	Nse#L#150	1
+82191	Tg#1238	2
+93780	Nw#L#5	1
+61204	Swb#M#156	42
+61204	Swb#M#157	118
+61204	Swb#M#154	10
+61204	Swb#M#155	2
+61204	Sws#M#1227	10
+61204	Sws#M#163	276
+61204	Nso#108	1
+61204	Sws#M#83	44
+61204	Sws#M#1234	15
+61204	Swb#M#137	2
+61204	Sws#M#1230	27
+130882	Nso#65	1
+130882	Nw#L#5	1
+130882	Nw#L#119	1
+130882	Sse#M#169	1
+130882	Nso#107	1
+130882	Nw#R#50	1
+6155	Nw#R#50	1
+6155	Nso#108	1
+6155	Nso#106	2
+167734	Vg#o#1132	2
+7624	Sws#M#1234	1
+7624	Sse#M#62	1
+7624	Sse#M#63	1
+7624	Sse#M#75	1
+7624	Sws#M#83	1
+182229	Vg#o#1153	1
+131207	Sse#M#169	1
+131207	Sse#M#63	1
+131207	Swb#M#156	2
+112460	Nse#L#151	1
+112460	Sse#M#64	1
+192175	Vg#o#1124	3
+57724	Swb#M#154	8
+57724	Sws#M#163	1
+57724	Nso#108	2
+57724	Nso#106	1
+166488	Nse#L#151	1
+155255	Nso#1209	1
+55589	Nso#65	1
+220301	Vg#o#1124	2
+97151	Vg#h#1039	4
+97151	Vg#h#1104	8
+97151	Vg#h#1043	1
+166485	Sse#M#62	1
+154652	Sse#M#62	1
+154652	Sse#M#63	2
+60136	Vg#oh#1055	2
+6294	Swb#M#156	1
+6294	Swb#M#155	1
+6294	Nse#L#98	1
+6294	Nw#R#50	1
+6294	Sse#M#75	1
+6294	Nso#108	2
+6294	Sse#M#64	1
+191465	Nse#L#98	1
+191465	Nw#L#160	1
+191465	Nso#106	1
+152550	Sws#M#1230	26
+152550	Sws#M#1227	56
+152550	Sws#M#1234	6
+152550	Sse#M#64	2
+152550	Sws#M#83	1
+106352	Swb#M#154	1
+106352	Sws#M#163	2
+106352	Sse#M#62	1
+106352	Sse#M#75	1
+152557	Sws#M#83	5
+105395	Nw#L#5	1
+105395	Nw#L#160	2
+105395	Nw#L#119	5
+105395	Nw#R#189	2
+105004	Nso#1209	3
+105004	Nso#107	1
+105004	Nw#R#50	1
+81674	Vg#oh#1070	127
+81674	Vg#oh#1055	10
+81674	Vg#oh#1051	137
+81674	Vg#o#1132	3
+140328	Nso#1209	1
+101380	Nw#L#119	1
+101380	Nso#107	1
+227238	Nso#65	1
+227238	Nse#L#150	1
+227238	Nso#106	1
+199059	Vg#o#1124	5
+43051	Vg#h#1043	1
+43051	Vg#o#1124	4
+43051	Vg#o#1128	1
+43051	Vg#oh#1070	1
+43051	Vg#o#1132	1
+43051	Vg#o#1153	1
+161296	Sse#M#63	1
+101018	Nse#L#98	1
+159156	Sse#M#75	1
+200413	Sse#M#62	5
+200413	Sse#M#63	2
+10709	Sse#M#169	1
+10709	Nso#108	2
+2446	Sse#M#14	1
+2446	Sws#M#1227	1
+2446	Sws#M#83	4
+2446	Nw#R#189	1
+2446	Swb#M#137	1
+2446	Sws#M#1230	12
+2446	Sse#M#62	1
+2446	Sse#M#64	4
+99206	Nse#L#150	1
+95791	Tg#1238	2
+95791	Tg#1249	1
+128299	Nse#L#151	1
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