Mercurial > repos > kaymccoy > aggregate_fitnesses
view aggregate.py @ 10:fa51e5de0cb3 draft
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| author | kaymccoy |
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| date | Fri, 12 Aug 2016 22:31:00 -0400 |
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# A translation of aggregate.pl into python! For analysis of Tn-Seq. # This script requires BioPython just like calc_fitness.py, so you need it installed along with its dependencies if you want to run these scripts on your own. # How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html # K. McCoy ##### ARGUMENTS ##### def print_usage(): print "Aggregate.py's usage is as follows:" + "\n\n" print "\033[1m" + "Required" + "\033[0m" + "\n" print "-o" + "\t\t" + "Output file for aggregated data." + "\n" print "\n" print "\033[1m" + "Optional" + "\033[0m" + "\n" print "-c" + "\t\t" + "Check for missing genes in the data set - provide a reference genome in genbank format. Missing genes will be sent to stdout." + "\n" print "-m" + "\t\t" + "Place a mark in an extra column for this set of genes. Provide a file with a list of genes seperated by newlines." + "\n" print "-x" + "\t\t" + "Cutoff: Don't include fitness scores with average counts (c1+c2)/2 < x (default: 0)" + "\n" print "-b" + "\t\t" + "Blanks: Exclude -b % of blank fitness scores (scores where c2 = 0) (default: 0 = 0%)" + "\n" print "-f" + "\t\t" + "An in-between file carrying information on the blank count found from calc_fitness or consol_fitness; one of two ways to pass a blank count to this script" + "\n" print "-w" + "\t\t" + "Use weighted algorithm to calculate averages, variance, sd, se" + "\n" print "-l" + "\t\t" + "Weight ceiling: maximum value to use as a weight (default: 999,999)" + "\n" print "\n" print "All remainder arguements will be treated as fitness files (those files created by calc_fitness.py)" + "\n" print "\n" import argparse parser = argparse.ArgumentParser() parser.add_argument("-o", action="store", dest="summary") parser.add_argument("-c", action="store", dest="find_missing") parser.add_argument("-m", action="store", dest="marked") parser.add_argument("-x", action="store", dest="cutoff") parser.add_argument("-b", action="store", dest="blank_pc") parser.add_argument("-f", action="store", dest="blank_file") parser.add_argument("-w", action="store", dest="weighted") parser.add_argument("-l", action="store", dest="weight_ceiling") parser.add_argument("fitnessfiles", nargs=argparse.REMAINDER) arguments = parser.parse_args() if not arguments.summary: print "\n" + "You are missing a value for the -o flag. " print_usage() quit() if not arguments.fitnessfiles: print "\n" + "You are missing fitness file(s); these should be entered immediately after all the flags. " print_usage() quit() # 999,999 is a trivial placeholder number if (not arguments.weight_ceiling): arguments.max_weight = 999999 # Cutoff exists to discard positions with a low number of counted transcripts, because their fitness may not be as accurate - for the same reasoning that studies with low sample sizes can be innacurate. if (not arguments.cutoff): arguments.cutoff = 0 # Gets information from the txt output file of calc_fit / consol, if inputted if arguments.blank_file: with open(arguments.blank_file) as file: blank_pc = file.read().splitlines() arguments.blank_pc = float(blank_pc[0].split()[1]) if (not arguments.blank_pc): arguments.blank_pc = 0 ##### SUBROUTINES ##### # A subroutine that calculates the average, variance, standard deviation (sd), and standard error (se) of a group of scores; for use when aggregating scores by gene later on import math def average(scores): sum = 0 num = 0 for i in scores: sum += i num += 1 average = sum/num xminusxbars = 0 for i in scores: xminusxbars += (i - average)**2 variance = xminusxbars/(num-1) sd = math.sqrt(variance) se = sd / math.sqrt(num) return (average, variance, sd, se) # A subroutine that calculates the weighted average, variance, standard deviation (sd), and standard error (se) of a group of scores; the weights come from the number of reads each insertion location has # For use when aggregating scores by gene later on, if the weighted argument is called def weighted_average(scores,weights): sum = 0 weighted_average = 0 weighted_variance = 0 top = 0 bottom = 0 i = 0 while i < len(weights): if not scores[i]: scores[i] = 0.0 top += float(weights[i])*float(scores[i]) bottom += float(weights[i]) i += 1 if bottom == 0: return 0 weighted_average = top/bottom top = 0 bottom = 0 i = 0 while i < len(weights): top += float(weights[i]) * (float(scores[i]) - weighted_average)**2 bottom += float(weights[i]) i += 1 weighted_variance = top/bottom weighted_stdev = math.sqrt(weighted_variance) weighted_stder = weighted_stdev/math.sqrt(len(scores)) return (weighted_average, weighted_variance, weighted_stdev, weighted_stder) ##### AGGREGATION / CALCULATIONS ##### #Reads the genes which should be marked in the final aggregate file into an array import os.path if arguments.marked: with open(arguments.marked) as file: marked_set = file.read().splitlines() #Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values #The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2] import csv gene_summary = {} for eachfile in arguments.fitnessfiles: with open(eachfile) as csvfile: lines = csv.reader(csvfile) for line in lines: locus = line[9] w = line[12] if w == 'nW': continue if not w: w == 0 c1 = float(line[2]) c2 = float(line[3]) avg = (c1+c2)/2 if avg < float(arguments.cutoff): continue if avg > float(arguments.weight_ceiling): avg = arguments.weight_ceiling if locus not in gene_summary: gene_summary[locus] = {"w" : [], "s": []} gene_summary[locus]["w"].append(w) gene_summary[locus]["s"].append(avg) #If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file from Bio import SeqIO if (arguments.find_missing): output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]] handle = open(arguments.find_missing, "rU") for record in SeqIO.parse(handle, "genbank"): refname = record.id features = record.features handle.close() #Goes through the features to find which are genes for feature in features: gene = "" if feature.type == "gene": locus = "".join(feature.qualifiers["locus_tag"]) if "gene" in feature.qualifiers: gene = "".join(feature.qualifiers["gene"]) else: continue #Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights #!!! issue = locus["w"] returning str not float for some reason! sum = 0 num = 0 avgsum = 0 blank_ws = 0 i = 0 if locus in gene_summary.keys(): for w in gene_summary[locus]["w"]: if float(w) == 0: blank_ws += 1 else: sum += float(w) num += 1 count = num + blank_ws removed = 0 to_remove = int(float(arguments.blank_pc)*count) if blank_ws > 0: i = 0 while i < len(gene_summary[locus]["w"]): w = gene_summary[locus]["w"][i] if removed == to_remove: break if float(w) == 0: del gene_summary[locus]["w"][i] del gene_summary[locus]["s"][i] removed += 1 i -= 1 i += 1 #If all the fitness values within a gene are empty, sets mean/var to 0.10 and Xs out sd/se; marks the gene if that's requested if num == 0: if (arguments.marked and locus in marked_set): output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"]) else: output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"]) #Otherwise calls average() or weighted_average() to find the aggregate w / count / standard deviation / standard error of the insertions within each gene; marks the gene if that's requested else: if not arguments.weighted: (average, variance, stdev, stderr) = average(gene_summary[locus]["w"]) else: (average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"]) if (arguments.marked and locus in marked_set): output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"]) else: output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"]) #If a gene doesn't have any insertions, sets mean/var to 0.10 and Xs out sd/se, plus leaves count through removed blank because there were no reads. else: if (arguments.marked and locus in marked_set): output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"]) else: output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"]) #Writes the aggregated fitness file with open(arguments.summary, "wb") as csvfile: writer = csv.writer(csvfile) writer.writerows(output) #If finding missing genes is not requested, just finds the aggregate w / count / standard deviation / standard error of the insertions within each gene, and writes them to a file, plus marks the genes requested #This is never called through Galaxy since finding missing genes is just better than not finding them. else: output = [["Locus","W","Count","SD","SE","M\n"]] for gene in gene_summary.keys(): sum = 0 num = 0 average = 0 if "w" not in gene_summary[gene]: continue for i in gene_summary[gene]["w"]: sum += i num += 1 average = sum/num xminusxbars = 0 for i in w: xminusxbars += (i-average)**2 if num > 1: sd = math.sqrt(xminusxbars/(num-1)) se = sd / math.sqrt(num) if (arguments.marked and locus in marked_set): output.append([gene, average, num, sd, se, "M", "\n"]) else: output.append([gene, average, num, sd, se, "\n"]) with open(arguments.summary, "wb") as csvfile: writer = 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MN~ +NM: # IMM8~ =MD, ?N= :,, ,+77?=+, $8MM7::OMMMMMMZ+ I+, ?NI , :MMM8$ NNI, 7M$ # ,MMN $MD 7M+ ~ODNZ~, :7MMN? , $MMMN= 7MNMN: +8+ ,D8~ =MMDD8Z= =NMD OM7, # OMMD~ IMN: =8O:,~+$OO? :IZ+: :ZMMNM8= =DMMI ~8MM8= , ,, :8M?,$MM, ::, DM, =Z~ ,I8DDDN8$DMN~ MN ,=Z8DNDZZ= MD: , # ,,,,,,,, ?8NNMM8$I????++=+8MMMD$77$ZDMMNMMMDNOZ~ :IZ8DOI~, =MMMNMNM8I:, ,7M8I +MND7 ~MNDDM8~ 7MI MM7 :8MDDM7 8N= ~ID$, ,:Z$?:,,,=ONNMNMD= ?M$ :: +MI ,,,,,,::~~:::,,,,,,, ,+OMO:, , # ,,,::~===~~:, ,~+I$88DNNNNNNNNNDNDD8O$?~:, ,7DNMNDMMMN~,ZMND$8NNDZ=, $M? ?NNN7 =MO OMD$MM= MMMINMO $M7 DN: 8MMD: ?D~ :O8ZDMMMMD=, ,ZN$ ,INNMD= :NO ,:+8MMMMMMMMDI:,::,, #=~~~::~~::, ~?78MMMDNMM? :+I$DD87?=, OMNDO$7$OI: 7N+ ?MNMN~ MMMNM$ 7M$ 7M$ +NMMO, ?N~ ~8+ ~7NMMNNOI= ~?ONZ+ ~ZNND,$M8 ~MO $MMMMMMMMMM7:::~~~~~~=+++===~~~:::,, # ,~ONMMNO~, :?8NNMN8?~ZNMMMMMMMMMNMMNDOI=~NM? =MMM+ NMMMO 8M$ ,MM, $MMMN =D, ,?N~ , ?NMMMNNNDDO8DDD887, ,$ND= :O$NMZ IMO ,IMMMMN$, 7NO, ,,,,,:,,, # ,~IDMNN8OI, :~+$DMMMMMMMN87+=~~~~?ZDNNMM, ?NM? :NMN :MM7 ~MN ~DMMMN O~ :DD, ,MMMMNNOI:,, , ,=ZODD8D? :MMM: NMZ +8NM8, ,, :~ ,,~:, # ,INNMMNNO+ :=78NNZIIONMNNDMMN ,7D~ :MN~ MNMM?=MM $? OD, ,IMD ,:8MNMNNNNNNNNMMNM7: :8~ ZND OMN7, :NNMMMMN :8MMMMMNZ=: # :O88NMMN$=~:, ?MMN88NMMMD =MN? =, :NM +7$NO, =N8 , OMI, ,, 7M: ZDDND? MMO ,, +MM8?OMN: ,:: # :$NMMMNMM8I: ,8MD, +MMN= ,=?77=:ZMMM7 ?MN ~$+ 7M7 ,DZ :M~ IMM8: ,8MN, , ,,,, :MMD,=DMM+,?O8= , , # ,=I77$ONNNZ?+ONMZ =$D ,+=::+$8NNMNDNMD7?, ?MN :~~: ~D7 ,NI ,M7 ~?ZNZ?, 8MM~ =ZMMN~$MMMMMMMNMMD$ONMMMMD: # ~DMM= ~I7=, =DMMMD7MMM, :+?=?O$: ~N: ~MN88D$: NMM=, ~MMMMMMMMMMMMOZMMMMMM7::8MMI # , :DNNI :~IDMMND :::=?II?==~::ZN7=+I$ZZZ8DZ+~~: IMMM~ ~MMMNMMMMMI~:7NMMMMD7,: +NM8, # :NMM7, ~MM+ ,,,:~==~~~: , OMMN: ?NNMMMMM8, ~NMNO, =MMMN?,, # ,,,,,,,, ,$8MMO= ~M8 +MNO: ,MM~ , :: ,~, ,,:::,,,, # ,::,,,, ~ONND+ OD+ ?NMD, ?? ,::::::,, # :=+??=:,,,,, ~?$D87I: ~Z? =$DM$: ,::,,,,,,,, # ,:~==~, :+=, $NNNNZ~ ,,:~~: , :DMMMI # ,~~~~~:,,, ,,, ,~IMMMN8O+, ,:~?7$Z7~, :ZDMNNDNM8ZI # ,,~~:,, , :$DNNMMN8?, , ,~7ZOO?: ,:$NMMMM? :7NMMD?, # ,:~~:,,, $NMMMMMNMNO?~ ~?ODDD$=, :?8MMMMMD?~7NMMMD$~ :ONMMN? # ,,,,,,,,, ?$DMMMMMMMMMMD$?=~, ,~7ZZODN87????I$ODNMDOZ$7I: ~$ZDMD7==~ =$ZDND$++~, # ~?++=~~, ~+?II7ZNMMMMMM8$$$?~, ,~?II7Z8DDOZ$77II+: ~?IZDM8O$I~, :??$8MMNZZ7+, # , , ,+D7 :=IZ8NMMMNNMNNO$= ,:?ZDNNMMMNMND8Z7=, =ZNMMMMMD?,, :ONMNMMMN?, # ,MMMM7 ,,::~IONNMMNNDDD8OZI=, ,::::=+I$ONMNNNNDDDNNMMMMMMMMMD87: ,:~=ZNNNDOI, ~7$ZO8DDNNNNDD8O+:, #~, +MMMMDI?~ ,~+Z8DNDNNMMMMDD$+:,,, , ,~?7O8DNNNNNNMMMMMNOOZI??++?IZ88$: ,,~ZDMMMMMMMMMMMMMMMMMMNNNNNMMMNND8$=,, # ,~: NMMMMOZMM8: ,7DZ~ ,,~IONMMMMMMMMMNDZ+~: , ,=I8DNMMMMMNMNMMMMMDNMMMMNMMMNNNDDDDD8O8Z$7II+++IZDDMMNMN$,, # :~: MMMMMNMMM+ :, +?DOI~ ,:~?7$$ZODDNMNN8Z7??+=~:, ,~=+?7I?=:,,,,:=?I7$$$$$$$ZZZOO8DDNNMMNNNNNMMMMNMZ+ # ~=, MMMMMMMM8 ,NNNO , ,~?ZDNMNNNNNNND8O7?:,,, ,~=7DMMMMMMMMMZ~ # ,:+~ ,MMMMMMMMO ~N8: ,,:~, ,:~:,,, ,:~, ,:~~=+++?7$O8DNNNNNMMN8$=, ~+$ZO8DNNMMNNNND8OOOZ$+:, :=+I8MMMMDOI # ,=+ MMMMMMMMMDMZ, ~?: ,,,,, ,:~~~, ,NN?$NMMMMMMMMMMMN87~:,,,=+=:,,,,,,:?NMMNMMMMMMNNDNDI8MMM+ , # +=, $MMMN?$NMN, ,+?+:, ,,:~~:, :=~, 8M= , :7ONNNO?~, , :$NNMMMMMMMMMMMMMN, # ,+? ,NNO,, , :ODNNNZ: :?777I= IMO ,,=$Z7+~ ,?NNMMNZ: :7NMMMMMNMMMM8 , ,IDND~ # ,=~, 7I :+I+~::,, ~?I~ , ,~=~==: ZMZ ?8I: :I$DND$?~ :?$8MMN$?: ?ZDMMMMMMMM7 +$NMMMMMI # ~~ , ,=+=: ,== ::, , ZMM: ?NN8: ,7NMMNI, ,$NMMMO: =NMMMNMM+ I8NMMMM, # ~= ,~:,,~~, ,:, ,,,, , ,, +MN8 ,~OMM8Z, :+ZMNDOI =IMMN8= ,=NNMO ?NMMMD # ::, ,,,:: ,:, ,, =MMO~ , ?NMMMI, ,INMMMDI, ~DMMN+, NMO ?DNO~ # ,~, ,,,::, ,, , ,, ,INMND+: :MMM+ ~ZDDMMMD?~:$MMMMMMMMMMM? # ,, :~~:, ,:, ,, :ZMMMMNNMMMN, ,=ONMMMMNM$,,,,,, ,, #Z, :, ,, ,~=~, ,, ,=?7$I= , :~~~, ,,,, #MD= :: ,,,, , ,, ,,,, #MNMI :: ,,, ,,,, #7MMM? , ,~: :~:, ,:, # OMMMO: ~NMD, :=: :+=: ,,, # ~MMMN8: NMMM~ +?: ,:=++~ ,:,, # $MMMMMNI,?MMMMZ ,DM7 ,=I= ~+~~:, ,:,,, # +MMO~OMMMMMMMMD MMM7 , , =$~ ?OZ+ ,:, # ~NMN: :+DNN7MMMNMMMDODMMN+ :+?, +77+~: :::, # IMM+ ,NMMMMMMMMN?NM7 ~7+, , :+$I, , +8? :+~ # =NMD, NMMMMMM8~ ?NN, =ND? ,=??: ,8NMMMM= :++ # +DO, ~ZZ$ODI :8M~ :=I8NMNMMD+, ,:~~=: , +8MO$DMD+ ~===~: :~~: # :$~ 7NMD$, ZMM8I? ,~=: , ZNN7,:MM8~,OMNMD8DMM= ++: # =ZNOI: ?DND= ,?I~ ,,,: ,$ND+ ?NMNNNNN7+, 7MN, ,=+~ # :ZI: :, ,=7: ::,, $NN= 7NMMMM7: :DMMMN8NMMDDMN7 ,::, # ~?= ,,, ,ZM= DMMD8 ?MMMMNN7, ,I+ :~~, # =+, ,,, ,, =Z8: +$~ # :~ ,,~~, # :, ::, # ::, ,:::, # ,, ,~ # ,, ,,, # #