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date Tue, 13 May 2014 21:58:57 -0400
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1 # Using microPITA commandline #
2
3 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.
4
5
6 ## Expected input file. ##
7
8 I. PCL file or BIOM file
9
10 BIOM file definition:
11 For BIOM file definition please see http://biom-format.org/
12
13 PCL file definition:
14 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.
15
16 1. Rows represent metadata and features (bugs), columns represent samples.
17 2. The first row by default should be the sample ids.
18 3. Metadata rows should be next.
19 4. Lastly, rows containing features (bugs) measurements (like abundance) should be after metadata rows.
20 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).
21 5. By default the file is expected to be TAB delimited.
22 6. If a consensus lineage or hierarchy of taxonomy is contained in the feature name, the default delimiter between clades is the pipe ("|").
23
24 II. Targeted feature file
25 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.
26
27
28 ## Basic unsupervised methods ##
29 Please note, all calls to microPITA should work interchangeably with PCL or BIOM files. BIOM files do not require the --lastmeta or --id arguments.
30
31 There are four unsupervised methods which can be performed:
32 diverse (maximum diversity), extreme (most dissimilar), representative (representative dissimilarity) and features (targeted feature).
33
34 The first three methods are performed as follows (selecting a default 10 samples):
35
36 > python MicroPITA.py --lastmeta Label -m representative input/Test.pcl output.txt
37 > python MicroPITA.py -m representative input/Test.biom output.txt
38
39 > python MicroPITA.py --lastmeta Label -m diverse input/Test.pcl output.txt
40 > python MicroPITA.py -m diverse input/Test.biom output.txt
41
42 > python MicroPITA.py --lastmeta Label -m extreme input/Test.pcl output.txt
43 > python MicroPITA.py -m extreme input/Test.biom output.txt
44
45 Each of the previous methods are made up of the following pieces:
46 1. python MicroPITA.py to call the microPITA script.
47 2. --lastmeta which indicates the keyword (first column value) of the last row that contains metadata (PCL input only).
48 3. -m which indicates the method to use in selection.
49 4. input/Test.pcl or input/Test.biom which is the first positional argument indicating an input file
50 5. output.txt which is the second positional argument indicating the location to write to the output file.
51
52 Selecting specific features has additional arguments to consider --targets (required) and --feature_method (optional).
53
54 > python MicroPITA.py --lastmeta Label -m features --targets input/TestFeatures.taxa input/Test.pcl output.txt
55 > python MicroPITA.py -m features --targets input/TestFeatures.taxa input/Test.biom output.txt
56
57 > python MicroPITA.py --lastmeta Label -m features --feature_method abundance --targets input/TestFeatures.taxa input/Test.pcl output.txt
58 > python MicroPITA.py -m features --feature_method abundance --targets input/TestFeatures.taxa input/Test.biom output.txt
59
60 These additional arguments are described as:
61 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!
62 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.
63 To differentiate the methods, rank tends to select samples in which the feature dominates the samples regardless of it's abundance.
64 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.
65
66
67 ## Basic supervised methods ##
68
69 Two supervised methods are also available:
70 distinct and discriminant
71
72 These methods require an additional argument --label which is the first column keyword of the row used to classify samples for the supervised methods.
73 These methods can be performed as follows:
74
75 > python MicroPITA.py --lastmeta Label --label Label -m distinct input/Test.pcl output.txt
76 > python MicroPITA.py --label Label -m distinct input/Test.biom output.txt
77
78 > python MicroPITA.py --lastmeta Label --label Label -m discriminant input/Test.pcl output.txt
79 > python MicroPITA.py --label Label -m discriminant input/Test.biom output.txt
80
81
82 ## Custom alpha- and beta-diversities ##
83
84 The default alpha diversity for the maximum diversity sampling method is inverse simpson; the default beta-diversity for representative and most dissimilar
85 selection is bray-curtis dissimilarity. There are several mechanisms that allow one to change this. You may:
86
87 1. Choose from a selection of alpha-diveristy metrics.
88 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).
89
90 > python MicroPITA.py --lastmeta Label -m diverse -a simpson input/Test.pcl output.txt
91 > python MicroPITA.py -m diverse -a simpson input/Test.biom output.txt
92
93 A case where inverting the metric is needed.
94
95 > python MicroPITA.py --lastmeta Label -m diverse -a dominance -f input/Test.pcl output.txt
96 > python MicroPITA.py -m diverse -a dominance -f input/Test.biom output.txt
97
98 2. Choose from a selection of beta-diversity metrics.
99 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.
100
101 > python MicroPITA.py --lastmeta Label -m representative -b euclidean input/Test.pcl output.txt
102 > python MicroPITA.py -m representative -b euclidean input/Test.biom output.txt
103
104 > python MicroPITA.py --lastmeta Label -m extreme -b euclidean input/Test.pcl output.txt
105 > python MicroPITA.py -m extreme -b euclidean input/Test.biom output.txt
106
107 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
108 (-i,--envr) as well as indicate using Unifrac with (-b,--beta)
109
110 > python MicroPITA.py --lastmeta Label -m extreme -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
111 > python MicroPITA.py -m extreme -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
112 > python MicroPITA.py --lastmeta Label -m extreme -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
113 > python MicroPITA.py -m extreme -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
114 > python MicroPITA.py --lastmeta Label -m representative -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
115 > python MicroPITA.py -m representative -b unifrac_weighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
116 > python MicroPITA.py --lastmeta Label -m representative -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.pcl output.txt
117 > python MicroPITA.py -m representative -b unifrac_unweighted -o input/Test.tree -i input/Test-env.txt input/Test.biom output.txt
118
119 3. Supply your own custom alpha-diversity per sample as a metadata (row) in your pcl file.
120
121 > python MicroPITA.py --lastmeta Label -m diverse -q alpha_custom input/Test.pcl output.txt
122 > python MicroPITA.py -m diverse -q alpha_custom input/Test2.biom output.txt
123
124 4. Supply your own custom beta diversity as a matrix (provided in a seperate file).
125
126 > python MicroPITA.py --lastmeta Label -m representative -x input/Test_Matrix.txt input/Test.pcl output.txt
127 > python MicroPITA.py -m representative -x input/Test_Matrix.txt input/Test.biom output.txt
128 > python MicroPITA.py --lastmeta Label -m extreme -x input/Test_Matrix.txt input/Test.pcl output.txt
129 > python MicroPITA.py -m extreme -x input/Test_Matrix.txt input/Test.biom output.txt
130
131
132 ## Changing defaults ##
133
134 Sample Selection:
135 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.
136
137 > python MicroPITA.py --lastmeta Label -m representative -n 6 input/Test.pcl output.txt
138 > python MicroPITA.py -m representative -n 6 input/Test.biom output.txt
139
140 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.
141
142 Stratification:
143 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".
144
145 > python MicroPITA.py --lastmeta Label --stratify Label -m representative input/Test.pcl output.txt
146 > python MicroPITA.py --stratify Label -m representative input/Test.biom output.txt
147
148 > python MicroPITA.py --lastmeta Label --label Label --stratify StratifyLabel -m distinct input/Test.pcl output.txt
149 > python MicroPITA.py --label Label --stratify StratifyLabel -m distinct input/Test2.biom output.txt
150
151 Changing PCL file defaults:
152 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.
153
154 > python MicroPITA.py --lastmeta Label -m representative -w taxonomy_5 input/FeatureMetadata.pcl output.txt
155
156 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.
157 --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.
158 This only applys to PCL files.
159
160 > python MicroPITA.py --id Sample --lastmeta Label -m representative input/Test.pcl output.txt
161
162 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.
163 Here is an example of reading the comma delimited file micropita/input/CommaDelim.pcl
164 This only applys to PCL files.
165
166 > python MicroPITA.py --delim , --lastmeta Label -m representative input/CommaDelim.pcl output.txt
167
168 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.
169 This only applys to PCL files.
170
171 > python MicroPITA.py --featdelim . --lastmeta Label -m representative input/PeriodDelim.pcl output.txt
172
173
174 ## Dependencies ##
175 Please note the following dependencies need to be installed for micropita to run.
176 1. Python 2.x http://www.python.org/download/
177 2. blist http://pypi.python.org/pypi/blist/
178 3. NumPy http://numpy.scipy.org/
179 4. SciPy http://www.scipy.org/
180 5. PyCogent http://pycogent.sourceforge.net/install.html
181 6. mlpy http://mlpy.sourceforge.net/
182 7. mpi4py http://mpi4py.scipy.org/
183 8. biome support http://biom-format.org/
184
185 This covers how to use microPITA. Thank you for using this software and good luck with all your endeavors!