Mercurial > repos > george-weingart > micropita
changeset 15:61e311c4d2d0
Updated test-data
| author | george-weingart |
|---|---|
| date | Mon, 19 May 2014 02:28:59 -0400 |
| parents | a206555874ee |
| children | 7d25ecd225dd |
| files | README.txt micropita.xml src/ConstantsMicropita.pyc src/__init__.pyc src/breadcrumbs/__init__.pyc src/breadcrumbs/src/AbundanceTable.pyc src/breadcrumbs/src/CClade.pyc src/breadcrumbs/src/ConstantsBreadCrumbs.pyc src/breadcrumbs/src/KMedoids.pyc src/breadcrumbs/src/MLPYDistanceAdaptor.pyc src/breadcrumbs/src/Metric.pyc src/breadcrumbs/src/SVM.pyc src/breadcrumbs/src/UtilityMath.pyc src/breadcrumbs/src/ValidateData.pyc src/breadcrumbs/src/__init__.pyc test-data/micropita_input test-data/micropita_output |
| diffstat | 17 files changed, 243 insertions(+), 185 deletions(-) [+] |
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--- a/README.txt Wed May 07 22:12:31 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,185 +0,0 @@ -# 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!
--- a/micropita.xml Wed May 07 22:12:31 2014 -0400 +++ b/micropita.xml Mon May 19 02:28:59 2014 -0400 @@ -73,6 +73,19 @@ <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>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/micropita_input Mon May 19 02:28:59 2014 -0400 @@ -0,0 +1,229 @@ +ID Sample_0_D Sample_1_D Sample_2_D Sample_3_D Sample_4_D Sample_5_D Sample_6_D Sample_7_D Sample_8_D Sample_9_D Sample_10_D Sample_11_D Sample_12_D Sample_13_D Sample_14_D Sample_15_D Sample_16_R Sample_17_R Sample_18_R Sample_19_R Sample_20_R Sample_21_R Sample_22_R Sample_23_R Sample_24_R Sample_25_R Sample_26_R Sample_27_R Sample_28_R Sample_29_R Sample_30_E Sample_31_E Sample_32_E Sample_33_E Sample_34_E Sample_35_E Sample_36_E Sample_37_E Sample_38_E Sample_39_E Sample_40_E Sample_41_E Sample_42_E Sample_43_E Sample_44_T Sample_45_T Sample_46_T Sample_47_T +Sample Sample_0_D Sample_1_D Sample_2_D Sample_3_D Sample_4_D Sample_5_D Sample_6_D Sample_7_D Sample_8_D Sample_9_D Sample_10_D Sample_11_D Sample_12_D Sample_13_D Sample_14_D Sample_15_D Sample_16_R Sample_17_R Sample_18_R Sample_19_R Sample_20_R Sample_21_R Sample_22_R Sample_23_R Sample_24_R Sample_25_R Sample_26_R Sample_27_R Sample_28_R Sample_29_R Sample_30_E Sample_31_E Sample_32_E Sample_33_E Sample_34_E Sample_35_E Sample_36_E Sample_37_E Sample_38_E Sample_39_E Sample_40_E Sample_41_E Sample_42_E Sample_43_E Sample_44_T Sample_45_T Sample_46_T Sample_47_T +Group Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Complex Moderate_Dissimilarity_Feature Moderate_Dissimilarity_Feature Moderate_Dissimilarity_Feature Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity Moderate_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity_Feature High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity High_Dissimilarity 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0 0 0 0 0 0 0 0 0 +Root|Taxa_164 0.4835113024 0 0 50.3719699426 0 51.9771225761 0 0 0 0 0 50.3547158319 50.2502208924 0 53.8459195753 54.3741304475 0.024577205 0 0 0 0 0 0 0 0 0 50.3286767397 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3925771428 0 0 0 0 0 +Root|Taxa_165 52.0968027182 0.1727382739 0 0 52.0064350501 0 0 50.5583883437 53.6043075055 53.0323951087 0.015868495 0.2276317641 0 0 0 0 0 0 0 0.4527866454 0 0 0.3027420952 0 0.384873724 0 53.9270526894 0 0 0 0 0 0 0.1809349348 0.3820501581 0 0 0 0 0.4145347686 0 0 0 0 0 0 0 0 +Root|Taxa_166 52.7505897873 53.9549468598 51.4271330184 52.6350719286 0 0 0 51.4072420959 0.1322200828 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3235715088 0 0 51.7533492233 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3218342588 0 +Root|Taxa_167 0 0 0 0.3558686048 0 0 0 0 0 0 0 0 0 0 0.1432562186 50.6823351017 0 0 0 0 0 0 0.2934262859 0 0 0 50.0240967706 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_168 0 0 0 0 0 54.3451420666 0 51.3494668429 51.5131678207 52.5733542268 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53.7249478704 51.0031927895 53.9509039005 53.833396097 0 0 0 0 0 0 0 0 0 0.3597522784 0 0 0 0 0 50.109383045 0 0 0 0 0 0.3478571808 0.2179063686 0 0.2819056479 0 0 0 0 0 0.0854901409 0 0.2940278032 0 0 0 +Root|Taxa_178 51.1271601449 54.8860173667 0 0 0 0 0 0 0 0 0 0 0 0 0 51.6268134565 0 0 0 0 0 0 0 0 0 0 0 51.3849503597 0 0.4858649487 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_179 0 0 0 0 52.166291339 0 0 53.765176324 0 0 0.1970422822 0 0 0 0 0 0.4265009899 0 0 0 0 0 0 0 0 0 0 52.5279778091 0 0 0 0 0 0 0.3352695026 0 0 0.33414374 0 0 0 0.3020387723 0 0 0.2431528132 0 0 0 +Root|Taxa_180 0 0 0 0 0.3200819933 0 0 0 0 0 0 50.583385197 0 0 0 0 0 0 0 0 0.0013638078 0 0 0 0 0 0 53.4349148139 0 0 0 0 0 0 0.4186021993 0 0 0.1217993021 0 0.3372202371 0 0 0 0 0 0 0 0 +Root|Taxa_181 0.2819251852 0 0 54.7493143772 54.2492072498 0 0 50.8945849016 0 0 0 0 0.4230922399 52.4557762201 0 50.5470429792 0 0 0 0 0 0 0 0 0 0 0 50.262423604 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_182 0 0 0 0 0 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0.3393123192 0 0.2282115619 0 0 0 0 0 52.3706346328 0 0 0.3694309992 0 0 0 0 0 0 0 0.0214825341 0 0 100.213819152 0.4798189848 0 0 0 0 0 +Root|Taxa_187 52.9225564174 50.9556974217 0 51.9632176621 53.041227125 52.1350513686 0 0 0 0.0109429699 0 52.6077372192 0 0 52.6016736512 0 0 0 0 0 0 0 0 0 0.1115057321 0 0 53.6644244189 0 0.0444891502 0 0.2264456126 0 0 0 0 0 0 0 0 0.4351778245 104.24137393 0 0 0 0.302465445 0 0.003393226 +Root|Taxa_188 0 0 50.7929335187 0 0 0 0 0.0230501905 0 53.6741909148 50.5908790468 0 0 54.3781425253 53.9150822838 51.9440621712 0 0 0 0 0 0 0 0 0 0 0 51.3050243267 0.3083766005 0 0 0 0 0 0 0 0 0 0 0 0 101.222657064 0 0 0 0 0 0 +Root|Taxa_189 53.1156860216 0 50.2004019551 0 53.5393781093 0 0 0.4767799759 0 0 0 0 0 0 0 0 0 0 0.2510714584 0 0 0 0 0 0.0313474882 0 0 54.3585907576 0 0 0 0 0 0 0 0.3815601461 0 0 0.0661751984 0 0 103.093749372 0 0.3328062103 0 0 0 0 +Root|Taxa_190 0 51.8421062419 0 0 0 53.3427531356 52.5952915076 0 52.2138916423 0 0.1584601307 0 0 0 0 0 0 0.1606301123 0 0 0 0 0 0 0 0 0 52.6852858362 0 0 0 0 0 0 0 0 0 0 0 0 0 104.906827283 0.2424074586 0 0 0 0 0 +Root|Taxa_191 0 51.2781148402 0 0 0.2445757987 0 0 52.2653477791 0 0 50.7773450711 0 0.096988651 52.9500712259 0 0 0 0 0 0 0 0 0 0 0 0 0 50.3546323716 0.3642238437 0 0 0.2580494612 0 0 0 0 0 0 0 0.4405137253 0 102.645104533 0 0 0 0 0 0 +Root|Taxa_192 52.0827037946 0 0 53.3591565492 0 0 52.3984658047 0 0 0 0 0 53.1365027963 52.1224468617 0.2413320858 0 0 0.4208501086 0 0 0 0 0 0 0 0 0 0 54.1591071637 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_193 51.1294803083 54.000378627 0 0 0 52.1201148141 0 51.4793102236 0 52.7028030721 0 0.2554842302 0 51.0278208653 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52.5398921345 0 0 0 0.2921036679 0 0 0.0121176867 0 0 0 0 0 0 0.1737701306 0 0 0 0 0 +Root|Taxa_194 0 53.3175217655 54.3162751517 0 50.1223851156 0 0.4184720644 53.8254943468 0 53.7365842832 0 50.3085076248 0 0.0440271517 54.2949627388 0 0 0 0 0 0.202831309 0 0 0 0 0.1944977516 0.0278157737 0 51.0262433359 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2768269464 0 0.1355769703 0 +Root|Taxa_195 0 0 0 0 0 0 0 0 0 54.2177701685 52.9208199615 0 51.3350564705 0 0 53.6987592548 0 0 0 0 0 0 0 0 0 0 0 0 50.4505941005 0 0 0 0.2366040567 0 0 0.0706983798 0 0.4048068191 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_196 0 0 50.6109850879 0 0 51.0658949888 0 0 0 0.4576835496 52.2501412607 0 53.2433525996 0 0.0866719841 0.1044734051 0 0 0 0 0 0.496363439 0 0 0 0 0 0 53.8202104672 0 0 0 0 0 0 0 0.4776304856 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_197 0 0 51.6313839246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52.7963856942 0 0 0 0 0 0 0.2973905015 0.0408162681 0 0 0 0 0 0 0.0982300275 0 0 0 0 +Root|Taxa_198 0 53.047207326 0 0 0 53.2380971568 54.5651723179 0 0 0 0 50.0759819974 0 0 0 0 0 0 0 0 0 0.191167896 0 0 0 0 0 0 50.0878653178 0 0.1127647836 0 0 0 0.3103576187 0 0 0 0 0 0 0 0 0 0.3988252675 0 0 0 +Root|Taxa_199 0 0 0 0 52.0714878521 0 0 51.3096678644 0 0 50.4793163079 0 0 0 53.2342697162 0 0 0 0 0 0 0.322882781 0 0 0 0.0518037931 0 0 53.2300848959 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2133714863 0 0 0 0.4607870842 +Root|Taxa_200 0 0 0 0 0 0 0 0 54.8676932307 0 0 0 54.8465490773 0 50.0099690264 50.7561511906 0 0 0 0 0.1307286405 0 0 0 0 0.0415073515 0 0 52.6545608425 0 0 0 0 0 0 0.184158767 0 0 0 0 0 0 104.026411169 0 0 0 0 0 +Root|Taxa_201 0 0 0.0684272076 0 0 0 0 0 0 0 0 0 0 0 0 0.3333347799 0 0 0 0 0 0 0.0661544997 0 0 0 0 0 51.4443606906 0 0 0.0771979655 0 0 0 0 0 0 0 0 0 0.2761105027 102.275033805 0 0 0 0 0 +Root|Taxa_202 54.5088647863 0 0 0 0.0321756373 0 0 0 52.0726025305 0 0 53.0253269521 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2083645807 0.1062779642 54.2981710912 0 0 0 0 0 0 0 0.3699929419 0 0 0 0 0 101.947183747 0 0 0 0 0 +Root|Taxa_203 0 0 0 53.0890034691 53.4478307116 0 0 52.0807007841 0 0 0.3932551686 0 0 50.8859238684 0 0 0 0 0 0 0 0 0.4819659907 0 0 0 0 0 50.4463062145 0 0 0 0 0 0.0022291218 0 0 0 0.0260392603 0 0 0 103.245769713 0 0 0 0 0 +Root|Taxa_204 0 0 0 50.2568349213 0 0 0 0 52.4975993007 0 0 0 0 0 0 0 0.3938938146 0 0 0 0 0 0 0 0.1752154 0 0 0 50.8254339282 0 0 0 0 0 0 0 0 0 0 0.2911676263 0 0 102.001279284 0 0 0 0 0 +Root|Taxa_205 0 0 52.7163994001 0 0 0 52.2676963135 0 0 0 53.5958965115 0 0 0.3119935484 0.476493088 0.0382216967 0 0 0 0 0 0 0 0 0 0 0.2807420398 0 50.4720582791 0 0 0.0450551832 0.3918770618 0 0 0 0 0 0 0 0 0 102.486209016 0 0 0.4059267464 0.4356299041 0.4526614144 +Root|Taxa_206 0 0 0 53.5087326257 0 53.0326686943 53.9077681483 0 0 0 0 0 0 53.8938971864 52.5574477678 53.8098522525 0 0 0 0 0 0 0 0 0 0 0 0 54.2401876661 0.2242680732 0.115238483 0 0 0 0 0.4881061169 0 0 0 0 0 0 102.186344705 0 0 0 0 0 +Root|Taxa_207 51.2183210273 51.1563774994 0 0 54.1762942805 0 0 0 51.2704897195 52.8968592985 0 52.288966104 0 0 0 54.3906412249 0 0 0 0 0.0048042154 0 0 0 0 0 0 0 51.2408250309 0 0 0 0 0 0 0 0 0 0 0 0 0 103.110597368 0 0 0 0 0 +Root|Taxa_208 0 0 0 0.0546805022 0 0.1599409596 50.0472074245 0.4099746803 51.4051352965 0 0 0 0 53.2887290406 0 0.0848935397 0 0 0 0 0 0 0 0.1555916655 0 0 0 0 0 53.0135385478 0.1497035187 0 0.0691993821 0 0.3373772825 0 0 0 0 0 0 0 0 0 0.2522945018 0 0 0 +Root|Taxa_209 0 50.1030172793 0 0 0 0 54.1650217307 52.2230603121 0 0 0 0 0 0 0 0.1143171628 0 0 0 0 0 0 0 0 0 0 0 0 0 51.8871962649 0.299430337 0 0 0 0 0 0 0.4798221512 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_210 0 0 0 0 0 0 50.5549695516 52.1576796275 0 0 0 0 0 0 0 52.3992524271 0 0 0 0.1706714754 0 0 0 0 0.3811782646 0.0008517706 0 0 0 51.0207565229 0 0 0 0 0 0 0 0 0 0 0.0102807436 0 0 0 0 0.0085181585 0 0 +Root|Taxa_211 50.536257303 0 0 0 0 0 0 0 0 0 51.9813477573 0 51.5911214861 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53.1296406718 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4077664043 0 +Root|Taxa_212 0 0 0 0 0.4699813783 0 0 0 0 54.5530559822 0 50.743119663 51.3155881447 0 0 53.2941828392 0 0 0 0 0 0 0 0 0 0 0.0342022552 0 0 54.1038020638 0 0 0 0.040960994 0 0 0 0.4947938425 0 0 0 0 0.3862038599 0 0 0 0 0 +Root|Taxa_213 0.3399472909 0 0 0 0 0 54.6671775034 0.050131803 50.2999962265 0 51.8713213189 0 0 0 53.2943170335 51.1243603164 0 0 0 0 0 0 0 0 0 0 0 0 0 50.6101294432 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +Root|Taxa_214 50.0115213059 0 0 0 0 50.8371273498 0 0 0 54.4139864126 0 0 0 0 0 0 0 0 0 0.3015435222 0 0 0 0 0 0 0 0.0872919265 0.2777439996 50.3179817753 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4557640334 0 0 0 +Root|Taxa_215 0 0.4561298327 0 53.5216413012 54.8891010063 50.9129555637 0 0 0 0 0 0 0 0 53.4880926895 0 0 0 0 0 0 0 0 0 0 0 0 0.1940475073 0.2803421878 50.3585027491 0 0 0 0.345883306 0.3534255574 0 0 0 0 0 0.2075798899 0 0.4493876473 0 0 0 0 0 +Root|Taxa_216 0 52.664567111 54.9286304319 0 0 0.3528460271 0 0 0 53.8271900954 53.2864160864 0 0 53.8916659856 0 0 0.0390396631 0 0 0 0 0 0 0 0 0 0 0 0.0501403367 53.9106611035 0 0 0 0 0 0 0 0 0 0 0 0 0 102.503416561 0 0 0.2323723816 0 +Root|Taxa_217 0 0 0 53.8189770697 0 0 0 0 0 0 51.0739213403 51.9325727684 53.6586894371 0 50.8848681615 0 0 0 0 0 0 0.4134750657 0.2984209586 0 0 0 0 0.3984011824 0 50.0637489202 0.1745273923 0 0 0 0 0 0 0 0 0 0 0 0.0970082737 102.133370457 0 0 0 0 +Root|Taxa_218 53.6309027759 0 0 54.1993531611 0 53.8609164351 0 0 0 0 0 0 0 0 0 0 0 0 0.4230445366 0 0 0 0 0 0 0.3460879368 0 0 0 54.0328089965 0 0 0 0 0 0 0 0 0 0 0.1875089533 0 0 102.742579014 0 0 0 0 +Root|Taxa_219 0 0 50.6959593123 0 0 0 0 0 52.9583336932 51.2063060177 0 0 0 0 0.1839092432 0.1624310372 0 0 0.2217437424 0 0 0 0 0 0 0 0 0 0 51.3989543421 0.1469196015 0 0 0.4515484111 0 0 0 0 0 0 0.362398058 0 0 103.426555217 0 0 0 0 +Root|Taxa_220 53.138860148 54.260252989 50.1122213715 0 53.7052604162 0 0.4760342302 0 51.9385050693 0.191283382 0 0 51.217538964 0 0.2580800578 50.1174597734 0 0 0 0 0 0 0.115889335 0 0 0 0 0 0 53.3111145776 0 0 0 0 0 0 0 0 0 0 0 0 0 104.430682236 0 0 0 0 +Root|Taxa_221 0 0.3384535892 0.4106893002 52.1411487055 50.5241190477 0 0 53.9774121283 0 0 0 52.1082021353 0 0 0 0 0.4172253944 0 0 0 0 0 0 0 0 0 0 0 0 50.9817139231 0 0 0 0 0 0 0 0 0 0.4224842988 0 0 0 102.376952212 0.2260951816 0 0.3067861355 0 +Root|Taxa_222 0 52.3172435891 52.6471280765 0 51.567115685 0 0 53.6724555185 0 0 0 0 0 54.2428549188 51.6482654065 0 0 0 0 0 0 0.2425898966 0 0 0 0 0 0 0 51.1118820396 0 0 0 0 0 0 0 0 0 0 0.2951844288 0 0 102.047792613 0 0 0 0 +Root|Taxa_223 0 0 0.2600381862 0 0 50.2577764475 0.4992511416 0 0 0 0 51.3720073928 0 53.1526569712 0.3864806128 0 0 0.0519771654 0 0 0 0 0 0.3896198936 0 0 0 0.4447746424 0 51.736707456 0 0 0 0 0 0 0 0 0 0 0 0 0 103.397909634 0 0 0 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/micropita_output Mon May 19 02:28:59 2014 -0400 @@ -0,0 +1,1 @@ +representative Sample_22_R Sample_20_R Sample_44_T Sample_28_R Sample_40_E Sample_10_D Sample_26_R Sample_46_T Sample_32_E Sample_16_R Sample_23_R Sample_21_R Sample_45_T Sample_29_R Sample_41_E Sample_1_D Sample_27_R Sample_47_T Sample_33_E Sample_17_R