| Previous changeset 3:5f670146a9af (2017-02-27) |
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Commit message:
"planemo upload for repository https://github.com/ImmPortDB/immport-galaxy-tools/tree/master/flowtools/cross_sample commit e7eab2dca0c1f73f580362f61425a78d4c8892ce" |
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added:
runCrossSample.py runCrossSample.xml test-data/gmfi.flowmfi test-data/input1.flowtext test-data/input2.flowtext test-data/input3.flowtext test-data/mfi.flowmfi test-data/out1.flowstat1 test-data/out1.flowstat2 test-data/out1.flowstat3 test-data/out2.flowstat1 test-data/out2.flowstat2 test-data/out2.flowstat3 test-data/run1/input1.flowtext.flowclr test-data/run1/input2.flowtext.flowclr test-data/run1/input3.flowtext.flowclr test-data/run2/input1.flowtext.flowclr test-data/run2/input2.flowtext.flowclr test-data/run2/input3.flowtext.flowclr |
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removed:
cross_sample/runCrossSample.py cross_sample/runCrossSample.xml cross_sample/src/README cross_sample/src/cent_adjust.c cross_sample/src/find_connected.c cross_sample/src/flock1.c cross_sample/src/flock2.c cross_sample/test-data/gmfi.flowmfi cross_sample/test-data/input1.flowtext cross_sample/test-data/input2.flowtext cross_sample/test-data/input3.flowtext cross_sample/test-data/mfi.flowmfi cross_sample/test-data/out1.flowstat1 cross_sample/test-data/out1.flowstat2 cross_sample/test-data/out1.flowstat3 cross_sample/test-data/out2.flowstat1 cross_sample/test-data/out2.flowstat2 cross_sample/test-data/out2.flowstat3 cross_sample/test-data/run1/input1.flowtext.flowclr cross_sample/test-data/run1/input2.flowtext.flowclr cross_sample/test-data/run1/input3.flowtext.flowclr cross_sample/test-data/run2/input1.flowtext.flowclr cross_sample/test-data/run2/input2.flowtext.flowclr cross_sample/test-data/run2/input3.flowtext.flowclr |
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| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/runCrossSample.py --- a/cross_sample/runCrossSample.py Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| b'@@ -1,224 +0,0 @@\n-#!/usr/bin/env python\n-######################################################################\n-# Copyright (c) 2016 Northrop Grumman.\n-# All rights reserved.\n-######################################################################\n-from __future__ import print_function\n-import sys\n-import os\n-from scipy.stats import gmean\n-from argparse import ArgumentParser\n-from collections import defaultdict\n-import pandas as pd\n-\n-#\n-# version 1.1 -- April 2016 -- C. Thomas\n-# modified to read in several input files and output to a directory\n-# + generates summary statistics\n-# also checks before running that input files are consistent with centroid file\n-#\n-\n-\n-def compare_MFIs(input_files, f_names, mfi_file):\n- header_MFIs = ""\n- flag_error = False\n- with open(mfi_file, "r") as mfi_check:\n- mfi_fl = mfi_check.readline().split("\\t")\n- header_MFIs = "\\t".join([mfi_fl[h] for h in range(1, len(mfi_fl))])\n-\n- for hh, files in enumerate(input_files):\n- with open(files, "r") as inf:\n- hdrs = inf.readline()\n- if hdrs != header_MFIs:\n- sys.stderr.write(hdrs + "headers in " + f_names[hh] + " are not consistent with FLOCK centroid file:\\n" + header_MFIs + "\\n")\n- flag_error = True\n- if flag_error:\n- sys.exit(2)\n-\n-\n-def stats_MFIs(cs_df, ctr, mfi_calc):\n- if mfi_calc == "mfi":\n- MFIs = cs_df.groupby(\'Population\').mean().round(decimals=2)\n- elif mfi_calc == "gmfi":\n- MFIs = cs_df.groupby(\'Population\').agg(lambda x: gmean(list(x))).round(decimals=2)\n- else:\n- MFIs = cs_df.groupby(\'Population\').median().round(decimals=2)\n- pop_freq = (cs_df.Population.value_counts(normalize=True) * 100).round(decimals=2)\n- sorted_pop_freq = pop_freq.sort_index()\n- MFIs[\'Percentage\'] = sorted_pop_freq\n- MFIs[\'Population\'] = MFIs.index\n- MFIs[\'SampleName\'] = "".join(["Sample", str(ctr).zfill(2)])\n- return MFIs\n-\n-\n-def get_pop_prop(input_files, summary_stat, mfi_stats, marker_names, mfi_calc):\n- pop_count = defaultdict(dict)\n- mrk = marker_names.strip().split("\\t")\n- markers = "\\t".join([mrk[m] for m in range(1, len(mrk))])\n-\n- ctr_mfi = 0\n- nb_pop = 0\n- tot = {}\n- with open(mfi_stats, "a") as mfis:\n- mfis.write("\\t".join([markers, "Percentage", "Population", "SampleName"]) + "\\n")\n- for files in input_files:\n- cs = pd.read_table(files)\n- tot[files] = len(cs.index)\n- for pops in cs.Population:\n- if pops in pop_count[files]:\n- pop_count[files][pops] += 1\n- else:\n- pop_count[files][pops] = 1\n- if (len(pop_count[files]) > nb_pop):\n- nb_pop = len(pop_count[files])\n- ctr_mfi += 1\n- cs_stats = stats_MFIs(cs, ctr_mfi, mfi_calc)\n- cs_stats.to_csv(mfis, sep="\\t", header=False, index=False)\n-\n- ctr = 0\n- with open(summary_stat, "w") as outf:\n- itpop = [str(x) for x in range(1, nb_pop + 1)]\n- cols = "\\t".join(itpop)\n- outf.write("FileID\\tSampleName\\t" + cols + "\\n")\n- for eachfile in pop_count:\n- tmp = []\n- for num in range(1, nb_pop + 1):\n- if num not in pop_count[eachfile]:\n- pop_count[eachfile][num] = 0\n- tmp.append(str((pop_count[eachfile][num] / float(tot[eachfile])) * 100))\n- props = "\\t".join(tmp)\n- ctr += 1\n- sample_name = "".join(["Sample", str(ctr).zfill(2)])\n- outf.write("\\t".join([input_files[eachfile], sample_name, props]) + "\\n")\n-\n-\n-def run_cross_sample(input_files, f_names, mfi_file, output_dir, summary_stat,\n- mfi_stats, tool_directory, mfi_calc):\n- markers = ""\n- # Strip off Header Line\n- with open(mfi_file, "r") as mfi_in, open("mfi.txt", "w") as mfi_out:\n- markers = mfi_in.readline().str'..b'e in enumerate(input_files):\n- run_command = tool_directory + "/bin/cent_adjust mfi.txt " + flow_file\n- print(run_command)\n- os.system(run_command)\n- flow_name = os.path.split(flow_file)[1]\n- outfile = os.path.join(output_dir, flow_name + ".flowclr")\n- outputs[outfile] = f_names[nm]\n- with open(flow_file, "r") as flowf, open("population_id.txt", "r") as popf, open(outfile, "w") as outf:\n- f_line = flowf.readline()\n- f_line = f_line.rstrip()\n- f_line = f_line + "\\tPopulation\\n"\n- outf.write(f_line)\n-\n- for line in flowf:\n- line = line.rstrip()\n- pop_line = popf.readline()\n- pop_line = pop_line.rstrip()\n- line = line + "\\t" + pop_line + "\\n"\n- outf.write(line)\n- get_pop_prop(outputs, summary_stat, mfi_stats, markers, mfi_calc)\n- return\n-\n-\n-def generate_CS_stats(mfi_stats, all_stats):\n- df = pd.read_table(mfi_stats)\n- means = df.groupby(\'Population\').mean().round(decimals=2)\n- medians = df.groupby(\'Population\').median().round(decimals=2)\n- stdev = df.groupby(\'Population\').std().round(decimals=2)\n- all_markers = []\n- with open(mfi_stats, "r") as ms:\n- ms_fl = ms.readline().strip()\n- all_markers = ms_fl.split("\\t")[0:-2]\n-\n- with open(all_stats, "w") as mstats:\n- hdgs = ["\\t".join(["_".join([mrs, "mean"]), "_".join([mrs, "median"]), "_".join([mrs, "stdev"])]) for mrs in all_markers]\n- mstats.write("Population\\t")\n- mstats.write("\\t".join(hdgs) + "\\n")\n- for pops in set(df.Population):\n- tmp_line = []\n- for mar in all_markers:\n- tmp_line.append("\\t".join([str(means.loc[pops, mar]), str(medians.loc[pops, mar]), str(stdev.loc[pops, mar])]))\n- mstats.write(str(pops) + "\\t")\n- mstats.write("\\t".join(tmp_line) + "\\n")\n-\n-\n-if __name__ == "__main__":\n- parser = ArgumentParser(\n- prog="runCrossSample",\n- description="Run CrossSample on Flow file")\n-\n- parser.add_argument(\n- \'-i\',\n- dest="input_files",\n- required=True,\n- action=\'append\',\n- help="File locations for flow text files.")\n-\n- parser.add_argument(\n- \'-n\',\n- dest="filenames",\n- required=True,\n- action=\'append\',\n- help="Filenames")\n-\n- parser.add_argument(\n- \'-m\',\n- dest="mfi",\n- required=True,\n- help="File location for the MFI text file.")\n-\n- parser.add_argument(\n- \'-o\',\n- dest="out_path",\n- required=True,\n- help="Path to the directory for the output files.")\n-\n- parser.add_argument(\n- \'-M\',\n- dest="mfi_calc",\n- required=True,\n- help="what to calculate for centroids.")\n-\n- parser.add_argument(\n- \'-s\',\n- dest="sstat",\n- required=True,\n- help="File location for the summary statistics.")\n-\n- parser.add_argument(\n- \'-S\',\n- dest="mfi_stat",\n- required=True,\n- help="File location for the MFI summary statistics.")\n-\n- parser.add_argument(\n- \'-t\',\n- dest="tool_dir",\n- required=True,\n- help="File location for cent_adjust.")\n-\n- parser.add_argument(\n- \'-a\',\n- dest="all_stats",\n- required=True,\n- help="File location for stats on all markers.")\n-\n- args = parser.parse_args()\n-\n- input_files = [f for f in args.input_files]\n- input_names = [n for n in args.filenames]\n- compare_MFIs(input_files, input_names, args.mfi)\n- run_cross_sample(input_files, input_names, args.mfi, args.out_path, args.sstat, args.mfi_stat, args.tool_dir, args.mfi_calc)\n- generate_CS_stats(args.mfi_stat, args.all_stats)\n-\n- sys.exit(0)\n' |
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| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/runCrossSample.xml --- a/cross_sample/runCrossSample.xml Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,162 +0,0 @@ -<tool id="run_cross_sample" name="Run Cross Sample" version="1.1"> - <description>using a Flow file that was run using FLOCK</description> - <requirements> - <requirement type="package" version="0.16.0">scipy</requirement> - <requirement type="package" version="0.17.1">pandas</requirement> - <requirement type="package" version="1.0">flock</requirement> - </requirements> - <stdio> - <exit_code range="2" level="fatal" description="Columns inconsistencies between files and centroids file. See stderr for more details." /> - </stdio> - <command><![CDATA[ - python $__tool_directory__/runCrossSample.py -m "${centroid}" -s "${stats}" -S "${mfistats}" -a "${allstats}" -M "${mfi}" -t $__tool_directory__ -o crossSampleOutputs - #for $f in $input# - -i $f - -n "${f.name}" - #end for# - ]]> - </command> - <inputs> - <param format="flowtext" name="input" type="data_collection" collection_type="list" label="Flowtext files Collection"/> - <param format="flowmfi" name="centroid" type="data" label="Centroid file"/> - <param name="mfi" type="select" label="Calculate centroids using:"> - <option value="mfi" selected="true">Mean Fluorescence Intensity</option> - <option value="mdfi">Median Fluorescence Intensity</option> - <option value="gmfi">Geometric Mean Fluorescence Intensity</option> - </param> - </inputs> - <outputs> - <collection type="list" label="CrossSample on ${input.name}" name="output"> - <discover_datasets pattern="(?P<name>.*)" directory="crossSampleOutputs" format="flowclr" /> - </collection> - <data format="flowstat1" name="stats" label="Population distribution after CrossSample on ${input.name} using ${mfi}"/> - <data format="flowstat2" name="mfistats" label="${mfi} centroids of CrossSample on ${input.name} using ${mfi}"/> - <data format="flowstat3" name="allstats" label="${mfi} descriptive stats of CrossSample on ${input.name} using ${mfi}"/> - </outputs> - <tests> - <test> - <param name="input"> - <collection type="list"> - <element name="input1" value="input1.flowtext"/> - <element name="input2" value="input2.flowtext"/> - <element name="input3" value="input3.flowtext"/> - </collection> - </param> - <param name="centroid" value="mfi.flowmfi"/> - <param name="mfi" value="mfi"/> - <output name="stats" file="out1.flowstat1" lines_diff="6"/> - <output name="allstats" file="out1.flowstat3"/> - <output name="mfistats" file="out1.flowstat2" compare="sim_size"/> - <output_collection name="output" type="list" count="3"> - <element name="input1" file="run1/input1.flowtext.flowclr"/> - <element name="input2" file="run1/input2.flowtext.flowclr"/> - <element name="input3" file="run1/input3.flowtext.flowclr"/> - </output_collection> - </test> - <test> - <param name="input"> - <collection type="list"> - <element name="input1" value="input1.flowtext"/> - <element name="input2" value="input2.flowtext"/> - <element name="input3" value="input3.flowtext"/> - </collection> - </param> - <param name="centroid" value="gmfi.flowmfi"/> - <param name="mfi" value="gmfi"/> - <output name="stats" file="out2.flowstat1" lines_diff="6"/> - <output name="allstats" file="out2.flowstat3"/> - <output name="mfistats" file="out2.flowstat2" compare="sim_size"/> - <output_collection name="output" type="list" count="3"> - <element name="input1" file="run2/input1.flowtext.flowclr"/> - <element name="input2" file="run2/input2.flowtext.flowclr"/> - <element name="input3" file="run2/input3.flowtext.flowclr"/> - </output_collection> - </test> - </tests> - <help><![CDATA[ - This tool runs CrossSample using the MFI from FLOCK and text-converted FCS files. - ------ - -**Input** - -This tool compares text-converted FCS files from a data collection to the MFI generated by a FLOCK run. The same data collection merged and run with FLOCK should be used to ensure consistency in the attribution of events to populations. - -.. class:: infomark - -The option chosen for the centroids (mean, median or geometric mean) should be the same as used to run FLOCK. - -**Output** - -Each event within each file of a dataset collection is attributed to a population depending on its intensity profile. -A table of the population composition of each file is generated as well as MFI and population descriptive statistics. - -.. class:: infomark - -Tip: If headers in each text-converted FCS file do not match those in the centroid file, the program will not run. Edit the input file using the Remove, rearrange and/or rename columns tool in the Flow File Tools section prior to Cross Sample analysis. - ------ - -**Example** - -*Input* - fluorescence intensities per marker per event:: - - Marker1 Marker2 Marker3 - 33 47 11 - 31 64 11 - 21 62 99 - 14 34 60 - -*Centroid file* - mean, geometric mean or median fluorescence intensity per marker per population:: - - Population Marker1 Marker2 Marker3 - 1 38 49 10 - 2 21 63 100 - 3 31 52 45 - 4 11 78 25 - -*Output* for each text file - fluorescence intensities per marker and population ID per event:: - - Marker1 Marker2 Marker3 Population - 33 47 11 1 - 31 64 11 6 - 21 62 99 2 - 14 34 60 7 - -*Summary table* - counts of events in each population in each file:: - - Filename SampleName Pop1 Pop2 Pop3 ... - File1 Biosample1 255 147 18 ... - File2 Biosample2 27 61 965 ... - File3 Biosample3 201 546 221 ... - File4 Biosample4 11 77 327 ... - -*Centroid MFI Summary table* - for each file, mean, median or geometric mean fluorescence intensities per marker per population:: - - Marker1 Marker2 Marker3 ... Population Percentage SampleName - 154 885 24 ... 1 0.2 Biosample1 - 458 74 574 ... 2 0.3 Biosample1 - 3 210 86 ... 3 0.05 Biosample1 - 140 921 19 ... 1 0.1 Biosample2 - 428 79 508 ... 2 0.25 Biosample2 - 9 225 90 ... 3 0.3 Biosample2 - -*MFI Descriptive Statistics table* - for the set of files, mean, median and standard deviation of each centroid per marker per population, as well as mean, median and standard deviation of the population's proportion:: - - Population Marker1_mean Marker1_median Marker1_stdev ... Percentage_mean Percentage_median Percentage_stdev - 1 94.65 90.86 25.8 ... 1.84 0.55 2.48 - 2 132.18 131.58 5.02 ... 9.89 9.76 0.33 - 3 71.8 69.68 10.53 ... 3.02 1.49 3.45 - 4 84.85 84.85 nan ... 8.52 8.52 nan - 5 161.82 132.77 61.29 ... 0.95 0.37 1.06 - ]]> - </help> - <citations> - <citation type="doi">10.1002/cyto.b.20554</citation> - <citation type="doi">10.3389/fimmu.2012.00302</citation> - <citation type="doi">10.1371/journal.pone.0038408</citation> - <citation type="doi">10.1371/journal.ppat.1003076</citation> - <citation type="doi">10.1016/j.clim.2012.12.003</citation> - <citation type="doi">10.1038/srep02327</citation> - </citations> -</tool> |
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| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/src/README --- a/cross_sample/src/README Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,139 +0,0 @@ -This package contains R code for converting and transforming a binary -FCS file, using the FCSTrans software, and the C code for running the -flock1 and flock2 population identification software. - -src - contains the C code for flock1, flock2 and cent_adjust -bin - contains the compiled C code for flock1, flock2 and cent_adjust, - plus the R code for using the FCSTrans algorithm for file - conversion and data transformation. -doc - documentation for FCSTrans and FLOCK algorithms -example - sample data and output from FCSTrans and FLOCK - -To run this software sucessfully the code assumes the software was installed -in the /usr/local/flock directory and that R, plus the Bioconductor flowCore -module has been installed. If you use the ipconvert.sh shell script, you -may need to adjust the location of the RScript executable and the location -of the FCSTrans.R code by editing the shell script. - -############################################################################# -# Overview -############################################################################# -ImmPort-FLOCK - -FLOCK (FLOw Clustering without K), an automated population discovery tool for -multidimensional FCM data was designed to specifically take into account the -unique feature of FCM data and produce objective segregation of cell -populations. - -FLOCK parameter settings can be customized by defining Bins and Density -Threshold. The number of bins is an integer specifying the number of -equal-sized regions the data will be partitioned into on each axis. Increasing -the number of bins increases the sensitivity to detect rare populations but -may also result in single populations being divided. Density Threshold is the -cut-off value to separate the dense regions from background. It is a floating -point number that helps define population centers; increasing the threshold -may help separate major populations but could cause the algorithm to overlook -rare populations. - -############################################################################# -# Compiling C code -############################################################################# -cd bin -cc -o flock1 ../src/flock1.c ../src/find_connected.c -lm -cc -o flock2 ../src/flock2.c -lm -cc -o cent_adjust ../src/cent_adjust.c -lm - -############################################################################# -# FCSTrans -############################################################################# -A shell script named ipconvert.sh is included that runs the FCSTrans R -code for converting and transforming a binary FCS file. The output consists -of one text file containing the transformed channel intensity values and -another file containing a list of the FCS parameters. - -cd bin -/usr/local/flock/bin/ipconvert.sh ../example/data/FCS2.fcs -/usr/local/flock/bin/ipconvert.sh ../example/data/FCS3.fcs - -############################################################################# -# Running flock1 or flock2 -############################################################################# -Running the FLOCK1 and FLOCK2 algorithms generate 8 output files that have -generic file names. For this reason, it is recommended that one output -directory be created for one input file to the program. - -cd example/output/FCS2 -/usr/local/flock/bin/flock1 ../../data/FCS2.txt - -cd example/output/FCS3 -/usr/local/flock/bin/flock2 ../../data/FCS3.txt - -Files created: MFI.txt, percentage.txt, population_id.txt, profile.txt, - flock_results.txt, coordinates.txt, population_center.txt - and percentage.txt - -Usage Information for FLOCK1 ----------------------------------------------------------------------------- -basic mode: flock1 fcs.txt -advanced_ mode: flock1 fcs.txt num_bin density_index max_num_pop - -Usage Information for FLOCK2 ----------------------------------------------------------------------------- -basic mode: flock data_file -advanced mode 0 (specify maximum # of pops): flock data_file max_num_pop -advanced mode 1 (without # of pops): flock data_file num_bin density_index -advanced mode 2 (specify # of pops): flock data_file num_bin density_index - number_of_pop -advanced mode 3 (specify both # of pops): flock data_file num_bin density_index - number_of_pop max_num_pop - -FLOCK Output Files ----------------------------------------------------------------------------- -coordinates.txt: - Output is the intensity values for each marker and event - -flock_results.txt: - A combination of the input file, event identifiers and population - identifiers. - -MFI.txt: - Provides the mean fluorescence intensity for each population for each - marker/parameter - -population_id.txt: - Contains population identifiers (i.e, values from [1 to n] where n is - the population assigned to the corresponding events in the input data - file, one identifier per row.) - -population_center.txt: - Contains the centroid coordinates for each identified population - -percentage.txt: - Includes the population identifiers and percentage of events within that - population (relative to the whole data file) - -profile.txt: - Displays an expression profile, where the approximate expression level - for each marker is assigned a numeric value from 1-4, for each identified - population - -fcs_properties.txt: - Contains the number of events, number of populations, and number of - markers, as well as the algorithm parameters used in the analysis - -############################################################################# -# Running cent_adjust -############################################################################# -Running the cent_adjust algorithm generates 4 output files that have -generic file names. For this reason, it is recommened that one output -directory be created for one input file to the program. - -mkdir example/output/FCS2/cent_adjust -cd example/output/FCS2/cent_adjust -/usr/local/flock/bin/cent_adjust ../population_center.txt ../coordinates.txt - -Files created: MFI.txt, percentage.txt, population_id.txt and profile.txt - -Usage Information for cent_adjust ----------------------------------------------------------------------------- -basic mode: cent_adjust input_center input_data_file |
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| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/src/cent_adjust.c --- a/cross_sample/src/cent_adjust.c Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,1009 +0,0 @@\n-/////////////////////////////////////////////////////////\n-// Cent_adjust version number and modification history\n-// ImmPort BISC project\n-// Author: Yu "Max" Qian\n-// v1.01: Oct 16, 2009\n-// Line 899 of the main function:\n-// Changed kmean_term=1 to kmean_term=2\n-//////////////////////////////////////////////////////////\n-\n-\n-#include <time.h>\n-#include <stdio.h>\n-#include <stdlib.h>\n-#include <math.h>\n-#include <string.h>\n-\n-#define DEBUG 0\n-#define LINE_LEN 1024\n-#define FILE_NAME_LEN 128\n-#define PARA_NAME_LEN 64\n-#define MAX_VALUE 1000000000\n-#define CUBE 0\n-\n-\n-void getctrfileinfo(FILE *f_src_ctr, long *num_clust)\n-{\n-\tint ch=\'\\n\';\n-\tint prev=\'\\n\';\n-\tlong num_rows=0;\n-\n-\twhile ((ch = fgetc(f_src_ctr))!= EOF )\n- {\n-\t\tif (ch == \'\\n\')\n- {\n-\t\t\t++num_rows;\n- }\n-\t\tprev = ch;\n- }\n-\tif (prev!=\'\\n\')\n-\t\t++num_rows;\n-\t\n-\t*num_clust=num_rows;\n-\t//printf("center file has %ld rows\\n", *num_clust);\n-}\n-\n-/************************************* Read basic info of the source file **************************************/\n-void getfileinfo(FILE *f_src, long *file_Len, long *num_dm, char *name_string, int *time_ID)\n-{\n-\tchar src[LINE_LEN];\n-\tchar current_name[64];\n-\tchar prv;\n-\n-\tlong num_rows=0;\n-\tlong num_columns=0;\n-\tint ch=\'\\n\';\n-\tint prev=\'\\n\';\n-\tlong time_pos=0;\n-\tlong i=0;\n-\tlong j=0;\n-\n-\t\n-\n-\tsrc[0]=\'\\0\';\n-\tfgets(src, LINE_LEN, f_src);\n-\n-\tname_string[0]=\'\\0\';\n-\tcurrent_name[0]=\'\\0\';\n-\tprv=\'\\n\';\n-\n-\twhile ((src[i]==\' \') || (src[i]==\'\\t\')) //skip space and tab characters\n-\t\ti++;\n-\n-\twhile ((src[i]!=\'\\r\') && (src[i]!=\'\\n\')) //repeat until the end of the line\n-\t{\n-\t\tcurrent_name[j]=src[i];\n-\t\t\n-\t\tif ((src[i]==\'\\t\') && (prv!=\'\\t\')) //a complete word\n-\t\t{\n-\t\t\tcurrent_name[j]=\'\\0\';\n-\t\t\t\n- /* \n- * Commented out John Campbell, June 10 2010\n- * We no longer want to automatically remove Time column.\n- * This column should have been removed by column selection\n- if (0!=strcmp(current_name,"Time"))\n- {\n- num_columns++; //num_columns does not inlcude the column of Time\n- time_pos++;\n- strcat(name_string,current_name); \n- strcat(name_string,"\\t");\n- }\n- else\n- {\n- *time_ID=time_pos;\n- }\n- */\n-\n- num_columns++;\n- strcat(name_string,current_name);\n- strcat(name_string,"\\t");\n-\n-\t\t current_name[0]=\'\\0\';\n-\t\t j=0;\t\t\t\n-\t\t}\t\t\n-\t\t\n-\t\tif ((src[i]==\'\\t\') && (prv==\'\\t\')) //a duplicate tab or space\n-\t\t{\n-\t\t\tcurrent_name[0]=\'\\0\';\n-\t\t\tj=0;\n-\t\t}\n-\t\t\n- if (src[i]!=\'\\t\')\n-\t j++;\n-\n-\t\t\n-\t\tprv=src[i];\n-\t\ti++;\n-\t}\n-\t\n-\tif (prv!=\'\\t\') //the last one hasn\'t been retrieved\n-\t{\n-\t\tcurrent_name[j]=\'\\0\';\n- /* \n- * Commented out John Campbell, June 10 2010\n- * We no longer want to automatically remove Time column.\n- * This column should have been removed by column selection\n- if (0!=strcmp(current_name,"Time"))\n- {\n- num_columns++;\n- strcat(name_string,current_name);\n- time_pos++;\n- }\n- else\n- {\n- *time_ID=time_pos;\n- }\n- */\n-\n- num_columns++;\n- strcat(name_string,current_name);\n-\t}\n-\n-\tif (DEBUG==1)\n-\t{\n-\t\tprintf("time_ID is %d\\n",*time_ID);\n-\t\tprintf("name_string is %s\\n",name_string);\n-\t}\n-\n-\t// # of rows\n-\n-\twhile ((ch = fgetc(f_src))!= EOF )\n- {\n-\t\tif (ch == \'\\n\')\n- {\n-\t\t\t++num_rows;\n- }\n-\t\tprev = ch;\n- }\n-\tif (prev!=\'\\n\')\n-\t\t++num_rows;\n-\t\n-\t*file_Len=num_rows;\n-\t*num_dm=num_columns; \n-\n-\t//printf("original file size is %ld; number of dimensions is %ld\\n", *file_Len, *num_dm);\n-}\n-\n-\n-\n-///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n-/************************************* Read the source file into uncomp_data **************************************/\n-void readsource(FILE *f_src, lo'..b'0;\n-\tlong j=0;\n-\n-\tlong *cluster_id;\n-\tlong *IDmapping; //this is to keep the original populationID of the center.txt\n-\n-\tdouble kmean_term=0;\n-\t\n-\tdouble **cluster_center;\n-\tdouble **orig_data;\n-\tdouble **normalized_data;\n-\t\t\n-/*\n-\t_strtime( tmpbuf );\n- printf( "Starting time:\\t\\t\\t\\t%s\\n", tmpbuf );\n-\t_strdate( tmpbuf );\n- printf( "Starting date:\\t\\t\\t\\t%s\\n", tmpbuf );\n-*/\n-\n-\tif (argc!=3)\n-\t{\n-\t\tprintf("usage: cent_adjust input_center input_data_file\\n"); \n-\t\texit(0);\n-\t}\t\n-\t\n-\n-\tf_src_ctr=fopen(argv[1],"r");\t\n-\t\n-\t//read source data\n-\tf_src=fopen(argv[2],"r");\n-\t\n-\tgetfileinfo(f_src, &file_Len, &num_dm, name_string, &time_id); //get the filelength, number of dimensions, and num/name of parameters\n-\n-\trewind(f_src); //reset data file pointer\t\n-\n-\torig_data = (double **)malloc(sizeof(double*)*file_Len);\n-\tmemset(orig_data,0,sizeof(double*)*file_Len);\n-\tfor (i=0;i<file_Len;i++)\n-\t{\n-\t\torig_data[i]=(double *)malloc(sizeof(double)*num_dm);\n-\t\tmemset(orig_data[i],0,sizeof(double)*num_dm);\n-\t}\n-\t\n-\treadsource(f_src, file_Len, num_dm, orig_data, time_id); //read the data;\n-\t\n-\tfclose(f_src);\n-\t/////////////////////////////////////////////////////////////////////////////\n-\tgetctrfileinfo(f_src_ctr, &num_clust); //get how many populations\n-\tnorm_used=0;\n-\tdist_used=0;\n-\tkmean_term=2; //modified on Oct 16, 2009: changed kmean_term=1 to kmean_term=2\n-\n-\trewind(f_src_ctr); //reset center file pointer\n-\n-\t//read population center\n-\tcluster_center=(double **)malloc(sizeof(double*)*num_clust);\n-\tmemset(cluster_center,0,sizeof(double*)*num_clust);\n-\tfor (i=0;i<num_clust;i++)\n-\t{\n-\t\tcluster_center[i]=(double*)malloc(sizeof(double)*num_dm);\n-\t\tmemset(cluster_center[i],0,sizeof(double)*num_dm);\n-\t}\n-\tfor (i=0;i<num_clust;i++)\n-\t\tfor (j=0;j<num_dm;j++)\n-\t\t\tcluster_center[i][j]=0;\n-\n-\tIDmapping=(long *)malloc(sizeof(long)*num_clust);\n-\tmemset(IDmapping,0,sizeof(long)*num_clust);\n-\n-\treadcenter(f_src_ctr,num_clust,num_dm,cluster_center,IDmapping); //read population center\n- fclose(f_src_ctr);\n-\n-\t/////////////////////////////////////////////////////////////////////////////\n-\tnormalized_data=(double **)malloc(sizeof(double*)*file_Len);\n-\tmemset(normalized_data,0,sizeof(double*)*file_Len);\n-\tfor (i=0;i<file_Len;i++)\n-\t{\n-\t\tnormalized_data[i]=(double *)malloc(sizeof(double)*num_dm);\n-\t\tmemset(normalized_data[i],0,sizeof(double)*num_dm);\n-\t}\n-\t\n-\ttran(orig_data, file_Len, num_dm, norm_used, normalized_data);\n-\t/************************************************* Compute number of clusters *************************************************/\n-\t\n-\tcluster_id=(long*)malloc(sizeof(long)*file_Len);\n-\tmemset(cluster_id,0,sizeof(long)*file_Len);\n-\n-\tassign_event(normalized_data,num_clust,dist_used,kmean_term,file_Len,num_dm,cluster_id,cluster_center,0);\n-\n-\t\n-\t//show(orig_data,cluster_id,file_Len,num_clust,num_dm,show_data,num_disp,name_string); \n-\tshow(orig_data, cluster_id, file_Len, num_clust, num_dm, name_string, IDmapping);\n-\n-\tf_cid=fopen("population_id.txt","w");\n-\n-\tfor (i=0;i<file_Len;i++)\n-\t\tfprintf(f_cid,"%ld\\n",IDmapping[cluster_id[i]]);\n-\t\t\n-\n-\tfclose(f_cid);\n- \n-\t//added April 16, 2009\n-\tf_mfi=fopen("MFI.txt","w");\n-\n-\tfor (i=0;i<num_clust;i++)\n-\t{\n-\t\tfprintf(f_mfi,"%ld\\t",IDmapping[i]);\n-\n-\t\tfor (j=0;j<num_dm;j++)\n-\t\t{\n-\t\t\tif (j==num_dm-1)\n-\t\t\t\tfprintf(f_mfi,"%.0f\\n",cluster_center[i][j]);\n-\t\t\telse\n-\t\t\t\tfprintf(f_mfi,"%.0f\\t",cluster_center[i][j]);\n-\t\t}\n-\t}\n-\tfclose(f_mfi);\n-\n-\t//ended April 16, 2009\n-\n-\tfor (i=0;i<num_clust;i++)\n-\t\tfree(cluster_center[i]);\n-\tfree(cluster_center);\n-\t\t\n-\n-\t/********************************************** Release memory ******************************************/\n- \n-\tfor (i=0;i<file_Len;i++)\n-\t{\n-\t\tfree(orig_data[i]);\t\t\n-\t\tfree(normalized_data[i]);\n-\t}\n-\t\n-\tfree(orig_data);\n-\tfree(normalized_data);\n-\tfree(cluster_id);\n-\tfree(IDmapping);\n-\n-/*\n-\t_strtime( tmpbuf );\n- printf( "Ending time:\\t\\t\\t\\t%s\\n", tmpbuf );\n-\t_strdate( tmpbuf );\n- printf( "Ending date:\\t\\t\\t\\t%s\\n", tmpbuf );\n-*/\n-\n-}\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/src/find_connected.c --- a/cross_sample/src/find_connected.c Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| @@ -1,176 +0,0 @@ -#include <stdlib.h> -#include <stdio.h> -#include <string.h> -#include <assert.h> - -//static const char *rcsid = "$Id: find_connected.c,v 1.1 2008/09/05 21:54:40 rpl Exp $"; - -int find_connected(int **G, int num_dense_grids, int ndim, int *grid_clusterID); -void depth_first(int startnode); - -void bail(const char *); /* exits via abort */ -static void check_clusters(int *gcID, int ndense); -static void merge_cluster(int from, int into); - - -/* Vars that will not change througout the depth-first recursion. We - store them here to avoid endless replication on the stack. */ -static int **Gr=0; -static int *gcID = 0; /* grid cluster IDs */ -static int *cluster_count=0; /* count of nodes per cluster */ -static int ndense=0; -static int ndim=0; -/* cid changes between depth-first searches, but is constant within a - single search, so it goes here. */ -static int cid=0; - -/* Find connected components in the graph of neighboring grids defined in G. - * - * Output: - * - * grid_clusterID[] -- cluster to which each dense grid was assigned - * return value -- number of clusters assigned. - */ -int find_connected(int **G, int n_dense_grids, int num_dm, int *grid_clusterID) -{ - int nclust=0; /* number of clusters found */ - int i; - int *subfac; - int subval=0,nempty=0; - int clustid=0; - - size_t sz = n_dense_grids*sizeof(int); - cluster_count = malloc(sz); - if(!cluster_count) - bail("find_connected: Unable to allocate %zd bytes.\n"); - memset(cluster_count,0,sz); - - /* set up the statics that will be used in the DFS */ - Gr=G; - gcID = grid_clusterID; - ndense = n_dense_grids; - ndim = num_dm; - - - for(i=0;i<ndense;++i) - grid_clusterID[i] = -1; - - for(i=0;i<ndense;++i) { - if(grid_clusterID[i] < 0) { /* grid hasn't been assigned yet */ - cid = nclust++; - depth_first(i); - } - } - -#ifndef NDEBUG - check_clusters(gcID,ndense); -#endif - - /* At this point we probably have some clusters that are empty due to merging. - We want to compact the cluster numbering to eliminate the empty clusters. */ - - subfac = malloc(sz); - if(!subfac) - bail("find_connected: Unable to allocate %zd bytes.\n"); - subval=0; - nempty=0; - - /* cluster #i needs to have its ID decremented by 1 for each empty cluster with - ID < i. Precaclulate the decrements in this loop: */ - for(i=0;i<nclust;++i) { - //clustid = grid_clusterID[i]; - if(cluster_count[i] == 0) { - subval++; - nempty++; - } - subfac[i] = subval; - } - - /* Now apply the decrements to all of the dense grids */ - for(i=0;i<ndense;++i) { - clustid = grid_clusterID[i]; - grid_clusterID[i] -= subfac[clustid]; - } - -#ifndef NDEBUG - // check_clusters(gcID,ndense); -#endif - - /* correct the number of clusters found */ - nclust -= nempty; - - return nclust; -} - - -/* Do a depth-first search for a single connected component in graph - * G. Start from node, tag the nodes found with cid, and record - * the tags in grid_clusterID. Also, record the node count in - * cluster_count. If we find a node that has already been assigned to - * a cluster, that means we're merging two clusters, so zero out the - * old cid's node count. - * - * Note that our graph is constructed as a DAG, so we can be - * guaranteed to terminate without checking for cycles. - * - * Note2: this function can potentially recurse to depth = ndense. - * Plan stack size accordingly. - * - * Output: - * - * grid_clusterID[] -- array where we tag the nodes. - * cluster_count[] -- count of the number of nodes per cluster. - */ - -void depth_first(int node) -{ - int i; - - if(gcID[node] == cid) // we're guaranteed no cycles, but it is possible to reach a node - return; // through two different paths in the same cluster. This early - // return saves us some unneeded work. - - /* Check to see if we are merging a cluster */ - if(gcID[node] >= 0) { - /* We are, so zero the count for the old cluster. */ - cluster_count[ gcID[node] ] = 0; - merge_cluster(gcID[node], cid); - return; - } - - /* Update for this node */ - gcID[node] = cid; - cluster_count[cid]++; - - /* Recursively search the child nodes */ - for(i=0; i<ndim; ++i) - if(Gr[node][i] >= 0) /* This is a child node */ - depth_first(Gr[node][i]); -} - -void bail(const char *msg) -{ - fprintf(stderr,"%s",msg); - abort(); -} - - -static void check_clusters(int *gcID, int ndense) -{ - int i; - - for(i=0; i<ndense; ++i) - if(gcID[i] < 0) { - fprintf(stderr,"faulty cluster id at i= %d\n",i); - abort(); - } -} - -static void merge_cluster(int from, int into) -{ - int i; - - for(i=0; i<ndense; ++i) - if(gcID[i] == from) - gcID[i] = into; -} |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/src/flock1.c --- a/cross_sample/src/flock1.c Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,2400 +0,0 @@\n-/*****************************************************************************\n-\t\n-\tFLOCK: FLOw cytometry Clustering without K (Named by: Jamie A. Lee and Richard H. Scheuermann) \n-\t\n-\tAuthor: (Max) Yu Qian, Ph.D.\n-\t\n-\tCopyright: Scheuermann Lab, Dept. of Pathology, UTSW\n-\t\n-\tDevelopment: November 2005 ~ Forever\n-\n-\tAlgorithm Status: May 2007: Release 1.0\n-\n-\tUsage: flock data_file\n-\t\t Note: the input file format must be channel values and the delimiter between two values must be a tab.\n-\n- Changes made July 23, 2010: made errors to STDERR\n-\tChanges made Nov 4, 2010: added one more error (select_num_bin<min_grid) || (select_num_bin>max_grid) to STDERR;\n-\t MAX_GRID changed to 50 as larger than 50 seems not useful for any file we have got\n-\t\n-******************************************************************************/\n-#include <time.h>\n-#include <stdio.h>\n-#include <stdlib.h>\n-#include <math.h>\n-#include <string.h>\n-#include <sys/stat.h>\n-#include <unistd.h>\n-#include <assert.h>\n-\n-\n-#define DEBUG 0\n-#define LINE_LEN 1024\n-#define FILE_NAME_LEN 128\n-#define PARA_NAME_LEN 64\n-#define MAX_VALUE 1000000000\n-#define MIN_GRID 6\n-#define MAX_GRID 50\n-\n-#define NORM_METHOD 2 //2 if z-score; 0 if no normalization; 1 if min-max \n-#define KMEANS_TERM 100\n-//#define MAX_NUM_POP 30\n-\n-\n-int find_connected(int **G, int num_dense_grids, int ndim, int *grid_clusterID);\n-\n-/************* Read basic info of the source file ****************************/\n-void getfileinfo(FILE *f_src, int *file_Len, int *num_dm, char *name_string, int *time_ID)\n-{\n- char src[LINE_LEN];\n- char current_name[64];\n- char prv;\n-\n- int num_rows=0;\n- int num_columns=0;\n- int ch=\'\\n\';\n- int prev=\'\\n\';\n- int time_pos=0;\n- int i=0;\n- int j=0;\n- int sw=0;\n-\n- src[0]=\'\\0\';\n- fgets(src, LINE_LEN, f_src);\n-\n- if ((src[0]==\'F\') && (src[1]==\'C\') && (src[2]==\'S\'))\n-\t{\n-\t\tfprintf(stderr,"the correct input format is a tab-delimited txt file, instead of FCS file.\\n");\n-\t\tabort();\n-\t}\n-\n- name_string[0]=\'\\0\';\n- current_name[0]=\'\\0\';\n- prv=\'\\n\';\n-\n- // skip space and tab characters\n- while ((src[i]==\' \') || (src[i]==\'\\t\'))\n- i++;\n-\n- // repeat until the end of line is reached\n- while ((src[i]!=\'\\0\') && (src[i]!=\'\\n\') && (src[i]!=\'\\r\'))\n- {\n- current_name[j]=src[i];\n-\t\t\n- if ((src[i]==\'\\t\') && (prv!=\'\\t\')) //a complete word\n- {\n- current_name[j]=\'\\0\';\n-\t\t\t\n- if (0!=strcmp(current_name,"Time"))\n- {\n- num_columns++; //num_columns does not inlcude the column of Time\n- time_pos++;\n- if (sw) {\n- strcat(name_string,"\\t");\n- }\n- strcat(name_string,current_name); \n- sw = 1;\n- }\n- else\n- {\n- *time_ID=time_pos;\n- }\n- \n- \n- current_name[0]=\'\\0\';\n- j=0;\t\t\t\n- }\t\t\n-\t\t\n- if ((src[i]==\'\\t\') && (prv==\'\\t\')) //a duplicate tab or space\n- {\n- current_name[0]=\'\\0\';\n- j=0;\n- }\n-\t\t\n- if (src[i]!=\'\\t\')\n- j++;\n-\t\t\n- prv=src[i];\n- i++;\n- }\n-\t\n- if (prv!=\'\\t\') //the last one hasn\'t been retrieved\n- {\n- current_name[j]=\'\\0\';\n- \n- if (0!=strcmp(current_name,"Time"))\n- {\n- num_columns++;\n- strcat(name_string,"\\t");\n- strcat(name_string,current_name);\n- time_pos++;\n- }\n- else\n- {\n- *time_ID=time_pos;\n- }\n- \n- \n- }\n- if (DEBUG==1)\n- {\n- printf("time_ID is %d\\n",*time_ID);\n- printf("name_string is %s\\n",name_string);\n- }\n-\n- //start computing # of rows\n-\n- while ((ch = fgetc(f_src))!= EOF )\n- {\n- if (ch == \'\\n\')\n- {\n- ++num_rows;\n- }\n- prev = ch;\n- }\n- if (prev!=\'\\n\')\n- ++num_rows;\n-\n- //added on July 23, 2010\n- if (num_rows<50)\n- {\n- fprintf(stderr,"Number of events '..b'lation_ID=(int*)malloc(sizeof(int)*file_Len);\n- memset(all_population_ID,0,sizeof(int)*file_Len);\n-\n- kmeans(normalized_data, num_population, KMEANS_TERM, file_Len, num_dm, all_population_ID, population_center);\n- show(input_data, all_population_ID, file_Len, num_population, num_dm, para_name_string);\n-\n- ID2Center_all(input_data,file_Len,num_dm,num_population,all_population_ID,population_center);\n- \n-\n- f_cid=fopen("population_id.txt","w");\n- f_ctr=fopen("population_center.txt","w");\n- f_out=fopen("coordinates.txt","w");\n- f_results=fopen("flock_results.txt","w");\n-\n-/*\n- f_parameters=fopen("parameters.txt","w");\n- fprintf(f_parameters,"Number_of_Bins\\t%d\\n",num_bin);\n- fprintf(f_parameters,"Density\\t%f\\n",aver_index);\n- fclose(f_parameters);\n-*/\n-\n- for (i=0;i<file_Len;i++)\n-\tfprintf(f_cid,"%d\\n",all_population_ID[i]+1); //all_population_ID[i] changed to all_population_ID[i]+1 to start from 1 instead of 0: April 16, 2009\n-\n- /*\n- * New to check for min/max to add to parameters.txt\n- *\n- */\n- \n- fprintf(f_out,"%s\\n",para_name_string);\n- //fprintf(f_results,"%s\\tEvent\\tPopulation\\n",para_name_string);\n- fprintf(f_results,"%s\\tPopulation\\n",para_name_string);\n- for (i=0;i<file_Len;i++)\n- {\n-\tfor (j=0;j<num_dm;j++)\n-\t{\n-\t\tif (input_data[i][j] < min) {\n-\t\t\tmin = (int)input_data[i][j];\n-\t\t}\n-\t\tif (input_data[i][j] > max) {\n-\t\t\tmax = (int)input_data[i][j];\n-\t\t}\n-\t\tif (j==num_dm-1)\n-\t\t{\n-\t\t\tfprintf(f_out,"%d\\n",(int)input_data[i][j]);\n-\t\t\tfprintf(f_results,"%d\\t",(int)input_data[i][j]);\n-\t\t}\n-\t\telse\n-\t\t{\n-\t\t\tfprintf(f_out,"%d\\t",(int)input_data[i][j]);\n-\t\t\tfprintf(f_results,"%d\\t",(int)input_data[i][j]);\n-\t\t}\n-\t}\n-\t//fprintf(f_results,"%d\\t",i + 1);\n-\tfprintf(f_results,"%d\\n",all_population_ID[i]+1); //all_population_ID[i] changed to all_population_ID[i]+1 to start from 1 instead of 0: April 16, 2009\n- }\n-\n-/*\n- f_parameters=fopen("parameters.txt","w");\n- fprintf(f_parameters,"Number_of_Bins\\t%d\\n",num_bin);\n- fprintf(f_parameters,"Density\\t%d\\n",den_t_event);\n- fprintf(f_parameters,"Min\\t%d\\n",min);\n- fprintf(f_parameters,"Max\\t%d\\n",max);\n- fclose(f_parameters);\n-*/\n-\n- f_properties=fopen("fcs.properties","w");\n- fprintf(f_properties,"Bins=%d\\n",num_bin);\n- fprintf(f_properties,"Density=%d\\n",den_t_event);\n- fprintf(f_properties,"Min=%d\\n",min);\n- fprintf(f_properties,"Max=%d\\n",max);\n- fprintf(f_properties,"Populations=%d\\n",num_population);\n- fprintf(f_properties,"Events=%d\\n",file_Len);\n- fprintf(f_properties,"Markers=%d\\n",num_dm);\n- fclose(f_properties);\n-\n-\n- for (i=0;i<num_population;i++) {\n-\t/* Add if we want to include population id in the output\n-\t*/\n-\tfprintf(f_ctr,"%d\\t",i+1); //i changed to i+1 to start from 1 instead of 0: April 16, 2009\n-\n-\tfor (j=0;j<num_dm;j++) {\n-\t\tif (j==num_dm-1)\n-\t\t\tfprintf(f_ctr,"%.0f\\n",population_center[i][j]);\n-\t\telse\n-\t\t\tfprintf(f_ctr,"%.0f\\t",population_center[i][j]);\n-\t}\n- }\n-\n- \t//added April 16, 2009\n-\tf_mfi=fopen("MFI.txt","w");\n-\n-\tfor (i=0;i<num_population;i++)\n-\t{\n-\t\tfprintf(f_mfi,"%d\\t",i+1);\n-\n-\t\tfor (j=0;j<num_dm;j++)\n-\t\t{\n-\t\t\tif (j==num_dm-1)\n-\t\t\t\tfprintf(f_mfi,"%.0f\\n",population_center[i][j]);\n-\t\t\telse\n-\t\t\t\tfprintf(f_mfi,"%.0f\\t",population_center[i][j]);\n-\t\t}\n-\t}\n-\tfclose(f_mfi);\n-\n-\t//ended April 16, 2009\n-\t\t\t\n- fclose(f_cid);\n- fclose(f_ctr);\n- fclose(f_out);\n- fclose(f_results);\n-\n-\n- for (i=0;i<num_population;i++)\n- {\n-\tfree(population_center[i]);\n- }\n- free(population_center);\n- \n-\n- for (i=0;i<file_Len;i++)\n- free(normalized_data[i]);\n- free(normalized_data);\t\n-\t\n- free(grid_populationID);\n-\n- free(cluster_populationID);\n- free(grid_clusterID);\n- free(cluster_ID);\n-\n- for (i=0;i<file_Len;i++)\n- free(input_data[i]);\n- free(input_data);\n-\n- free(grid_ID);\n- free(population_ID);\n- free(all_population_ID);\n- free(eventID_To_denseventID);\n-\t\t\n- ///////////////////////////////////////////////////////////\n- printf("Ending time:\\t\\t\\t\\t");\n- fflush(stdout);\n- system("/bin/date");\n-\n- return 0;\n-\n-}\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/src/flock2.c --- a/cross_sample/src/flock2.c Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b"@@ -1,3405 +0,0 @@\n-///////////\n-// Changes made:\n-// 1. Added another parameter: number of populations\n-// 2. Added a hierarchical merging step based on density change between centroids of two hyper-regions\n-// 3. Picked the longest dimensions between the population centroids to judge whether the two parts should be merged\n-// 4. Removed checking time parameter\n-// 5. Output error to stderr\n-// 6. Fixed the bug of density threshold always = 3\n-// 7. Added another error (select_num_bin<min_grid) || (select_num_bin>max_grid) to STDERR\n-// 8. Fixed a bug for 2D data by using K=e*K\n-// 9. Added some header files, may not be necessary\n-// 10. Added a lower bound (at least two) for number of populations\n-/***************************************************************************************************************************************\n-\t\n-\tFLOCK: FLOw cytometry Clustering without K (Named by: Jamie A. Lee and Richard H. Scheuermann) \n-\t\n-\tAuthor: (Max) Yu Qian, Ph.D.\n-\t\n-\tCopyright: Scheuermann Lab, Dept. of Pathology, UTSW\n-\t\n-\tDevelopment: November 2005 ~ Forever\n-\n-\tStatus: July 2010: Release 2.0\n-\n-\tUsage: flock data_file\n-\t\t Note: the input file format must be channel values and the delimiter between two values must be a tab.\n-\n-\n- \t\n-****************************************************************************************************************************************/\n-\n-#include <time.h>\n-#include <stdio.h>\n-#include <stdlib.h>\n-#include <math.h>\n-#include <string.h>\n-#include <sys/stat.h>\n-#include <unistd.h>\n-#include <assert.h>\n-\n-\n-\n-#define DEBUG 0\n-#define LINE_LEN 1024\n-#define FILE_NAME_LEN 128\n-#define PARA_NAME_LEN 64\n-#define MAX_VALUE 1000000000\n-#define MIN_GRID 6\n-#define MAX_GRID 50\n-#define E_T 1.0\n-\n-#define NORM_METHOD 2 //2 if z-score; 0 if no normalization; 1 if min-max \n-#define KMEANS_TERM 10 \n-#define MAX_POP_NUM 128\n-\n-#ifndef max\n- #define max( a, b ) ( ((a) > (b)) ? (a) : (b) )\n-#endif\n-\n-#ifndef min\n- #define min( a, b ) ( ((a) < (b)) ? (a) : (b) )\n-#endif\n-\n-static long **Gr=0;\n-static long *gcID = 0; /* grid cluster IDs */\n-static long *cluster_count=0; /* count of nodes per cluster */\n-static long ndense=0;\n-static long ndim=0;\n-/* cid changes between depth-first searches, but is constant within a\n- single search, so it goes here. */\n-static long cid=0;\n-/* Do a depth-first search for a single connected component in graph\n- * G. Start from node, tag the nodes found with cid, and record\n- * the tags in grid_clusterID. Also, record the node count in\n- * cluster_count. If we find a node that has already been assigned to\n- * a cluster, that means we're merging two clusters, so zero out the\n- * old cid's node count.\n- *\n- * Note that our graph is constructed as a DAG, so we can be\n- * guaranteed to terminate without checking for cycles. \n- *\n- * Note2: this function can potentially recurse to depth = ndense.\n- * Plan stack size accordingly. \n- *\n- * Output:\n- *\n- * grid_clusterID[] -- array where we tag the nodes.\n- * cluster_count[] -- count of the number of nodes per cluster.\n- */\n-static void merge_cluster(long from, long into)\n-{\n- int i;\n-\n- for(i=0; i<ndense; ++i)\n- if(gcID[i] == from)\n- gcID[i] = into;\n-}\n- \n-void depth_first(long node)\n-{\n- long i;\n-\n- if(gcID[node] == cid) // we're guaranteed no cycles, but it is possible to reach a node \n- return; // through two different paths in the same cluster. This early\n- // return saves us some unneeded work.\n-\n- /* Check to see if we are merging a cluster */\n- if(gcID[node] >= 0) {\n- /* We are, so zero the count for the old cluster. */\n- cluster_count[ gcID[node] ] = 0; \n- merge_cluster(gcID[node], cid);\n- return;\n- }\n-\n- /* Update for this node */\n- gcID[node] = cid;\n- cluster_count[cid]++;\n-\n- /* Recursively search the child nodes */\n- for(i=0; i<ndim; ++i)\n- if(Gr[node][i] >= 0) /* This is a child node */\n"..b'ring);\n-\n- ID2Center_all(input_data,file_Len,num_dm,num_real_pop,all_population_ID,new_population_center);\n- \n-\n- f_cid=fopen("population_id.txt","w");\n- f_ctr=fopen("population_center.txt","w");\n- f_out=fopen("coordinates.txt","w");\n- f_results=fopen("flock_results.txt","w");\n-\n-/*\n- f_parameters=fopen("parameters.txt","w");\n- fprintf(f_parameters,"Number_of_Bins\\t%d\\n",num_bin);\n- fprintf(f_parameters,"Density\\t%f\\n",aver_index);\n- fclose(f_parameters);\n-*/\n-\n- for (i=0;i<file_Len;i++)\n-\tfprintf(f_cid,"%ld\\n",all_population_ID[i]+1); //all_population_ID[i] changed to all_population_ID[i]+1 to start from 1 instead of 0: April 16, 2009\n-\n- /*\n- * New to check for min/max to add to parameters.txt\n- *\n- */\n- \n- fprintf(f_out,"%s\\n",para_name_string);\n- //fprintf(f_results,"%s\\tEvent\\tPopulation\\n",para_name_string);\n- fprintf(f_results,"%s\\tPopulation\\n",para_name_string);\n- for (i=0;i<file_Len;i++)\n- {\n-\tfor (j=0;j<num_dm;j++)\n-\t{\n-\t\tif (input_data[i][j] < min) {\n-\t\t\tmin = (int)input_data[i][j];\n-\t\t}\n-\t\tif (input_data[i][j] > max) {\n-\t\t\tmax = (int)input_data[i][j];\n-\t\t}\n-\t\tif (j==num_dm-1)\n-\t\t{\n-\t\t\tfprintf(f_out,"%d\\n",(int)input_data[i][j]);\n-\t\t\tfprintf(f_results,"%d\\t",(int)input_data[i][j]);\n-\t\t}\n-\t\telse\n-\t\t{\n-\t\t\tfprintf(f_out,"%d\\t",(int)input_data[i][j]);\n-\t\t\tfprintf(f_results,"%d\\t",(int)input_data[i][j]);\n-\t\t}\n-\t}\n-\t//fprintf(f_results,"%ld\\t",i + 1);\n-\tfprintf(f_results,"%ld\\n",all_population_ID[i]+1); //all_population_ID[i] changed to all_population_ID[i]+1 to start from 1 instead of 0: April 16, 2009\n- }\n-\n-/*\n- f_parameters=fopen("parameters.txt","w");\n- fprintf(f_parameters,"Number_of_Bins\\t%ld\\n",num_bin);\n- fprintf(f_parameters,"Density\\t%d\\n",den_t_event);\n- fprintf(f_parameters,"Min\\t%d\\n",min);\n- fprintf(f_parameters,"Max\\t%d\\n",max);\n- fclose(f_parameters);\n-*/\n-\n- f_properties=fopen("fcs.properties","w");\n- fprintf(f_properties,"Bins=%ld\\n",num_bin);\n- fprintf(f_properties,"Density=%d\\n",den_t_event);\n- fprintf(f_properties,"Min=%d\\n",min);\n- fprintf(f_properties,"Max=%d\\n",max);\n- fprintf(f_properties,"Populations=%ld\\n",num_real_pop);\n- fprintf(f_properties,"Events=%ld\\n",file_Len);\n- fprintf(f_properties,"Markers=%ld\\n",num_dm);\n- fclose(f_properties);\n-\n- for (i=0;i<num_real_pop;i++) {\n-\t/* Add if we want to include population id in the output\n-\t*/\n-\tfprintf(f_ctr,"%ld\\t",i+1); //i changed to i+1 to start from 1 instead of 0: April 16, 2009\n-\n-\tfor (j=0;j<num_dm;j++) {\n-\t\tif (j==num_dm-1)\n-\t\t\tfprintf(f_ctr,"%.0f\\n",new_population_center[i][j]);\n-\t\telse\n-\t\t\tfprintf(f_ctr,"%.0f\\t",new_population_center[i][j]);\n-\t}\n- }\n-\n- \t//added April 16, 2009\n-\tf_mfi=fopen("MFI.txt","w");\n-\n-\tfor (i=0;i<num_real_pop;i++)\n-\t{\n-\t\tfprintf(f_mfi,"%ld\\t",i+1);\n-\n-\t\tfor (j=0;j<num_dm;j++)\n-\t\t{\n-\t\t\tif (j==num_dm-1)\n-\t\t\t\tfprintf(f_mfi,"%.0f\\n",new_population_center[i][j]);\n-\t\t\telse\n-\t\t\t\tfprintf(f_mfi,"%.0f\\t",new_population_center[i][j]);\n-\t\t}\n-\t}\n-\tfclose(f_mfi);\n-\n-\t//ended April 16, 2009\n-\t\t\t\n- fclose(f_cid);\n- fclose(f_ctr);\n- fclose(f_out);\n- fclose(f_results);\n-\n-\n- for (i=0;i<num_population;i++)\n- \tfree(population_center[i]);\n- \n- free(population_center);\n- \n- for (i=0;i<num_real_pop;i++)\n-\t free(new_population_center[i]);\n- \n- free(new_population_center);\n-\n- for (i=0;i<file_Len;i++)\n- free(normalized_data[i]);\n- free(normalized_data);\t\n-\t\n- free(grid_populationID);\n-\n- free(cluster_populationID);\n- free(grid_clusterID);\n- free(cluster_ID);\n-\n- for (i=0;i<file_Len;i++)\n- free(input_data[i]);\n- free(input_data);\n-\n- free(grid_ID);\n- free(population_ID);\n- free(all_population_ID);\n- free(eventID_To_denseventID);\n-\t\t\n- ///////////////////////////////////////////////////////////\n- printf("Ending time:\\t\\t\\t\\t");\n- fflush(stdout);\n- system("/bin/date");\n-\n- /*\n- * Windows version\n- _strtime( tmpbuf );\n- printf( "Ending time:\\t\\t\\t\\t%s\\n", tmpbuf );\n- _strdate( tmpbuf );\n- printf( "Ending date:\\t\\t\\t\\t%s\\n", tmpbuf );\n- */\n- \n- return 0;\n-}\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/gmfi.flowmfi --- a/cross_sample/test-data/gmfi.flowmfi Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,27 +0,0 @@ -Population FSC SSC CD4 CCR7 CD8 CCR3 -1 362 113 519 182 0 0 -2 368 116 522 0 0 181 -3 360 108 511 0 630 472 -4 354 165 0 0 0 514 -5 329 149 0 0 0 0 -6 370 147 0 0 606 0 -7 386 154 0 310 617 294 -8 369 109 516 249 640 500 -9 413 200 0 403 0 633 -10 354 110 520 0 0 0 -11 379 133 0 0 602 562 -12 405 242 0 0 0 0 -13 402 129 514 409 613 294 -14 399 133 525 560 0 0 -15 353 109 318 0 0 0 -16 407 214 0 502 0 0 -17 432 180 503 592 0 279 -18 381 123 523 192 0 187 -19 935 946 478 612 513 523 -20 390 148 0 528 618 0 -21 614 972 0 0 0 0 -22 662 422 382 0 0 276 -23 538 965 362 471 382 442 -24 677 427 387 0 0 0 -25 724 1012 0 265 0 0 -26 957 963 311 337 0 300 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/input1.flowtext --- a/cross_sample/test-data/input1.flowtext Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/input2.flowtext --- a/cross_sample/test-data/input2.flowtext Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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' 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| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/input3.flowtext --- a/cross_sample/test-data/input3.flowtext Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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\t197\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/mfi.flowmfi --- a/cross_sample/test-data/mfi.flowmfi Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,27 +0,0 @@ -Population FSC SSC CD4 CCR7 CD8 CCR3 -1 364 117 520 188 53 71 -2 370 121 523 37 67 187 -3 363 112 512 69 632 482 -4 360 175 84 141 114 520 -5 333 156 56 68 82 109 -6 372 154 75 90 608 251 -7 389 162 83 318 620 304 -8 371 113 517 258 642 505 -9 422 214 138 420 327 640 -10 356 114 521 31 59 68 -11 381 138 85 111 604 565 -12 410 276 119 213 183 161 -13 406 137 516 424 615 305 -14 401 139 526 566 76 108 -15 356 121 322 84 75 110 -16 414 239 104 510 138 200 -17 440 198 509 598 119 289 -18 384 128 524 199 60 194 -19 944 966 490 629 533 541 -20 393 154 82 534 621 267 -21 617 975 145 224 225 173 -22 667 437 389 204 215 283 -23 546 975 366 476 387 448 -24 681 441 392 204 187 132 -25 727 1013 182 269 259 232 -26 960 975 325 343 318 311 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out1.flowstat1 --- a/cross_sample/test-data/out1.flowstat1 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,4 +0,0 @@ -FileID SampleName 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 -Rearranged FCSGateTrans on FCS2.003.20Kevents.fcs Sample01 10.18 8.555 0.15 0.42 6.67 13.3 2.985 0.0 0.05 9.825 0.0 3.265 0.065 0.045 1.42 0.495 0.125 7.6 0.465 0.03 14.78 2.36 1.385 2.205 12.0 1.625 -Rearranged FCSGateTrans on FCS2.001.20Kevents.fcs Sample02 0.0 0.41 10.735 5.69 0.275 3.075 2.945 11.245 1.285 0.0 8.975 1.04 10.935 0.085 0.25 0.585 0.015 0.225 0.71 0.535 12.76 2.775 2.01 1.585 19.97 1.885 -Rearranged FCSGateTrans on FCS2.002.20Kevents.fcs Sample03 8.88 5.35 0.05 0.35 4.615 1.945 5.765 0.015 0.18 7.445 0.105 3.0 0.195 4.695 1.61 3.165 1.515 5.28 0.485 7.53 16.55 1.525 1.77 2.64 13.6 1.74 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out1.flowstat2 --- a/cross_sample/test-data/out1.flowstat2 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,75 +0,0 @@ -FSC SSC CD4 CCR7 CD8 CCR3 Percentage Population SampleName -360.93 118.98 521.7 181.35 48.29 76.01 10.18 1 Sample01 -374.35 125.62 525.73 36.34 72.0 197.55 8.56 2 Sample01 -406.43 162.77 410.23 69.3 580.9 275.2 0.15 3 Sample01 -363.32 241.5 99.96 145.89 102.85 318.19 0.42 4 Sample01 -337.09 155.29 56.73 72.34 73.35 112.25 6.67 5 Sample01 -368.59 148.89 69.12 71.72 606.04 237.42 13.3 6 Sample01 -403.26 176.59 94.75 191.8 620.71 263.77 2.99 7 Sample01 -345.6 331.2 292.9 562.5 293.0 413.3 0.05 9 Sample01 -352.15 115.59 519.81 29.88 54.23 72.34 9.83 10 Sample01 -421.38 241.15 115.02 151.72 215.97 147.47 3.26 12 Sample01 -423.08 219.69 405.31 271.0 585.31 272.92 0.06 13 Sample01 -363.33 188.33 360.33 551.11 77.56 198.67 0.04 14 Sample01 -354.94 118.97 301.48 81.19 69.35 117.17 1.42 15 Sample01 -419.08 392.96 120.34 408.48 122.57 168.11 0.5 16 Sample01 -462.0 422.08 421.0 718.08 357.2 480.84 0.12 17 Sample01 -388.6 132.42 528.82 190.06 61.71 205.87 7.6 18 Sample01 -972.67 1014.57 510.17 649.92 498.02 507.34 0.46 19 Sample01 -436.0 331.33 195.33 587.83 698.0 475.67 0.03 20 Sample01 -602.71 967.97 140.02 220.24 213.08 176.15 14.78 21 Sample01 -687.46 490.61 376.69 209.82 192.18 267.15 2.36 22 Sample01 -551.76 988.58 350.25 482.74 364.3 411.62 1.38 23 Sample01 -647.77 387.0 397.71 171.63 167.15 143.66 2.2 24 Sample01 -726.15 1016.99 182.62 264.79 248.23 217.08 12.0 25 Sample01 -969.6 1001.4 329.26 338.48 302.98 333.58 1.62 26 Sample01 -562.4 279.96 395.37 85.56 233.15 423.82 0.41 2 Sample02 -361.23 110.65 512.56 68.97 633.79 483.67 10.74 3 Sample02 -355.26 167.2 73.65 147.5 108.78 528.01 5.69 4 Sample02 -320.36 182.36 72.15 113.6 120.13 198.04 0.27 5 Sample02 -379.34 171.32 79.2 96.05 612.98 319.43 3.08 6 Sample02 -412.99 178.92 94.51 281.53 623.12 374.9 2.94 7 Sample02 -371.1 112.03 517.49 259.35 642.93 505.5 11.24 8 Sample02 -431.34 213.62 133.05 402.6 302.14 645.66 1.28 9 Sample02 -380.63 135.19 85.29 110.18 602.83 574.54 8.98 11 Sample02 -447.85 525.88 156.54 205.92 232.46 226.29 1.04 12 Sample02 -406.39 136.68 517.93 421.76 615.38 306.36 10.94 13 Sample02 -358.41 103.0 460.06 676.12 556.82 164.12 0.08 14 Sample02 -392.88 210.34 274.9 177.9 205.7 226.14 0.25 15 Sample02 -433.7 264.01 107.35 510.36 149.73 224.73 0.58 16 Sample02 -503.0 183.67 512.67 468.67 380.67 284.33 0.02 17 Sample02 -401.98 199.4 351.38 197.56 325.84 376.18 0.22 18 Sample02 -942.13 977.37 496.89 556.9 589.52 591.96 0.71 19 Sample02 -431.49 175.21 108.33 449.62 589.52 338.83 0.53 20 Sample02 -612.66 962.76 145.03 218.29 241.58 218.2 12.76 21 Sample02 -677.97 432.92 394.9 215.63 253.73 320.89 2.78 22 Sample02 -537.34 987.67 377.09 463.75 412.69 504.01 2.01 23 Sample02 -708.86 473.52 400.38 219.62 227.69 178.15 1.58 24 Sample02 -724.21 1017.01 183.89 263.49 272.15 249.63 19.97 25 Sample02 -956.13 990.81 316.68 335.77 337.56 324.8 1.88 26 Sample02 -363.8 112.35 518.73 192.67 50.31 67.22 8.88 1 Sample03 -366.09 113.61 521.93 39.72 65.66 165.65 5.35 2 Sample03 -395.7 119.3 384.9 85.9 585.6 230.1 0.05 3 Sample03 -368.81 204.11 96.34 237.57 86.57 292.1 0.35 4 Sample03 -318.7 144.37 48.23 81.95 64.63 103.98 4.61 5 Sample03 -356.9 131.76 69.32 144.68 612.42 244.06 1.94 6 Sample03 -373.64 149.87 73.71 364.36 620.22 274.19 5.76 7 Sample03 -438.0 176.67 517.33 423.0 277.33 437.33 0.02 8 Sample03 -456.47 368.94 339.69 764.56 534.31 616.89 0.18 9 Sample03 -358.85 110.16 520.35 32.56 61.77 62.47 7.44 10 Sample03 -395.52 201.76 124.0 438.24 675.14 501.81 0.1 11 Sample03 -416.9 238.72 120.19 236.94 187.78 133.18 3.0 12 Sample03 -435.72 195.44 389.95 550.08 631.15 289.23 0.19 13 Sample03 -399.59 136.41 525.52 567.5 69.54 112.64 4.7 14 Sample03 -361.32 113.15 344.56 109.52 71.4 102.73 1.61 15 Sample03 -404.76 196.99 95.3 519.01 130.48 201.21 3.16 16 Sample03 -451.65 196.73 522.28 588.96 117.05 274.0 1.52 17 Sample03 -383.32 121.85 525.67 214.77 64.27 172.42 5.28 18 Sample03 -948.66 979.64 477.21 676.02 492.0 476.04 0.48 19 Sample03 -393.34 154.15 80.74 547.09 620.52 265.14 7.53 20 Sample03 -618.74 977.0 140.47 231.46 216.74 155.22 16.55 21 Sample03 -632.72 434.17 361.81 253.26 192.78 244.91 1.52 22 Sample03 -550.94 992.18 358.59 469.96 366.78 399.73 1.77 23 Sample03 -692.69 478.12 385.77 214.78 183.47 124.04 2.64 24 Sample03 -733.01 1018.55 182.18 275.33 250.23 190.78 13.6 25 Sample03 -971.95 998.45 334.9 363.32 315.39 269.99 1.74 26 Sample03 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out1.flowstat3 --- a/cross_sample/test-data/out1.flowstat3 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,27 +0,0 @@ -Population FSC_mean FSC_median FSC_stdev SSC_mean SSC_median SSC_stdev CD4_mean CD4_median CD4_stdev CCR7_mean CCR7_median CCR7_stdev CD8_mean CD8_median CD8_stdev CCR3_mean CCR3_median CCR3_stdev Percentage_mean Percentage_median Percentage_stdev -1 362.36 362.36 2.03 115.66 115.66 4.69 520.22 520.22 2.1 187.01 187.01 8.0 49.3 49.3 1.43 71.62 71.62 6.22 9.53 9.53 0.92 -2 434.28 374.35 111.03 173.06 125.62 92.77 481.01 521.93 74.19 53.87 39.72 27.49 123.6 72.0 94.92 262.34 197.55 140.75 4.77 5.35 4.11 -3 387.79 395.7 23.62 130.91 119.3 27.93 435.9 410.23 67.59 74.72 69.3 9.68 600.1 585.6 29.27 329.66 275.2 135.27 3.65 0.15 6.14 -4 362.46 363.32 6.82 204.27 204.11 37.15 89.98 96.34 14.26 176.99 147.5 52.47 99.4 102.85 11.5 379.43 318.19 129.33 2.15 0.42 3.06 -5 325.38 320.36 10.17 160.67 155.29 19.56 59.04 56.73 12.13 89.3 81.95 21.59 86.04 73.35 29.85 138.09 112.25 52.08 3.85 4.61 3.27 -6 368.28 368.59 11.22 150.66 148.89 19.84 72.55 69.32 5.76 104.15 96.05 37.15 610.48 612.42 3.86 266.97 244.06 45.55 6.11 3.08 6.26 -7 396.63 403.26 20.5 168.46 176.59 16.14 87.66 94.51 12.08 279.23 281.53 86.3 621.35 620.71 1.55 304.29 274.19 61.37 3.9 2.99 1.61 -8 404.55 404.55 47.31 144.35 144.35 45.71 517.41 517.41 0.11 341.18 341.18 115.72 460.13 460.13 258.52 471.42 471.42 48.2 5.63 5.63 7.93 -9 411.14 431.34 58.13 304.59 331.2 81.01 255.21 292.9 108.35 576.55 562.5 181.39 376.48 302.14 136.76 558.62 616.89 126.67 0.5 0.18 0.68 -10 355.5 355.5 4.74 112.88 112.88 3.84 520.08 520.08 0.38 31.22 31.22 1.9 58.0 58.0 5.33 67.4 67.4 6.98 8.64 8.64 1.69 -11 388.08 388.08 10.53 168.48 168.48 47.07 104.65 104.65 27.37 274.21 274.21 231.97 638.98 638.98 51.13 538.17 538.17 51.43 4.54 4.54 6.28 -12 428.71 421.38 16.73 335.25 241.15 165.09 130.58 120.19 22.63 198.19 205.92 43.13 212.07 215.97 22.59 168.98 147.47 50.14 2.43 3.0 1.21 -13 421.73 423.08 14.71 183.94 195.44 42.68 437.73 405.31 69.88 414.28 421.76 139.69 610.61 615.38 23.29 289.5 289.23 16.72 3.73 0.19 6.24 -14 373.78 363.33 22.49 142.58 136.41 43.0 448.64 460.06 83.19 598.24 567.5 67.94 234.64 77.56 279.04 158.48 164.12 43.29 1.61 0.08 2.68 -15 369.71 361.32 20.31 147.49 118.97 54.51 306.98 301.48 35.15 122.87 109.52 49.72 115.48 71.4 78.14 148.68 117.17 67.47 1.09 1.42 0.74 -16 419.18 419.08 14.47 284.65 264.01 99.6 107.66 107.35 12.52 479.28 510.36 61.47 134.26 130.48 13.97 198.02 201.21 28.44 1.41 0.58 1.51 -17 472.22 462.0 27.16 267.49 196.73 134.04 485.32 512.67 55.91 591.9 588.96 124.73 284.97 357.2 145.9 346.39 284.33 116.55 0.55 0.12 0.84 -18 391.3 388.6 9.62 151.22 132.42 42.06 468.62 525.67 101.55 200.8 197.56 12.67 150.61 64.27 151.76 251.49 205.87 109.27 4.37 5.28 3.77 -19 954.49 948.66 16.08 990.53 979.64 20.85 494.76 496.89 16.58 627.61 649.92 62.61 526.51 498.02 54.65 525.11 507.34 59.97 0.55 0.48 0.14 -20 420.28 431.49 23.44 220.23 175.21 96.79 128.13 108.33 59.81 528.18 547.09 71.02 636.01 620.52 55.87 359.88 338.83 106.83 2.7 0.53 4.19 -21 611.37 612.66 8.09 969.24 967.97 7.2 141.84 140.47 2.77 223.33 220.24 7.11 223.8 216.74 15.51 183.19 176.15 32.07 14.7 14.78 1.9 -22 666.05 677.97 29.25 452.57 434.17 32.95 377.8 376.69 16.57 226.24 215.63 23.58 212.9 192.78 35.36 277.65 267.15 39.06 2.22 2.36 0.64 -23 546.68 550.94 8.1 989.48 988.58 2.38 361.98 358.59 13.74 472.15 469.96 9.68 381.26 366.78 27.25 438.45 411.62 57.08 1.72 1.77 0.32 -24 683.11 692.69 31.65 446.21 473.52 51.33 394.62 397.71 7.78 202.01 214.78 26.42 192.77 183.47 31.32 148.62 143.66 27.39 2.14 2.2 0.53 -25 727.79 726.15 4.62 1017.52 1017.01 0.89 182.9 182.62 0.89 267.87 264.79 6.49 256.87 250.23 13.27 219.16 217.08 29.48 15.19 13.6 4.22 -26 965.89 969.6 8.54 996.89 998.45 5.47 326.95 329.26 9.33 345.86 338.48 15.18 318.64 315.39 17.52 309.46 324.8 34.46 1.75 1.74 0.13 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out2.flowstat1 --- a/cross_sample/test-data/out2.flowstat1 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,4 +0,0 @@ -FileID SampleName 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 -Rearranged FCSGateTrans on FCS2.003.20Kevents.fcs Sample01 8.5 9.99 0.12 0.67 5.145 4.76 7.925 0.105 0.165 7.27 3.9 3.7 0.12 0.015 1.325 0.72 0.17 10.48 0.955 0.0 4.35 3.195 3.925 1.435 15.705 5.355 -Rearranged FCSGateTrans on FCS2.001.20Kevents.fcs Sample02 0.05 0.33 9.37 4.345 0.225 1.575 4.41 12.41 2.385 0.0 9.395 0.725 11.265 0.0 0.075 0.5 0.18 0.46 1.54 0.395 3.915 3.635 9.05 0.895 14.01 8.86 -Rearranged FCSGateTrans on FCS2.002.20Kevents.fcs Sample03 7.265 6.76 0.07 0.575 4.185 1.035 8.955 0.16 1.1 5.46 0.995 2.145 0.295 3.74 1.41 2.675 2.53 7.615 0.88 4.68 3.98 2.515 3.275 1.69 20.385 5.625 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out2.flowstat2 --- a/cross_sample/test-data/out2.flowstat2 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,76 +0,0 @@ -FSC SSC CD4 CCR7 CD8 CCR3 Percentage Population SampleName -355.78 114.46 519.34 168.82 0.0 0.0 8.5 1 Sample01 -366.75 118.51 522.91 0.0 0.0 173.37 9.99 2 Sample01 -395.48 148.84 392.96 0.0 586.93 243.93 0.12 3 Sample01 -354.8 191.64 0.0 0.0 0.0 275.85 0.67 4 Sample01 -319.28 138.51 0.0 0.0 0.0 0.0 5.14 5 Sample01 -362.66 143.68 0.0 0.0 555.18 0.0 4.76 6 Sample01 -380.81 152.04 0.0 145.67 614.99 252.75 7.92 7 Sample01 -400.38 174.42 364.97 180.95 586.5 279.3 0.1 8 Sample01 -466.22 295.56 170.77 285.06 183.68 310.72 0.16 9 Sample01 -350.06 111.14 519.16 0.0 0.0 0.0 7.27 10 Sample01 -369.23 147.33 0.0 0.0 631.66 282.47 3.9 11 Sample01 -409.74 209.13 0.0 0.0 0.0 0.0 3.7 12 Sample01 -425.44 428.91 350.72 615.96 500.26 478.29 0.12 13 Sample01 -337.43 123.45 431.7 538.75 0.0 114.12 0.02 14 Sample01 -348.19 106.43 290.24 0.0 0.0 0.0 1.32 15 Sample01 -403.15 365.18 0.0 321.2 0.0 0.0 0.72 16 Sample01 -438.05 324.84 321.28 594.04 0.0 317.46 0.17 17 Sample01 -380.78 125.14 526.06 177.24 0.0 180.63 10.48 18 Sample01 -947.3 1005.61 446.06 514.03 412.83 450.29 0.96 19 Sample01 -564.8 892.55 0.0 0.0 0.0 0.0 4.35 21 Sample01 -666.51 454.64 369.55 0.0 0.0 241.11 3.2 22 Sample01 -601.97 997.64 234.58 333.96 283.79 305.79 3.92 23 Sample01 -650.11 361.04 386.73 0.0 0.0 0.0 1.44 24 Sample01 -648.85 1003.84 0.0 239.36 0.0 0.0 15.7 25 Sample01 -823.2 1013.04 211.5 279.26 260.44 233.45 5.36 26 Sample01 -476.64 216.34 377.33 193.69 240.82 0.0 0.05 1 Sample02 -471.05 266.57 360.02 0.0 199.15 475.21 0.33 2 Sample02 -359.23 107.35 511.79 0.0 632.87 473.39 9.37 3 Sample02 -336.76 151.5 0.0 0.0 0.0 512.43 4.35 4 Sample02 -317.03 152.51 0.0 0.0 0.0 172.25 0.22 5 Sample02 -371.85 156.31 0.0 0.0 594.77 279.72 1.58 6 Sample02 -402.12 171.98 0.0 237.43 620.27 364.06 4.41 7 Sample02 -368.32 108.49 514.9 236.18 641.3 500.82 12.41 8 Sample02 -409.74 195.75 0.0 294.04 0.0 580.59 2.38 9 Sample02 -378.44 132.1 0.0 0.0 600.97 570.17 9.4 11 Sample02 -426.29 523.22 0.0 0.0 0.0 197.13 0.73 12 Sample02 -401.36 127.3 516.01 406.73 613.97 296.09 11.26 13 Sample02 -400.4 219.68 190.49 78.26 125.95 176.58 0.08 15 Sample02 -423.89 256.67 0.0 514.7 0.0 187.2 0.5 16 Sample02 -497.74 393.88 292.7 475.7 352.66 342.82 0.18 17 Sample02 -420.84 217.2 284.35 211.29 245.95 275.71 0.46 18 Sample02 -884.52 972.3 440.63 466.03 477.16 487.96 1.54 19 Sample02 -423.88 161.22 0.0 448.14 577.34 274.5 0.4 20 Sample02 -581.05 892.1 0.0 0.0 233.34 0.0 3.91 21 Sample02 -674.41 425.53 389.33 0.0 236.75 288.13 3.64 22 Sample02 -624.32 1007.46 217.78 295.71 297.25 314.48 9.05 23 Sample02 -718.58 474.62 396.15 0.0 224.48 0.0 0.9 24 Sample02 -660.25 1000.27 0.0 237.39 243.98 0.0 14.01 25 Sample02 -807.85 1014.39 196.22 272.05 278.9 248.53 8.86 26 Sample02 -361.35 109.32 516.61 180.94 0.0 0.0 7.26 1 Sample03 -361.78 108.85 520.87 0.0 0.0 146.36 6.76 2 Sample03 -373.61 121.06 332.84 0.0 583.08 240.41 0.07 3 Sample03 -383.18 197.39 0.0 0.0 0.0 258.69 0.57 4 Sample03 -310.49 136.14 0.0 0.0 0.0 0.0 4.18 5 Sample03 -364.87 131.0 0.0 0.0 489.71 0.0 1.03 6 Sample03 -375.6 144.75 0.0 389.84 622.87 273.03 8.96 7 Sample03 -439.6 306.15 288.84 737.82 594.44 672.52 0.16 8 Sample03 -398.88 189.54 0.0 499.34 0.0 324.64 1.1 9 Sample03 -356.28 107.51 519.27 0.0 0.0 0.0 5.46 10 Sample03 -353.41 129.92 0.0 0.0 641.06 278.58 1.0 11 Sample03 -413.37 228.27 0.0 0.0 0.0 0.0 2.14 12 Sample03 -439.22 239.91 367.79 549.6 553.86 308.33 0.3 13 Sample03 -394.14 128.93 523.27 554.83 0.0 0.0 3.74 14 Sample03 -355.73 103.18 334.1 0.0 0.0 0.0 1.41 15 Sample03 -396.87 187.11 0.0 467.44 0.0 0.0 2.68 16 Sample03 -431.47 165.27 520.82 580.86 0.0 230.86 2.53 17 Sample03 -376.04 115.81 519.43 197.15 0.0 151.84 7.61 18 Sample03 -932.7 989.79 437.87 562.38 426.21 406.49 0.88 19 Sample03 -394.2 149.29 0.0 589.44 603.93 0.0 4.68 20 Sample03 -585.23 908.66 0.0 0.0 0.0 0.0 3.98 21 Sample03 -643.85 436.66 357.78 0.0 0.0 212.63 2.51 22 Sample03 -584.99 986.35 270.28 378.3 0.0 317.27 3.28 23 Sample03 -689.54 462.88 376.1 0.0 0.0 0.0 1.69 24 Sample03 -659.74 1006.36 0.0 251.84 0.0 0.0 20.39 25 Sample03 -836.45 1013.12 214.98 295.93 264.33 0.0 5.62 26 Sample03 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/out2.flowstat3 --- a/cross_sample/test-data/out2.flowstat3 Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,27 +0,0 @@ -Population FSC_mean FSC_median FSC_stdev SSC_mean SSC_median SSC_stdev CD4_mean CD4_median CD4_stdev CCR7_mean CCR7_median CCR7_stdev CD8_mean CD8_median CD8_stdev CCR3_mean CCR3_median CCR3_stdev Percentage_mean Percentage_median Percentage_stdev -1 397.92 361.35 68.23 146.71 114.46 60.36 471.09 516.61 81.21 181.15 180.94 12.44 80.27 0.0 139.04 0.0 0.0 0.0 5.27 7.26 4.56 -2 399.86 366.75 61.7 164.64 118.51 88.4 467.93 520.87 93.46 0.0 0.0 0.0 66.38 0.0 114.98 264.98 173.37 182.56 5.69 6.76 4.92 -3 376.11 373.61 18.25 125.75 121.06 21.14 412.53 392.96 91.07 0.0 0.0 0.0 600.96 586.93 27.7 319.24 243.93 133.51 3.19 0.12 5.35 -4 358.25 354.8 23.4 180.18 191.64 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 348.99 275.85 141.8 1.86 0.67 2.15 -5 315.6 317.03 4.57 142.39 138.51 8.85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 57.42 0.0 99.45 3.18 4.18 2.61 -6 366.46 364.87 4.8 143.66 143.68 12.66 0.0 0.0 0.0 0.0 0.0 0.0 546.55 555.18 53.06 93.24 0.0 161.5 2.46 1.58 2.01 -7 386.18 380.81 14.05 156.26 152.04 14.1 0.0 0.0 0.0 257.65 237.43 123.33 619.38 620.27 4.02 296.61 273.03 59.28 7.1 7.92 2.38 -8 402.77 400.38 35.7 196.35 174.42 100.64 389.57 364.97 115.02 384.98 236.18 306.81 607.41 594.44 29.61 484.21 500.82 197.14 4.22 0.16 7.09 -9 424.95 409.74 36.15 226.95 195.75 59.5 56.92 0.0 98.59 359.48 294.04 121.21 61.23 0.0 106.05 405.32 324.64 151.95 1.21 1.1 1.11 -10 353.17 353.17 4.4 109.32 109.32 2.57 519.21 519.21 0.08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.36 6.36 1.28 -11 367.03 369.23 12.66 136.45 132.1 9.49 0.0 0.0 0.0 0.0 0.0 0.0 624.56 631.66 20.97 377.07 282.47 167.24 4.77 3.9 4.27 -12 416.47 413.37 8.7 320.21 228.27 176.07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 65.71 0.0 113.81 2.19 2.14 1.49 -13 422.01 425.44 19.16 265.37 239.91 152.41 411.51 367.79 90.9 524.1 549.6 106.92 556.03 553.86 56.89 360.9 308.33 101.84 3.89 0.3 6.38 -14 365.78 365.78 40.1 126.19 126.19 3.87 477.48 477.48 64.75 546.79 546.79 11.37 0.0 0.0 0.0 57.06 57.06 80.7 1.88 1.88 2.63 -15 368.11 355.73 28.22 143.1 106.43 66.34 271.61 290.24 73.6 26.09 0.0 45.18 41.98 0.0 72.72 58.86 0.0 101.95 0.94 1.32 0.74 -16 407.97 403.15 14.14 269.65 256.67 89.74 0.0 0.0 0.0 434.45 467.44 100.88 0.0 0.0 0.0 62.4 0.0 108.08 1.3 0.72 1.2 -17 455.75 438.05 36.51 294.66 324.84 117.25 378.27 321.28 124.28 550.2 580.86 64.85 117.55 0.0 203.61 297.05 317.46 58.71 0.96 0.18 1.36 -18 392.55 380.78 24.61 152.72 125.14 56.04 443.28 519.43 137.68 195.23 197.15 17.11 81.98 0.0 142.0 202.73 180.63 64.82 6.18 7.61 5.16 -19 921.51 932.7 32.85 989.23 989.79 16.66 441.52 440.63 4.17 514.15 514.03 48.18 438.73 426.21 33.94 448.25 450.29 40.77 1.13 0.96 0.36 -20 409.04 409.04 20.99 155.26 155.26 8.44 0.0 0.0 0.0 518.79 518.79 99.91 590.64 590.64 18.8 137.25 137.25 194.1 2.54 2.54 3.03 -21 577.03 581.05 10.79 897.77 892.55 9.43 0.0 0.0 0.0 0.0 0.0 0.0 77.78 0.0 134.72 0.0 0.0 0.0 4.08 3.98 0.24 -22 661.59 666.51 15.86 438.94 436.66 14.69 372.22 369.55 15.94 0.0 0.0 0.0 78.92 0.0 136.69 247.29 241.11 38.13 3.12 3.2 0.57 -23 603.76 601.97 19.73 997.15 997.64 10.56 240.88 234.58 26.81 335.99 333.96 41.33 193.68 283.79 167.87 312.51 314.48 5.99 5.42 3.92 3.16 -24 686.08 689.54 34.37 432.85 462.88 62.46 386.33 386.73 10.03 0.0 0.0 0.0 74.83 0.0 129.6 0.0 0.0 0.0 1.34 1.44 0.4 -25 656.28 659.74 6.44 1003.49 1003.84 3.06 0.0 0.0 0.0 242.86 239.36 7.84 81.33 0.0 140.86 0.0 0.0 0.0 16.7 15.7 3.31 -26 822.5 823.2 14.31 1013.52 1013.12 0.76 207.57 211.5 9.98 282.41 279.26 12.25 267.89 264.33 9.73 160.66 233.45 139.34 6.61 5.62 1.95 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run1/input1.flowtext.flowclr --- a/cross_sample/test-data/run1/input1.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
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-694\t370\t391\t254\t311\t330\t22\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run1/input2.flowtext.flowclr --- a/cross_sample/test-data/run1/input2.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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|
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run1/input3.flowtext.flowclr --- a/cross_sample/test-data/run1/input3.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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t1\n-385\t99\t179\t115\t513\t163\t6\n-400\t157\t126\t99\t638\t313\t6\n-431\t97\t527\t156\t122\t197\t18\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run2/input1.flowtext.flowclr --- a/cross_sample/test-data/run2/input1.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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-694\t370\t391\t254\t311\t330\t22\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run2/input2.flowtext.flowclr --- a/cross_sample/test-data/run2/input2.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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t223\t65\t352\t600\t403\t7\n-1014\t1023\t410\t349\t265\t286\t26\n-656\t1023\t149\t307\t240\t175\t25\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 cross_sample/test-data/run2/input3.flowtext.flowclr --- a/cross_sample/test-data/run2/input3.flowtext.flowclr Mon Feb 27 13:29:54 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| b'@@ -1,20001 +0,0 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n-385\t99\t179\t115\t513\t163\t6\n-400\t157\t126\t99\t638\t313\t7\n-431\t97\t527\t156\t122\t197\t18\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 runCrossSample.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runCrossSample.py Wed Jul 29 13:32:17 2020 -0400 |
| [ |
| b'@@ -0,0 +1,216 @@\n+#!/usr/bin/env python\n+######################################################################\n+# Copyright (c) 2016 Northrop Grumman.\n+# All rights reserved.\n+######################################################################\n+import sys\n+import os\n+from scipy.stats import gmean\n+from argparse import ArgumentParser\n+from collections import defaultdict\n+import pandas as pd\n+\n+#\n+# version 1.1 -- April 2016 -- C. Thomas\n+# modified to read in several input files and output to a directory\n+# + generates summary statistics\n+# also checks before running that input files are consistent with centroid file\n+#\n+\n+\n+def compare_MFIs(input_files, f_names, mfi_file):\n+ header_MFIs = ""\n+ flag_error = False\n+ with open(mfi_file, "r") as mfi_check:\n+ mfi_fl = mfi_check.readline().split("\\t")\n+ header_MFIs = "\\t".join([mfi_fl[h] for h in range(1, len(mfi_fl))])\n+\n+ for hh, files in enumerate(input_files):\n+ with open(files, "r") as inf:\n+ hdrs = inf.readline()\n+ if hdrs != header_MFIs:\n+ sys.stderr.write(hdrs + "headers in " + f_names[hh] + " are not consistent with FLOCK centroid file:\\n" + header_MFIs + "\\n")\n+ flag_error = True\n+ if flag_error:\n+ sys.exit(2)\n+\n+\n+def stats_MFIs(cs_df, ctr, mfi_calc):\n+ if mfi_calc == "mfi":\n+ MFIs = cs_df.groupby(\'Population\').mean().round(decimals=2)\n+ elif mfi_calc == "gmfi":\n+ MFIs = cs_df.groupby(\'Population\').agg(lambda x: gmean(list(x))).round(decimals=2)\n+ else:\n+ MFIs = cs_df.groupby(\'Population\').median().round(decimals=2)\n+ pop_freq = (cs_df.Population.value_counts(normalize=True) * 100).round(decimals=2)\n+ sorted_pop_freq = pop_freq.sort_index()\n+ MFIs[\'Percentage\'] = sorted_pop_freq\n+ MFIs[\'Population\'] = MFIs.index\n+ MFIs[\'SampleName\'] = "".join(["Sample", str(ctr).zfill(2)])\n+ return MFIs\n+\n+\n+def get_pop_prop(input_files, summary_stat, mfi_stats, marker_names, mfi_calc):\n+ pop_count = defaultdict(dict)\n+ mrk = marker_names.strip().split("\\t")\n+ markers = "\\t".join([mrk[m] for m in range(1, len(mrk))])\n+\n+ ctr_mfi = 0\n+ nb_pop = 0\n+ tot = {}\n+ with open(mfi_stats, "a") as mfis:\n+ mfis.write("\\t".join([markers, "Percentage", "Population", "SampleName"]) + "\\n")\n+ for files in input_files:\n+ cs = pd.read_table(files)\n+ tot[files] = len(cs.index)\n+ for pops in cs.Population:\n+ if pops in pop_count[files]:\n+ pop_count[files][pops] += 1\n+ else:\n+ pop_count[files][pops] = 1\n+ max_nb_pop = max(set(cs.Population))\n+ if (max_nb_pop > nb_pop):\n+ nb_pop = max_nb_pop\n+ ctr_mfi += 1\n+ cs_stats = stats_MFIs(cs, ctr_mfi, mfi_calc)\n+ cs_stats.to_csv(mfis, sep="\\t", header=False, index=False)\n+\n+ ctr = 0\n+ with open(summary_stat, "w") as outf:\n+ itpop = [str(x) for x in range(1, nb_pop + 1)]\n+ cols = "\\t".join(itpop)\n+ outf.write("FileID\\tSampleName\\t" + cols + "\\n")\n+ for eachfile in pop_count:\n+ tmp = []\n+ for num in range(1, nb_pop + 1):\n+ if num not in pop_count[eachfile]:\n+ pop_count[eachfile][num] = 0\n+ tmp.append(str((pop_count[eachfile][num] / float(tot[eachfile])) * 100))\n+ props = "\\t".join(tmp)\n+ ctr += 1\n+ sample_name = "".join(["Sample", str(ctr).zfill(2)])\n+ outf.write("\\t".join([input_files[eachfile], sample_name, props]) + "\\n")\n+\n+\n+def run_cross_sample(input_files, f_names, mfi_file, output_dir, summary_stat,\n+ mfi_stats, mfi_calc):\n+ markers = ""\n+ # Strip off Header Line\n+ with open(mfi_file, "r") as mfi_in, open("mfi.txt", "w") as mfi_out:\n+ markers = mfi_in.readline().strip("\\n")\n+ for line '..b'mfi_in:\n+ mfi_out.write(line)\n+\n+ # Create output directory\n+ if not os.path.exists(output_dir):\n+ os.makedirs(output_dir)\n+\n+ outputs = {}\n+ # Run cent_adjust\n+ for nm, flow_file in enumerate(input_files):\n+ run_command = "cent_adjust mfi.txt " + flow_file\n+ print(run_command)\n+ os.system(run_command)\n+ flow_name = os.path.split(flow_file)[1]\n+ outfile = os.path.join(output_dir, f_names[nm] + ".flowclr")\n+ outputs[outfile] = f_names[nm]\n+ with open(flow_file, "r") as flowf, open("population_id.txt", "r") as popf, open(outfile, "w") as outf:\n+ f_line = flowf.readline()\n+ f_line = f_line.rstrip()\n+ f_line = f_line + "\\tPopulation\\n"\n+ outf.write(f_line)\n+\n+ for line in flowf:\n+ line = line.rstrip()\n+ pop_line = popf.readline()\n+ pop_line = pop_line.rstrip()\n+ line = line + "\\t" + pop_line + "\\n"\n+ outf.write(line)\n+ get_pop_prop(outputs, summary_stat, mfi_stats, markers, mfi_calc)\n+ return\n+\n+\n+def generate_CS_stats(mfi_stats, all_stats):\n+ df = pd.read_table(mfi_stats)\n+ means = df.groupby(\'Population\').mean().round(decimals=2)\n+ medians = df.groupby(\'Population\').median().round(decimals=2)\n+ stdev = df.groupby(\'Population\').std().round(decimals=2)\n+ all_markers = []\n+ with open(mfi_stats, "r") as ms:\n+ ms_fl = ms.readline().strip()\n+ all_markers = ms_fl.split("\\t")[0:-2]\n+\n+ with open(all_stats, "w") as mstats:\n+ hdgs = ["\\t".join(["_".join([mrs, "mean"]), "_".join([mrs, "median"]), "_".join([mrs, "stdev"])]) for mrs in all_markers]\n+ mstats.write("Population\\t")\n+ mstats.write("\\t".join(hdgs) + "\\n")\n+ for pops in set(df.Population):\n+ tmp_line = []\n+ for mar in all_markers:\n+ tmp_line.append("\\t".join([str(means.loc[pops, mar]), str(medians.loc[pops, mar]), str(stdev.loc[pops, mar])]))\n+ mstats.write(str(pops) + "\\t")\n+ mstats.write("\\t".join(tmp_line) + "\\n")\n+\n+\n+if __name__ == "__main__":\n+ parser = ArgumentParser(\n+ prog="runCrossSample",\n+ description="Run CrossSample on Flow file")\n+\n+ parser.add_argument(\n+ \'-i\',\n+ dest="input_files",\n+ required=True,\n+ action=\'append\',\n+ help="File locations for flow text files.")\n+\n+ parser.add_argument(\n+ \'-n\',\n+ dest="filenames",\n+ required=True,\n+ action=\'append\',\n+ help="Filenames")\n+\n+ parser.add_argument(\n+ \'-m\',\n+ dest="mfi",\n+ required=True,\n+ help="File location for the MFI text file.")\n+\n+ parser.add_argument(\n+ \'-o\',\n+ dest="out_path",\n+ required=True,\n+ help="Path to the directory for the output files.")\n+\n+ parser.add_argument(\n+ \'-M\',\n+ dest="mfi_calc",\n+ required=True,\n+ help="what to calculate for centroids.")\n+\n+ parser.add_argument(\n+ \'-s\',\n+ dest="sstat",\n+ required=True,\n+ help="File location for the summary statistics.")\n+\n+ parser.add_argument(\n+ \'-S\',\n+ dest="mfi_stat",\n+ required=True,\n+ help="File location for the MFI summary statistics.")\n+\n+ parser.add_argument(\n+ \'-a\',\n+ dest="all_stats",\n+ required=True,\n+ help="File location for stats on all markers.")\n+\n+ args = parser.parse_args()\n+\n+ input_files = [f for f in args.input_files]\n+ input_names = [n for n in args.filenames]\n+ compare_MFIs(input_files, input_names, args.mfi)\n+ run_cross_sample(input_files, input_names, args.mfi, args.out_path, args.sstat, args.mfi_stat, args.mfi_calc)\n+ generate_CS_stats(args.mfi_stat, args.all_stats)\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 runCrossSample.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/runCrossSample.xml Wed Jul 29 13:32:17 2020 -0400 |
| [ |
| b'@@ -0,0 +1,148 @@\n+<tool id="run_cross_sample" name="Run Cross Sample" version="1.1+galaxy0" profile="18.01">\n+ <description>using a Flow file that was run using FLOCK</description>\n+ <requirements>\n+ <requirement type="package" version="1.5.1">scipy</requirement>\n+ <requirement type="package" version="1.0.5">pandas</requirement>\n+ <requirement type="package" version="1.0">flock</requirement>\n+ </requirements>\n+ <stdio>\n+ <exit_code range="1" level="fatal"/>\n+ <exit_code range="2" level="fatal" description="Columns inconsistencies between files and centroids file. See stderr for more details." />\n+ <exit_code range="3:" level="fatal"/>\n+ </stdio>\n+ <command><![CDATA[\n+ python \'$__tool_directory__/runCrossSample.py\' -m \'${centroid}\' -s \'${stats}\' -S \'${mfistats}\' -a \'${allstats}\' -M \'${mfi}\' -o crossSampleOutputs\n+ #for $f in $input\n+ -i $f\n+ -n \'${f.name}\'\n+ #end for\n+\n+ ]]>\n+ </command>\n+ <inputs>\n+ <param format="flowtext" name="input" type="data_collection" collection_type="list" label="Flowtext files Collection"/>\n+ <param format="flowmfi" name="centroid" type="data" label="Centroid file"/>\n+ <param name="mfi" type="select" label="Calculate centroids using:">\n+ <option value="mfi" selected="true">Mean Fluorescence Intensity</option>\n+ <option value="mdfi">Median Fluorescence Intensity</option>\n+ <option value="gmfi">Geometric Mean Fluorescence Intensity</option>\n+ </param>\n+ </inputs>\n+ <outputs>\n+ <collection type="list" label="CrossSample on ${input.name}" name="output">\n+ <discover_datasets pattern="(?P<name>.*)" directory="crossSampleOutputs" format="flowclr" />\n+ </collection>\n+ <data format="flowstat1" name="stats" label="Population distribution after CrossSample on ${input.name} using ${mfi}"/>\n+ <data format="flowstat2" name="mfistats" label="${mfi} centroids of CrossSample on ${input.name} using ${mfi}"/>\n+ <data format="flowstat3" name="allstats" label="${mfi} descriptive stats of CrossSample on ${input.name} using ${mfi}"/>\n+ </outputs>\n+ <tests>\n+ <test>\n+ <param name="input">\n+ <collection type="list">\n+ <element name="input1" value="input1.flowtext"/>\n+ <element name="input2" value="input2.flowtext"/>\n+ <element name="input3" value="input3.flowtext"/>\n+ </collection>\n+ </param>\n+ <param name="centroid" value="mfi.flowmfi"/>\n+ <param name="mfi" value="mfi"/>\n+ <output name="stats" file="out1.flowstat1" lines_diff="6"/>\n+ <output name="allstats" file="out1.flowstat3"/>\n+ <output name="mfistats" file="out1.flowstat2" compare="sim_size"/>\n+ <output_collection name="output" count="3">\n+ <element name="input1.flowtext.flowclr" file="run1/input1.flowtext.flowclr" compare="sim_size"/>\n+ <element name="input2.flowtext.flowclr" file="run1/input2.flowtext.flowclr" compare="sim_size"/>\n+ <element name="input3.flowtext.flowclr" file="run1/input3.flowtext.flowclr" compare="sim_size"/>\n+ </output_collection>\n+ </test>\n+ <test>\n+ <param name="input">\n+ <collection type="list">\n+ <element name="input1" value="input1.flowtext"/>\n+ <element name="input2" value="input2.flowtext"/>\n+ <element name="input3" value="input3.flowtext"/>\n+ </collection>\n+ </param>\n+ <param name="centroid" value="gmfi.flowmfi"/>\n+ <param name="mfi" value="gmfi"/>\n+ <output name="stats" file="out2.flowstat1" lines_diff="6"/>\n+ <output name="allstats" file="out2.flowstat3"/>\n+ <output name="mfistats" file="out2.flowstat2" compare="sim_size"/>\n+ <output_collection name="output" type="list" count="3">\n+ <element name="input1.flowtext.flowclr" file="run2/input1.flowtext.flowclr"/>\n+ <element name="input2.flowtext.flowclr" file="run2/input2.flowtext.flowclr"/>\n+ <element name="input3.flowtext.flowclr" file="run2/input3.flowtext.flowclr"/>\n+ </o'..b' the attribution of events to populations.\n+.. class:: infomark\n+The option chosen for the centroids (mean, median or geometric mean) should be the same as used to run FLOCK.\n+**Output**\n+Each event within each file of a dataset collection is attributed to a population depending on its intensity profile.\n+A table of the population composition of each file is generated as well as MFI and population descriptive statistics.\n+.. class:: infomark\n+Tip: If headers in each text-converted FCS file do not match those in the centroid file, the program will not run. Edit the input file using the Remove, rearrange and/or rename columns tool in the Flow Text File Tools section prior to Cross Sample analysis.\n+-----\n+**Example**\n+*Input* - fluorescence intensities per marker per event::\n+ Marker1 Marker2 Marker3 ...\n+ 33 47 11 ...\n+ 31 64 11 ...\n+ 21 62 99 ...\n+ 14 34 60 ...\n+ ... ... ... ...\n+*Centroid file* - mean, geometric mean or median fluorescence intensity per marker per population::\n+ Population Marker1 Marker2 Marker3 ...\n+ 1 38 49 10 ...\n+ 2 21 63 100 ...\n+ 3 31 52 45 ...\n+ 4 11 78 25 ...\n+ ... ... ... ... ...\n+*Output* for each text file - fluorescence intensities per marker and population ID per event::\n+ Marker1 Marker2 Marker3 ... Population\n+ 33 47 11 ... 1\n+ 31 64 11 ... 6\n+ 21 62 99 ... 2\n+ 14 34 60 ... 7\n+ ... ... ... ... ...\n+*Summary table* - distribution of events in each population in each file::\n+ Filename SampleName Pop1 Pop2 Pop3 ...\n+ File1 Biosample1 0.1 0.25 0.14 ...\n+ File2 Biosample2 0.02 0.1 0.17 ...\n+ File3 Biosample3 0.4 0.05 0.21 ...\n+ File4 Biosample4 0.05 0.3 0.08 ...\n+*Centroid MFI Summary table* - for each file, mean, median or geometric mean fluorescence intensities per marker per population::\n+ Marker1 Marker2 Marker3 ... Population Percentage SampleName\n+ 154 885 24 ... 1 0.2 Biosample1\n+ 458 74 574 ... 2 0.3 Biosample1\n+ 3 210 86 ... 3 0.05 Biosample1\n+ ... ... ... ... ... ... ...\n+ 140 921 19 ... 1 0.1 Biosample2\n+ 428 79 508 ... 2 0.25 Biosample2\n+ 9 225 90 ... 3 0.3 Biosample2\n+ ... ... ... ... ... ... ...\n+*MFI Descriptive Statistics table* - for the set of files, mean, median and standard deviation of each centroid per marker per population, as well as mean, median and standard deviation of the population\'s proportion::\n+ Population Marker1_mean Marker1_median Marker1_stdev ... Percentage_mean Percentage_median Percentage_stdev\n+ 1 94.65 90.86 25.8 ... 1.84 0.55 2.48\n+ 2 132.18 131.58 5.02 ... 9.89 9.76 0.33\n+ 3 71.8 69.68 10.53 ... 3.02 1.49 3.45\n+ 4 84.85 84.85 nan ... 8.52 8.52 nan\n+ 5 161.82 132.77 61.29 ... 0.95 0.37 1.06\n+ ... ... ... ... ... ... ... ...\n+ ]]>\n+ </help>\n+ <citations>\n+ <citation type="doi">10.1002/cyto.b.20554</citation>\n+ <citation type="doi">10.3389/fimmu.2012.00302</citation>\n+ <citation type="doi">10.1371/journal.pone.0038408</citation>\n+ <citation type="doi">10.1371/journal.ppat.1003076</citation>\n+ <citation type="doi">10.1016/j.clim.2012.12.003</citation>\n+ <citation type="doi">10.1038/srep02327</citation>\n+ </citations>\n+</tool>\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/gmfi.flowmfi --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gmfi.flowmfi Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,27 @@ +Population FSC SSC CD4 CCR7 CD8 CCR3 +1 362 113 519 182 0 0 +2 368 116 522 0 0 181 +3 360 108 511 0 630 472 +4 354 165 0 0 0 514 +5 329 149 0 0 0 0 +6 370 147 0 0 606 0 +7 386 154 0 310 617 294 +8 369 109 516 249 640 500 +9 413 200 0 403 0 633 +10 354 110 520 0 0 0 +11 379 133 0 0 602 562 +12 405 242 0 0 0 0 +13 402 129 514 409 613 294 +14 399 133 525 560 0 0 +15 353 109 318 0 0 0 +16 407 214 0 502 0 0 +17 432 180 503 592 0 279 +18 381 123 523 192 0 187 +19 935 946 478 612 513 523 +20 390 148 0 528 618 0 +21 614 972 0 0 0 0 +22 662 422 382 0 0 276 +23 538 965 362 471 382 442 +24 677 427 387 0 0 0 +25 724 1012 0 265 0 0 +26 957 963 311 337 0 300 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/input1.flowtext --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input1.flowtext Wed Jul 29 13:32:17 2020 -0400 |
| b |
| b'@@ -0,0 +1,20001 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| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/input2.flowtext --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input2.flowtext Wed Jul 29 13:32:17 2020 -0400 |
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' 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| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/input3.flowtext --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input3.flowtext Wed Jul 29 13:32:17 2020 -0400 |
| b |
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\t197\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/mfi.flowmfi --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/mfi.flowmfi Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,27 @@ +Population FSC SSC CD4 CCR7 CD8 CCR3 +1 364 117 520 188 53 71 +2 370 121 523 37 67 187 +3 363 112 512 69 632 482 +4 360 175 84 141 114 520 +5 333 156 56 68 82 109 +6 372 154 75 90 608 251 +7 389 162 83 318 620 304 +8 371 113 517 258 642 505 +9 422 214 138 420 327 640 +10 356 114 521 31 59 68 +11 381 138 85 111 604 565 +12 410 276 119 213 183 161 +13 406 137 516 424 615 305 +14 401 139 526 566 76 108 +15 356 121 322 84 75 110 +16 414 239 104 510 138 200 +17 440 198 509 598 119 289 +18 384 128 524 199 60 194 +19 944 966 490 629 533 541 +20 393 154 82 534 621 267 +21 617 975 145 224 225 173 +22 667 437 389 204 215 283 +23 546 975 366 476 387 448 +24 681 441 392 204 187 132 +25 727 1013 182 269 259 232 +26 960 975 325 343 318 311 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out1.flowstat1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out1.flowstat1 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,4 @@ +FileID SampleName 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 +Rearranged FCSGateTrans on FCS2.003.20Kevents.fcs Sample01 10.18 8.555 0.15 0.42 6.67 13.3 2.985 0.0 0.05 9.825 0.0 3.265 0.065 0.045 1.42 0.495 0.125 7.6 0.465 0.03 14.78 2.36 1.385 2.205 12.0 1.625 +Rearranged FCSGateTrans on FCS2.001.20Kevents.fcs Sample02 0.0 0.41 10.735 5.69 0.275 3.075 2.945 11.245 1.285 0.0 8.975 1.04 10.935 0.085 0.25 0.585 0.015 0.225 0.71 0.535 12.76 2.775 2.01 1.585 19.97 1.885 +Rearranged FCSGateTrans on FCS2.002.20Kevents.fcs Sample03 8.88 5.35 0.05 0.35 4.615 1.945 5.765 0.015 0.18 7.445 0.105 3.0 0.195 4.695 1.61 3.165 1.515 5.28 0.485 7.53 16.55 1.525 1.77 2.64 13.6 1.74 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out1.flowstat2 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out1.flowstat2 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,75 @@ +FSC SSC CD4 CCR7 CD8 CCR3 Percentage Population SampleName +360.93 118.98 521.7 181.35 48.29 76.01 10.18 1 Sample01 +374.35 125.62 525.73 36.34 72.0 197.55 8.56 2 Sample01 +406.43 162.77 410.23 69.3 580.9 275.2 0.15 3 Sample01 +363.32 241.5 99.96 145.89 102.85 318.19 0.42 4 Sample01 +337.09 155.29 56.73 72.34 73.35 112.25 6.67 5 Sample01 +368.59 148.89 69.12 71.72 606.04 237.42 13.3 6 Sample01 +403.26 176.59 94.75 191.8 620.71 263.77 2.99 7 Sample01 +345.6 331.2 292.9 562.5 293.0 413.3 0.05 9 Sample01 +352.15 115.59 519.81 29.88 54.23 72.34 9.83 10 Sample01 +421.38 241.15 115.02 151.72 215.97 147.47 3.26 12 Sample01 +423.08 219.69 405.31 271.0 585.31 272.92 0.06 13 Sample01 +363.33 188.33 360.33 551.11 77.56 198.67 0.04 14 Sample01 +354.94 118.97 301.48 81.19 69.35 117.17 1.42 15 Sample01 +419.08 392.96 120.34 408.48 122.57 168.11 0.5 16 Sample01 +462.0 422.08 421.0 718.08 357.2 480.84 0.12 17 Sample01 +388.6 132.42 528.82 190.06 61.71 205.87 7.6 18 Sample01 +972.67 1014.57 510.17 649.92 498.02 507.34 0.46 19 Sample01 +436.0 331.33 195.33 587.83 698.0 475.67 0.03 20 Sample01 +602.71 967.97 140.02 220.24 213.08 176.15 14.78 21 Sample01 +687.46 490.61 376.69 209.82 192.18 267.15 2.36 22 Sample01 +551.76 988.58 350.25 482.74 364.3 411.62 1.38 23 Sample01 +647.77 387.0 397.71 171.63 167.15 143.66 2.2 24 Sample01 +726.15 1016.99 182.62 264.79 248.23 217.08 12.0 25 Sample01 +969.6 1001.4 329.26 338.48 302.98 333.58 1.62 26 Sample01 +562.4 279.96 395.37 85.56 233.15 423.82 0.41 2 Sample02 +361.23 110.65 512.56 68.97 633.79 483.67 10.74 3 Sample02 +355.26 167.2 73.65 147.5 108.78 528.01 5.69 4 Sample02 +320.36 182.36 72.15 113.6 120.13 198.04 0.27 5 Sample02 +379.34 171.32 79.2 96.05 612.98 319.43 3.08 6 Sample02 +412.99 178.92 94.51 281.53 623.12 374.9 2.94 7 Sample02 +371.1 112.03 517.49 259.35 642.93 505.5 11.24 8 Sample02 +431.34 213.62 133.05 402.6 302.14 645.66 1.28 9 Sample02 +380.63 135.19 85.29 110.18 602.83 574.54 8.98 11 Sample02 +447.85 525.88 156.54 205.92 232.46 226.29 1.04 12 Sample02 +406.39 136.68 517.93 421.76 615.38 306.36 10.94 13 Sample02 +358.41 103.0 460.06 676.12 556.82 164.12 0.08 14 Sample02 +392.88 210.34 274.9 177.9 205.7 226.14 0.25 15 Sample02 +433.7 264.01 107.35 510.36 149.73 224.73 0.58 16 Sample02 +503.0 183.67 512.67 468.67 380.67 284.33 0.02 17 Sample02 +401.98 199.4 351.38 197.56 325.84 376.18 0.22 18 Sample02 +942.13 977.37 496.89 556.9 589.52 591.96 0.71 19 Sample02 +431.49 175.21 108.33 449.62 589.52 338.83 0.53 20 Sample02 +612.66 962.76 145.03 218.29 241.58 218.2 12.76 21 Sample02 +677.97 432.92 394.9 215.63 253.73 320.89 2.78 22 Sample02 +537.34 987.67 377.09 463.75 412.69 504.01 2.01 23 Sample02 +708.86 473.52 400.38 219.62 227.69 178.15 1.58 24 Sample02 +724.21 1017.01 183.89 263.49 272.15 249.63 19.97 25 Sample02 +956.13 990.81 316.68 335.77 337.56 324.8 1.88 26 Sample02 +363.8 112.35 518.73 192.67 50.31 67.22 8.88 1 Sample03 +366.09 113.61 521.93 39.72 65.66 165.65 5.35 2 Sample03 +395.7 119.3 384.9 85.9 585.6 230.1 0.05 3 Sample03 +368.81 204.11 96.34 237.57 86.57 292.1 0.35 4 Sample03 +318.7 144.37 48.23 81.95 64.63 103.98 4.61 5 Sample03 +356.9 131.76 69.32 144.68 612.42 244.06 1.94 6 Sample03 +373.64 149.87 73.71 364.36 620.22 274.19 5.76 7 Sample03 +438.0 176.67 517.33 423.0 277.33 437.33 0.02 8 Sample03 +456.47 368.94 339.69 764.56 534.31 616.89 0.18 9 Sample03 +358.85 110.16 520.35 32.56 61.77 62.47 7.44 10 Sample03 +395.52 201.76 124.0 438.24 675.14 501.81 0.1 11 Sample03 +416.9 238.72 120.19 236.94 187.78 133.18 3.0 12 Sample03 +435.72 195.44 389.95 550.08 631.15 289.23 0.19 13 Sample03 +399.59 136.41 525.52 567.5 69.54 112.64 4.7 14 Sample03 +361.32 113.15 344.56 109.52 71.4 102.73 1.61 15 Sample03 +404.76 196.99 95.3 519.01 130.48 201.21 3.16 16 Sample03 +451.65 196.73 522.28 588.96 117.05 274.0 1.52 17 Sample03 +383.32 121.85 525.67 214.77 64.27 172.42 5.28 18 Sample03 +948.66 979.64 477.21 676.02 492.0 476.04 0.48 19 Sample03 +393.34 154.15 80.74 547.09 620.52 265.14 7.53 20 Sample03 +618.74 977.0 140.47 231.46 216.74 155.22 16.55 21 Sample03 +632.72 434.17 361.81 253.26 192.78 244.91 1.52 22 Sample03 +550.94 992.18 358.59 469.96 366.78 399.73 1.77 23 Sample03 +692.69 478.12 385.77 214.78 183.47 124.04 2.64 24 Sample03 +733.01 1018.55 182.18 275.33 250.23 190.78 13.6 25 Sample03 +971.95 998.45 334.9 363.32 315.39 269.99 1.74 26 Sample03 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out1.flowstat3 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out1.flowstat3 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,27 @@ +Population FSC_mean FSC_median FSC_stdev SSC_mean SSC_median SSC_stdev CD4_mean CD4_median CD4_stdev CCR7_mean CCR7_median CCR7_stdev CD8_mean CD8_median CD8_stdev CCR3_mean CCR3_median CCR3_stdev Percentage_mean Percentage_median Percentage_stdev +1 362.36 362.36 2.03 115.66 115.66 4.69 520.22 520.22 2.1 187.01 187.01 8.0 49.3 49.3 1.43 71.62 71.62 6.22 9.53 9.53 0.92 +2 434.28 374.35 111.03 173.06 125.62 92.77 481.01 521.93 74.19 53.87 39.72 27.49 123.6 72.0 94.92 262.34 197.55 140.75 4.77 5.35 4.11 +3 387.79 395.7 23.62 130.91 119.3 27.93 435.9 410.23 67.59 74.72 69.3 9.68 600.1 585.6 29.27 329.66 275.2 135.27 3.65 0.15 6.14 +4 362.46 363.32 6.82 204.27 204.11 37.15 89.98 96.34 14.26 176.99 147.5 52.47 99.4 102.85 11.5 379.43 318.19 129.33 2.15 0.42 3.06 +5 325.38 320.36 10.17 160.67 155.29 19.56 59.04 56.73 12.13 89.3 81.95 21.59 86.04 73.35 29.85 138.09 112.25 52.08 3.85 4.61 3.27 +6 368.28 368.59 11.22 150.66 148.89 19.84 72.55 69.32 5.76 104.15 96.05 37.15 610.48 612.42 3.86 266.97 244.06 45.55 6.11 3.08 6.26 +7 396.63 403.26 20.5 168.46 176.59 16.14 87.66 94.51 12.08 279.23 281.53 86.3 621.35 620.71 1.55 304.29 274.19 61.37 3.9 2.99 1.61 +8 404.55 404.55 47.31 144.35 144.35 45.71 517.41 517.41 0.11 341.18 341.18 115.72 460.13 460.13 258.52 471.42 471.42 48.2 5.63 5.63 7.93 +9 411.14 431.34 58.13 304.59 331.2 81.01 255.21 292.9 108.35 576.55 562.5 181.39 376.48 302.14 136.76 558.62 616.89 126.67 0.5 0.18 0.68 +10 355.5 355.5 4.74 112.88 112.88 3.84 520.08 520.08 0.38 31.22 31.22 1.9 58.0 58.0 5.33 67.4 67.4 6.98 8.64 8.64 1.69 +11 388.08 388.08 10.53 168.48 168.48 47.07 104.65 104.65 27.37 274.21 274.21 231.97 638.98 638.98 51.13 538.17 538.17 51.43 4.54 4.54 6.28 +12 428.71 421.38 16.73 335.25 241.15 165.09 130.58 120.19 22.63 198.19 205.92 43.13 212.07 215.97 22.59 168.98 147.47 50.14 2.43 3.0 1.21 +13 421.73 423.08 14.71 183.94 195.44 42.68 437.73 405.31 69.88 414.28 421.76 139.69 610.61 615.38 23.29 289.5 289.23 16.72 3.73 0.19 6.24 +14 373.78 363.33 22.49 142.58 136.41 43.0 448.64 460.06 83.19 598.24 567.5 67.94 234.64 77.56 279.04 158.48 164.12 43.29 1.61 0.08 2.68 +15 369.71 361.32 20.31 147.49 118.97 54.51 306.98 301.48 35.15 122.87 109.52 49.72 115.48 71.4 78.14 148.68 117.17 67.47 1.09 1.42 0.74 +16 419.18 419.08 14.47 284.65 264.01 99.6 107.66 107.35 12.52 479.28 510.36 61.47 134.26 130.48 13.97 198.02 201.21 28.44 1.41 0.58 1.51 +17 472.22 462.0 27.16 267.49 196.73 134.04 485.32 512.67 55.91 591.9 588.96 124.73 284.97 357.2 145.9 346.39 284.33 116.55 0.55 0.12 0.84 +18 391.3 388.6 9.62 151.22 132.42 42.06 468.62 525.67 101.55 200.8 197.56 12.67 150.61 64.27 151.76 251.49 205.87 109.27 4.37 5.28 3.77 +19 954.49 948.66 16.08 990.53 979.64 20.85 494.76 496.89 16.58 627.61 649.92 62.61 526.51 498.02 54.65 525.11 507.34 59.97 0.55 0.48 0.14 +20 420.28 431.49 23.44 220.23 175.21 96.79 128.13 108.33 59.81 528.18 547.09 71.02 636.01 620.52 55.87 359.88 338.83 106.83 2.7 0.53 4.19 +21 611.37 612.66 8.09 969.24 967.97 7.2 141.84 140.47 2.77 223.33 220.24 7.11 223.8 216.74 15.51 183.19 176.15 32.07 14.7 14.78 1.9 +22 666.05 677.97 29.25 452.57 434.17 32.95 377.8 376.69 16.57 226.24 215.63 23.58 212.9 192.78 35.36 277.65 267.15 39.06 2.22 2.36 0.64 +23 546.68 550.94 8.1 989.48 988.58 2.38 361.98 358.59 13.74 472.15 469.96 9.68 381.26 366.78 27.25 438.45 411.62 57.08 1.72 1.77 0.32 +24 683.11 692.69 31.65 446.21 473.52 51.33 394.62 397.71 7.78 202.01 214.78 26.42 192.77 183.47 31.32 148.62 143.66 27.39 2.14 2.2 0.53 +25 727.79 726.15 4.62 1017.52 1017.01 0.89 182.9 182.62 0.89 267.87 264.79 6.49 256.87 250.23 13.27 219.16 217.08 29.48 15.19 13.6 4.22 +26 965.89 969.6 8.54 996.89 998.45 5.47 326.95 329.26 9.33 345.86 338.48 15.18 318.64 315.39 17.52 309.46 324.8 34.46 1.75 1.74 0.13 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out2.flowstat1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out2.flowstat1 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,4 @@ +FileID SampleName 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 +Rearranged FCSGateTrans on FCS2.003.20Kevents.fcs Sample01 8.5 9.99 0.12 0.67 5.145 4.76 7.925 0.105 0.165 7.27 3.9 3.7 0.12 0.015 1.325 0.72 0.17 10.48 0.955 0.0 4.35 3.195 3.925 1.435 15.705 5.355 +Rearranged FCSGateTrans on FCS2.001.20Kevents.fcs Sample02 0.05 0.33 9.37 4.345 0.225 1.575 4.41 12.41 2.385 0.0 9.395 0.725 11.265 0.0 0.075 0.5 0.18 0.46 1.54 0.395 3.915 3.635 9.05 0.895 14.01 8.86 +Rearranged FCSGateTrans on FCS2.002.20Kevents.fcs Sample03 7.265 6.76 0.07 0.575 4.185 1.035 8.955 0.16 1.1 5.46 0.995 2.145 0.295 3.74 1.41 2.675 2.53 7.615 0.88 4.68 3.98 2.515 3.275 1.69 20.385 5.625 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out2.flowstat2 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out2.flowstat2 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,76 @@ +FSC SSC CD4 CCR7 CD8 CCR3 Percentage Population SampleName +355.78 114.46 519.34 168.82 0.0 0.0 8.5 1 Sample01 +366.75 118.51 522.91 0.0 0.0 173.37 9.99 2 Sample01 +395.48 148.84 392.96 0.0 586.93 243.93 0.12 3 Sample01 +354.8 191.64 0.0 0.0 0.0 275.85 0.67 4 Sample01 +319.28 138.51 0.0 0.0 0.0 0.0 5.14 5 Sample01 +362.66 143.68 0.0 0.0 555.18 0.0 4.76 6 Sample01 +380.81 152.04 0.0 145.67 614.99 252.75 7.92 7 Sample01 +400.38 174.42 364.97 180.95 586.5 279.3 0.1 8 Sample01 +466.22 295.56 170.77 285.06 183.68 310.72 0.16 9 Sample01 +350.06 111.14 519.16 0.0 0.0 0.0 7.27 10 Sample01 +369.23 147.33 0.0 0.0 631.66 282.47 3.9 11 Sample01 +409.74 209.13 0.0 0.0 0.0 0.0 3.7 12 Sample01 +425.44 428.91 350.72 615.96 500.26 478.29 0.12 13 Sample01 +337.43 123.45 431.7 538.75 0.0 114.12 0.02 14 Sample01 +348.19 106.43 290.24 0.0 0.0 0.0 1.32 15 Sample01 +403.15 365.18 0.0 321.2 0.0 0.0 0.72 16 Sample01 +438.05 324.84 321.28 594.04 0.0 317.46 0.17 17 Sample01 +380.78 125.14 526.06 177.24 0.0 180.63 10.48 18 Sample01 +947.3 1005.61 446.06 514.03 412.83 450.29 0.96 19 Sample01 +564.8 892.55 0.0 0.0 0.0 0.0 4.35 21 Sample01 +666.51 454.64 369.55 0.0 0.0 241.11 3.2 22 Sample01 +601.97 997.64 234.58 333.96 283.79 305.79 3.92 23 Sample01 +650.11 361.04 386.73 0.0 0.0 0.0 1.44 24 Sample01 +648.85 1003.84 0.0 239.36 0.0 0.0 15.7 25 Sample01 +823.2 1013.04 211.5 279.26 260.44 233.45 5.36 26 Sample01 +476.64 216.34 377.33 193.69 240.82 0.0 0.05 1 Sample02 +471.05 266.57 360.02 0.0 199.15 475.21 0.33 2 Sample02 +359.23 107.35 511.79 0.0 632.87 473.39 9.37 3 Sample02 +336.76 151.5 0.0 0.0 0.0 512.43 4.35 4 Sample02 +317.03 152.51 0.0 0.0 0.0 172.25 0.22 5 Sample02 +371.85 156.31 0.0 0.0 594.77 279.72 1.58 6 Sample02 +402.12 171.98 0.0 237.43 620.27 364.06 4.41 7 Sample02 +368.32 108.49 514.9 236.18 641.3 500.82 12.41 8 Sample02 +409.74 195.75 0.0 294.04 0.0 580.59 2.38 9 Sample02 +378.44 132.1 0.0 0.0 600.97 570.17 9.4 11 Sample02 +426.29 523.22 0.0 0.0 0.0 197.13 0.73 12 Sample02 +401.36 127.3 516.01 406.73 613.97 296.09 11.26 13 Sample02 +400.4 219.68 190.49 78.26 125.95 176.58 0.08 15 Sample02 +423.89 256.67 0.0 514.7 0.0 187.2 0.5 16 Sample02 +497.74 393.88 292.7 475.7 352.66 342.82 0.18 17 Sample02 +420.84 217.2 284.35 211.29 245.95 275.71 0.46 18 Sample02 +884.52 972.3 440.63 466.03 477.16 487.96 1.54 19 Sample02 +423.88 161.22 0.0 448.14 577.34 274.5 0.4 20 Sample02 +581.05 892.1 0.0 0.0 233.34 0.0 3.91 21 Sample02 +674.41 425.53 389.33 0.0 236.75 288.13 3.64 22 Sample02 +624.32 1007.46 217.78 295.71 297.25 314.48 9.05 23 Sample02 +718.58 474.62 396.15 0.0 224.48 0.0 0.9 24 Sample02 +660.25 1000.27 0.0 237.39 243.98 0.0 14.01 25 Sample02 +807.85 1014.39 196.22 272.05 278.9 248.53 8.86 26 Sample02 +361.35 109.32 516.61 180.94 0.0 0.0 7.26 1 Sample03 +361.78 108.85 520.87 0.0 0.0 146.36 6.76 2 Sample03 +373.61 121.06 332.84 0.0 583.08 240.41 0.07 3 Sample03 +383.18 197.39 0.0 0.0 0.0 258.69 0.57 4 Sample03 +310.49 136.14 0.0 0.0 0.0 0.0 4.18 5 Sample03 +364.87 131.0 0.0 0.0 489.71 0.0 1.03 6 Sample03 +375.6 144.75 0.0 389.84 622.87 273.03 8.96 7 Sample03 +439.6 306.15 288.84 737.82 594.44 672.52 0.16 8 Sample03 +398.88 189.54 0.0 499.34 0.0 324.64 1.1 9 Sample03 +356.28 107.51 519.27 0.0 0.0 0.0 5.46 10 Sample03 +353.41 129.92 0.0 0.0 641.06 278.58 1.0 11 Sample03 +413.37 228.27 0.0 0.0 0.0 0.0 2.14 12 Sample03 +439.22 239.91 367.79 549.6 553.86 308.33 0.3 13 Sample03 +394.14 128.93 523.27 554.83 0.0 0.0 3.74 14 Sample03 +355.73 103.18 334.1 0.0 0.0 0.0 1.41 15 Sample03 +396.87 187.11 0.0 467.44 0.0 0.0 2.68 16 Sample03 +431.47 165.27 520.82 580.86 0.0 230.86 2.53 17 Sample03 +376.04 115.81 519.43 197.15 0.0 151.84 7.61 18 Sample03 +932.7 989.79 437.87 562.38 426.21 406.49 0.88 19 Sample03 +394.2 149.29 0.0 589.44 603.93 0.0 4.68 20 Sample03 +585.23 908.66 0.0 0.0 0.0 0.0 3.98 21 Sample03 +643.85 436.66 357.78 0.0 0.0 212.63 2.51 22 Sample03 +584.99 986.35 270.28 378.3 0.0 317.27 3.28 23 Sample03 +689.54 462.88 376.1 0.0 0.0 0.0 1.69 24 Sample03 +659.74 1006.36 0.0 251.84 0.0 0.0 20.39 25 Sample03 +836.45 1013.12 214.98 295.93 264.33 0.0 5.62 26 Sample03 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/out2.flowstat3 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/out2.flowstat3 Wed Jul 29 13:32:17 2020 -0400 |
| b |
| @@ -0,0 +1,27 @@ +Population FSC_mean FSC_median FSC_stdev SSC_mean SSC_median SSC_stdev CD4_mean CD4_median CD4_stdev CCR7_mean CCR7_median CCR7_stdev CD8_mean CD8_median CD8_stdev CCR3_mean CCR3_median CCR3_stdev Percentage_mean Percentage_median Percentage_stdev +1 397.92 361.35 68.23 146.71 114.46 60.36 471.09 516.61 81.21 181.15 180.94 12.44 80.27 0.0 139.04 0.0 0.0 0.0 5.27 7.26 4.56 +2 399.86 366.75 61.7 164.64 118.51 88.4 467.93 520.87 93.46 0.0 0.0 0.0 66.38 0.0 114.98 264.98 173.37 182.56 5.69 6.76 4.92 +3 376.11 373.61 18.25 125.75 121.06 21.14 412.53 392.96 91.07 0.0 0.0 0.0 600.96 586.93 27.7 319.24 243.93 133.51 3.19 0.12 5.35 +4 358.25 354.8 23.4 180.18 191.64 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 348.99 275.85 141.8 1.86 0.67 2.15 +5 315.6 317.03 4.57 142.39 138.51 8.85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 57.42 0.0 99.45 3.18 4.18 2.61 +6 366.46 364.87 4.8 143.66 143.68 12.66 0.0 0.0 0.0 0.0 0.0 0.0 546.55 555.18 53.06 93.24 0.0 161.5 2.46 1.58 2.01 +7 386.18 380.81 14.05 156.26 152.04 14.1 0.0 0.0 0.0 257.65 237.43 123.33 619.38 620.27 4.02 296.61 273.03 59.28 7.1 7.92 2.38 +8 402.77 400.38 35.7 196.35 174.42 100.64 389.57 364.97 115.02 384.98 236.18 306.81 607.41 594.44 29.61 484.21 500.82 197.14 4.22 0.16 7.09 +9 424.95 409.74 36.15 226.95 195.75 59.5 56.92 0.0 98.59 359.48 294.04 121.21 61.23 0.0 106.05 405.32 324.64 151.95 1.21 1.1 1.11 +10 353.17 353.17 4.4 109.32 109.32 2.57 519.21 519.21 0.08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.36 6.36 1.28 +11 367.03 369.23 12.66 136.45 132.1 9.49 0.0 0.0 0.0 0.0 0.0 0.0 624.56 631.66 20.97 377.07 282.47 167.24 4.77 3.9 4.27 +12 416.47 413.37 8.7 320.21 228.27 176.07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 65.71 0.0 113.81 2.19 2.14 1.49 +13 422.01 425.44 19.16 265.37 239.91 152.41 411.51 367.79 90.9 524.1 549.6 106.92 556.03 553.86 56.89 360.9 308.33 101.84 3.89 0.3 6.38 +14 365.78 365.78 40.1 126.19 126.19 3.87 477.48 477.48 64.75 546.79 546.79 11.37 0.0 0.0 0.0 57.06 57.06 80.7 1.88 1.88 2.63 +15 368.11 355.73 28.22 143.1 106.43 66.34 271.61 290.24 73.6 26.09 0.0 45.18 41.98 0.0 72.72 58.86 0.0 101.95 0.94 1.32 0.74 +16 407.97 403.15 14.14 269.65 256.67 89.74 0.0 0.0 0.0 434.45 467.44 100.88 0.0 0.0 0.0 62.4 0.0 108.08 1.3 0.72 1.2 +17 455.75 438.05 36.51 294.66 324.84 117.25 378.27 321.28 124.28 550.2 580.86 64.85 117.55 0.0 203.61 297.05 317.46 58.71 0.96 0.18 1.36 +18 392.55 380.78 24.61 152.72 125.14 56.04 443.28 519.43 137.68 195.23 197.15 17.11 81.98 0.0 142.0 202.73 180.63 64.82 6.18 7.61 5.16 +19 921.51 932.7 32.85 989.23 989.79 16.66 441.52 440.63 4.17 514.15 514.03 48.18 438.73 426.21 33.94 448.25 450.29 40.77 1.13 0.96 0.36 +20 409.04 409.04 20.99 155.26 155.26 8.44 0.0 0.0 0.0 518.79 518.79 99.91 590.64 590.64 18.8 137.25 137.25 194.1 2.54 2.54 3.03 +21 577.03 581.05 10.79 897.77 892.55 9.43 0.0 0.0 0.0 0.0 0.0 0.0 77.78 0.0 134.72 0.0 0.0 0.0 4.08 3.98 0.24 +22 661.59 666.51 15.86 438.94 436.66 14.69 372.22 369.55 15.94 0.0 0.0 0.0 78.92 0.0 136.69 247.29 241.11 38.13 3.12 3.2 0.57 +23 603.76 601.97 19.73 997.15 997.64 10.56 240.88 234.58 26.81 335.99 333.96 41.33 193.68 283.79 167.87 312.51 314.48 5.99 5.42 3.92 3.16 +24 686.08 689.54 34.37 432.85 462.88 62.46 386.33 386.73 10.03 0.0 0.0 0.0 74.83 0.0 129.6 0.0 0.0 0.0 1.34 1.44 0.4 +25 656.28 659.74 6.44 1003.49 1003.84 3.06 0.0 0.0 0.0 242.86 239.36 7.84 81.33 0.0 140.86 0.0 0.0 0.0 16.7 15.7 3.31 +26 822.5 823.2 14.31 1013.52 1013.12 0.76 207.57 211.5 9.98 282.41 279.26 12.25 267.89 264.33 9.73 160.66 233.45 139.34 6.61 5.62 1.95 |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run1/input1.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run1/input1.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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|
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run1/input2.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run1/input2.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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|
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run1/input3.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run1/input3.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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|
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run2/input1.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run2/input1.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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+694\t370\t391\t254\t311\t330\t22\n' |
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run2/input2.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run2/input2.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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|
| b |
| diff -r 5f670146a9af -r e80b0f62ffb3 test-data/run2/input3.flowtext.flowclr --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/run2/input3.flowtext.flowclr Wed Jul 29 13:32:17 2020 -0400 |
| b |
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