Mercurial > repos > bzonnedda > conifer2
changeset 6:063348f7c3e4 draft
Deleted selected files
| author | bzonnedda |
|---|---|
| date | Mon, 06 Feb 2017 11:10:16 -0500 |
| parents | 5748dc3b41de |
| children | 5dcedbe7edff |
| files | c_calls.xml c_plotcalls.xml c_rpkm.xml conifer.py conifer_functions.py conifer_wrapper.pl |
| diffstat | 6 files changed, 0 insertions(+), 1301 deletions(-) [+] |
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--- a/c_calls.xml Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,48 +0,0 @@ -<tool id="conifer_call" name="CoNIFER CNVs caller" version="1.0"> - <description></description> - <command> - python \${CONIFER_PATH}/conifer.py call - --input $input - --output temporary - --threshold $threshold - 2>&1 - && awk '{print \$2,\$3,\$4,\$5,\$1}' OFS="\t" temporary > $outputFile - </command> - <inputs> - <param format="hdf5" name="input" type="data" label="CoNIFER output analysis file (HDF5)" /> - <param name="threshold" type="float" value="1.5" label="Threshold" /> - </inputs> - <outputs> - <data format="bed" name="outputFile" label="${tool.name} on ${on_string}" /> - </outputs> - <help> -**What it does** - -This tool is a CNVs caller using SVD-ZRPKM thresholding. - -**License and citation** - -This Galaxy tool is Copyright © 2014 `CRS4 Srl.`_ and is released under the `MIT license`_. - -.. _CRS4 Srl.: http://www.crs4.it/ -.. _MIT license: http://opensource.org/licenses/MIT - -You can use this tool only if you agree to the license terms of: `CoNIFER`_. - -.. _CoNIFER: http://conifer.sourceforge.net/ - -If you use this tool, please cite: - -- |Cuccuru2014|_ -- |Krumm2012|_. - -.. |Cuccuru2014| replace:: Cuccuru, G., Orsini, M., Pinna, A., Sbardellati, A., Soranzo, N., Travaglione, A., Uva, P., Zanetti, G., Fotia, G. (2014) Orione, a web-based framework for NGS analysis in microbiology. *Bioinformatics* 30(13), 1928-1929 -.. _Cuccuru2014: http://bioinformatics.oxfordjournals.org/content/30/13/1928 -.. |Krumm2012| replace:: Krumm, N., Sudmant, P. H., Ko, A., O'Roak, B. J., Malig, M., Coe, B. P., NHLBI Exome Sequencing Project, Quinlan, A. R., Nickerson, D. A., Eichler, E. E. (2012) Copy number variation detection and genotyping from exome sequence data. *Genome Res.* 22(8), 1525-1532 -.. _Krumm2012: http://genome.cshlp.org/content/22/8/1525 - </help> - <citations> - <citation type="doi">10.1093/bioinformatics/btu135</citation> - <citation type="doi">10.1101/gr.138115.112</citation> - </citations> -</tool>
--- a/c_plotcalls.xml Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,57 +0,0 @@ -<tool id="conifer_plotcalls" name="CoNIFER plot caller" version="1.0"> - <description>cnv caller</description> - <command interpreter="perl"> - conifer_wrapper.pl --input $input --regions $plot_option.regions --html_file $html_file --html_folder $html_file.files_path - #if str($plot_option.plot_option_select) == "single" - --sample "$plot_option.sample" - #else - --window $plot_option.window - --multiple - #end if - 2>&1 - </command> - <inputs> - <param format="hdf5" name="input" type="data" label="CoNIFER output analysis file (hdf5)"/> - <conditional name="plot_option"> - <param name="plot_option_select" type="select" label="Single or multiple regions?"> - <option value="single" selected="true">Single region</option> - <option value="multiple">Multiple regions</option> - </param> - <when value="single"> - <param name="regions" type="text" size="50" optional="false" label="Region" help="Region for the plot (i.e. chr1:1000-2000)"/> - <param name="sample" type="text" size="50" optional="true" value="" label="Sample" help="Sample names to be highlighted in the plot (optional)"/> - </when> - <when value="multiple"> - <param format="tabular" name="regions" type="data" label="CoNIFER output calls file (tabular)"/> - <param name="window" size="10" type="integer" value="50" label="Window"/> - </when> - </conditional> - </inputs> - <outputs> - <data format="html" name="html_file" label="${tool.name} on ${on_string}" /> - </outputs> -<help> -**What it does** - -Draw a HTML page with CNV plots. It accepts as input either a single region or a file with CoNIFER calls. - -**License and citation** - -This Galaxy tool is Copyright © 2013 `CRS4 Srl.`_ and is released under the `MIT license`_. - -.. _CRS4 Srl.: http://www.crs4.it/ -.. _MIT license: http://opensource.org/licenses/MIT - -If you use this tool in Galaxy, please cite |Cuccuru2014|_. - -.. |Cuccuru2014| replace:: Cuccuru, G., Orsini, M., Pinna, A., Sbardellati, A., Soranzo, N., Travaglione, A., Uva, P., Zanetti, G., Fotia, G. (2014) Orione, a web-based framework for NGS analysis in microbiology. *Bioinformatics*, accepted -.. _Cuccuru2014: http://bioinformatics.oxfordjournals.org/content/early/2014/03/10/bioinformatics.btu135 - -This tool uses CoNIFER, which is licensed separately. Please cite: - -- |Krumm2012|_. - -.. |Krumm2012| replace:: (Krumm et al., 2012) Copy number variation detection and genotyping from exome sequence data. Genome research. -.. _Krumm2012: http://genome.cshlp.org/content/22/8/1525.full?sid=4a7a300a-b960-4544-8611-effc3315411c - </help> -</tool>
--- a/c_rpkm.xml Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,82 +0,0 @@ -<tool id="conifer_rpkm" name="CoNIFER RPKM and Analyze" version="1.0"> - <description></description> - <command> - mkdir rpkm_dir; -#for $input_bam in $inputs: - #if str($input_bam.label.value) != "": - ln -s ${input_bam.inputFile} ${$input_bam.label.value}.bam; - ln -s ${input_bam.inputFile.metadata.bam_index} ${$input_bam.label.value}.bam.bai; - python conifer.py rpkm - --probes $probesFile - --input ${$input_bam.label.value}.bam - --output rpkm_dir/${$input_bam.label.value}.txt; - #else - ln -s ${input_bam.inputFile} ${input_bam.inputFile.dataset.name}.bam; - ln -s ${input_bam.inputFile.metadata.bam_index} ${input_bam.inputFile.dataset.name}.bam.bai; - python conifer.py rpkm - --probes $probesFile - --input ${input_bam.inputFile.dataset.name}.bam - --output rpkm_dir/${input_bam.inputFile.dataset.name}.txt; - #end if -#end for - python conifer.py analyze - --probes $probesFile - --rpkm_dir rpkm_dir - --output $outputFile - --svd $svd - 2>&1; - </command> - <inputs> - <param format="bed" name="probesFile" type="data" label="Target regions (BED)" /> - <repeat name="inputs" title="BAM" min="2" help="Need to add more files? Use controls below."> - <param format="bam" name="inputFile" type="data" label="BAM file" /> - <param name="label" type="text" size="30" value="" label="Label" help="Label to use in the output. If not given, the dataset name will be used instead"> - <validator type="regex" message="Spaces are not allowed">^\S*$</validator> - </param> - </repeat> - <param name="svd" type="integer" value="2" label="SVD" /> - </inputs> - <outputs> - <data format="hdf5" name="outputFile" label="${tool.name} on ${on_string}: hdf5" /> - </outputs> - <help> - -.. class:: warningmark - -**Warning about SVD** - -The SVD value must be less than the number of samples. - -E.g.: if the number of samples is 3, the value of SVD must be 2 or less. - -**What it does** - -This tool calculates RPKM for each BAM file and analyzes them, afterward it creates a unique HDF5 file containing all SVD-ZRPKM values, -probes and samples for downstream analysis with 'CoNIFER CNV caller' and 'CoNIFER plot'. - -**License and citation** - -This Galaxy tool is Copyright © 2015 `CRS4 Srl.`_ and is released under the `MIT license`_. - -.. _CRS4 Srl.: http://www.crs4.it/ -.. _MIT license: http://opensource.org/licenses/MIT - -You can use this tool only if you agree to the license terms of: `CoNIFER`_. - -.. _CoNIFER: http://conifer.sourceforge.net/ - -If you use this tool, please cite: - -- |Cuccuru2014|_ -- |Krumm2012|_. - -.. |Cuccuru2014| replace:: Cuccuru, G., Orsini, M., Pinna, A., Sbardellati, A., Soranzo, N., Travaglione, A., Uva, P., Zanetti, G., Fotia, G. (2014) Orione, a web-based framework for NGS analysis in microbiology. *Bioinformatics* 30(13), 1928-1929 -.. _Cuccuru2014: http://bioinformatics.oxfordjournals.org/content/30/13/1928 -.. |Krumm2012| replace:: Krumm, N., Sudmant, P. H., Ko, A., O'Roak, B. J., Malig, M., Coe, B. P., NHLBI Exome Sequencing Project, Quinlan, A. R., Nickerson, D. A., Eichler, E. E. (2012) Copy number variation detection and genotyping from exome sequence data. *Genome Res.* 22(8), 1525-1532 -.. _Krumm2012: http://genome.cshlp.org/content/22/8/1525 - </help> - <citations> - <citation type="doi">10.1093/bioinformatics/btu135</citation> - <citation type="doi">10.1101/gr.138115.112</citation> - </citations> -</tool>
--- a/conifer.py Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,692 +0,0 @@ -####################################################################### -####################################################################### -# CoNIFER: Copy Number Inference From Exome Reads -# Developed by Niklas Krumm (C) 2012 -# nkrumm@gmail.com -# -# homepage: http://conifer.sf.net -# This program is described in: -# Krumm et al. 2012. Genome Research. doi:10.1101/gr.138115.112 -# -# This file is part of CoNIFER. -# CoNIFER is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see <http://www.gnu.org/licenses/>. -####################################################################### -####################################################################### - -import argparse -import os, sys, copy -import glob -import csv -import conifer_functions as cf -import operator -from tables import * -import numpy as np - -def CF_analyze(args): - # do path/file checks: - try: - # read probes table - probe_fn = str(args.probes[0]) - probes = cf.loadProbeList(probe_fn) - num_probes = len(probes) - print '[INIT] Successfully read in %d probes from %s' % (num_probes, probe_fn) - except IOError as e: - print '[ERROR] Cannot read probes file: ', probe_fn - sys.exit(0) - - try: - svd_outfile_fn = str(args.output) - # h5file_out = openFile(svd_outfile_fn, mode='w') - h5file_out = open_file(svd_outfile_fn, mode='w') - # probe_group = h5file_out.createGroup("/","probes","probes") - probe_group = h5file_out.create_group("/","probes","probes") - except IOError as e: - print '[ERROR] Cannot open SVD output file for writing: ', svd_outfile_fn - sys.exit(0) - - if args.write_svals != "": - sval_f = open(args.write_svals,'w') - - if args.plot_scree != "": - try: - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import pylab as P - from matplotlib.lines import Line2D - from matplotlib.patches import Rectangle - except: - print "[ERROR] One or more of the required modules for plotting cannot be loaded! Are matplotlib and pylab installed?" - sys.exit(0) - - plt.gcf().clear() - fig = plt.figure(figsize=(10,5)) - ax = fig.add_subplot(111) - - rpkm_dir = str(args.rpkm_dir[0]) - rpkm_files = glob.glob(rpkm_dir + "/*") - if len(rpkm_files) == 0: - print '[ERROR] Cannot find any files in RPKM directory (or directory path is incorrect): ', rpkm_dir - sys.exit(0) - elif len(rpkm_files) == 1: - print '[ERROR] Found only 1 RPKM file (sample). CoNIFER requires multiple samples (8 or more) to run. Exiting.' - sys.exit(0) - elif len(rpkm_files) < 8: - print '[WARNING] Only found %d samples... this is less than the recommended minimum, and CoNIFER may not analyze this dataset correctly!' % len(rpkm_files) - elif len(rpkm_files) <= int(args.svd): - print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (len(rpkm_files), int(args.svd)) - sys.exit(0) - else: - print '[INIT] Found %d RPKM files in %s' % (len(rpkm_files), rpkm_dir) - - # read in samples names and generate file list - samples = {} - for f in rpkm_files: - s = '.'.join(f.split('/')[-1].split('.')[0:-1]) - print "[INIT] Mapping file to sampleID: %s --> %s" % (f, s) - samples[s] = f - - #check uniqueness and total # of samples - if len(set(samples)) != len(set(rpkm_files)): - print '[ERROR] Could not successfully derive sample names from RPKM filenames. There are probably non-unique sample names! Please rename files using <sampleID>.txt format!' - sys.exit(0) - - # LOAD RPKM DATA - RPKM_data = np.zeros([num_probes,len(samples)], dtype=np.float) - failed_samples = 0 - - for i,s in enumerate(samples.keys()): - t = np.loadtxt(samples[s], dtype=np.float, delimiter="\t", skiprows=0, usecols=[2]) - if len(t) != num_probes: - print "[WARNING] Number of RPKM values for %s in file %s does not match number of defined probes in %s. **This sample will be dropped from analysis**!" % (s, samples[s], probe_fn) - _ = samples.pop(s) - failed_samples += 1 - else: - RPKM_data[:,i] = t - print "[INIT] Successfully read RPKM data for sampleID: %s" % s - - RPKM_data = RPKM_data[:,0:len(samples)] - print "[INIT] Finished reading RPKM files. Total number of samples in experiment: %d (%d failed to read properly)" % (len(samples), failed_samples) - - if len(samples) <= int(args.svd): - print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (int(args.svd), len(samples)) - sys.exit(0) - - # BEGIN - chrs_to_process = set(map(operator.itemgetter("chr"),probes)) - chrs_to_process_str = ', '.join([cf.chrInt2Str(c) for c in chrs_to_process]) - print '[INIT] Attempting to process chromosomes: ', chrs_to_process_str - - - - for chr in chrs_to_process: - print "[RUNNING: chr%d] Now on: %s" %(chr, cf.chrInt2Str(chr)) - chr_group_name = "chr%d" % chr - # chr_group = h5file_out.createGroup("/",chr_group_name,chr_group_name) - chr_group = h5file_out.create_group("/",chr_group_name,chr_group_name) - - chr_probes = filter(lambda i: i["chr"] == chr, probes) - num_chr_probes = len(chr_probes) - start_probeID = chr_probes[0]['probeID'] - stop_probeID = chr_probes[-1]['probeID'] - print "[RUNNING: chr%d] Found %d probes; probeID range is [%d-%d]" % (chr, len(chr_probes), start_probeID-1, stop_probeID) # probeID is 1-based and slicing is 0-based, hence the start_probeID-1 term - - rpkm = RPKM_data[start_probeID:stop_probeID,:] - - print "[RUNNING: chr%d] Calculating median RPKM" % chr - median = np.median(rpkm,1) - sd = np.std(rpkm,1) - probe_mask = median >= float(args.min_rpkm) - print "[RUNNING: chr%d] Masking %d probes with median RPKM < %f" % (chr, np.sum(probe_mask==False), float(args.min_rpkm)) - - rpkm = rpkm[probe_mask, :] - num_chr_probes = np.sum(probe_mask) - - if num_chr_probes <= len(samples): - print "[ERROR] This chromosome has fewer informative probes than there are samples in the analysis! There are probably no mappings on this chromosome. Please remove these probes from the probes.txt file" - sys.exit(0) - - probeIDs = np.array(map(operator.itemgetter("probeID"),chr_probes))[probe_mask] - probe_starts = np.array(map(operator.itemgetter("start"),chr_probes))[probe_mask] - probe_stops = np.array(map(operator.itemgetter("stop"),chr_probes))[probe_mask] - gene_names = np.array(map(operator.itemgetter("name"),chr_probes))[probe_mask] - - dt = np.dtype([('probeID',np.uint32),('start',np.uint32),('stop',np.uint32), ('name', np.str_, 20)]) - - out_probes = np.empty(num_chr_probes,dtype=dt) - out_probes['probeID'] = probeIDs - out_probes['start'] = probe_starts - out_probes['stop'] = probe_stops - out_probes['name'] = gene_names - # probe_table = h5file_out.createTable(probe_group,"probes_chr%d" % chr,cf.probe,"chr%d" % chr) - probe_table = h5file_out.create_table(probe_group,"probes_chr%d" % chr,cf.probe,"chr%d" % chr) - probe_table.append(out_probes) - - print "[RUNNING: chr%d] Calculating ZRPKM scores..." % chr - rpkm = np.apply_along_axis(cf.zrpkm, 0, rpkm, median[probe_mask], sd[probe_mask]) - - # svd transform - print "[RUNNING: chr%d] SVD decomposition..." % chr - components_removed = int(args.svd) - - U, S, Vt = np.linalg.svd(rpkm,full_matrices=False) - new_S = np.diag(np.hstack([np.zeros([components_removed]),S[components_removed:]])) - - if args.write_svals != "": - sval_f.write('chr' + str(chr) + '\t' + '\t'.join([str(_i) for _i in S]) + "\n") - - if args.plot_scree != "": - ax.plot(S, label='chr' + str(chr),lw=0.5) - - # reconstruct data matrix - rpkm = np.dot(U, np.dot(new_S, Vt)) - - - # save to HDF5 file - print "[RUNNING: chr%d] Saving SVD-ZRPKM values" % chr - - for i,s in enumerate(samples): - out_data = np.empty(num_chr_probes,dtype='u4,f8') - out_data['f0'] = probeIDs - out_data['f1'] = rpkm[:,i] - # sample_tbl = h5file_out.createTable(chr_group,"sample_" + str(s),cf.rpkm_value,"%s" % str(s)) - sample_tbl = h5file_out.create_table(chr_group,"sample_" + str(s),cf.rpkm_value,"%s" % str(s)) - sample_tbl.append(out_data) - - - print "[RUNNING] Saving sampleIDs to file..." - # sample_group = h5file_out.createGroup("/","samples","samples") - # sample_table = h5file_out.createTable(sample_group,"samples",cf.sample,"samples") - sample_group = h5file_out.create_group("/","samples","samples") - sample_table = h5file_out.create_table(sample_group,"samples",cf.sample,"samples") - dt = np.dtype([('sampleID',np.str_,100)]) - out_samples = np.empty(len(samples.keys()),dtype=dt) - out_samples['sampleID'] = np.array(samples.keys()) - sample_table.append(out_samples) - - - if args.write_sd != "": - print "[RUNNING] Calculating standard deviations for all samples (this can take a while)..." - - sd_file = open(args.write_sd,'w') - - for i,s in enumerate(samples): - # collect all SVD-ZRPKM values - count = 1 - for chr in chrs_to_process: - if count == 1: - sd_out = h5file_out.root._f_getChild("chr%d" % chr)._f_getChild("sample_%s" % s).read(field="rpkm").flatten() - else: - sd_out = np.hstack([sd_out,out.h5file_out.root._f_getChild("chr%d" % chr)._f_getChild("sample_%s" % s).read(field="rpkm").flatten()]) - - sd = np.std(sd_out) - sd_file.write("%s\t%f\n" % (s,sd)) - - sd_file.close() - - if args.plot_scree != "": - plt.title("Scree plot") - if len(samples) < 50: - plt.xlim([0,len(samples)]) - plt.xlabel("S values") - else: - plt.xlim([0,50]) - plt.xlabel("S values (only first 50 plotted)") - plt.ylabel("Relative contributed variance") - plt.savefig(args.plot_scree) - - print "[FINISHED]" - h5file_out.close() - sys.exit(0) - -def CF_export(args): - try: - h5file_in_fn = str(args.input) - # h5file_in = openFile(h5file_in_fn, mode='r') - h5file_in = open_file(h5file_in_fn, mode='r') - except IOError as e: - print '[ERROR] Cannot open CoNIFER input file for reading: ', h5file_in_fn - sys.exit(0) - - # read probes - probes = {} - for probes_chr in h5file_in.root.probes: - probes[probes_chr.title] = probes_chr.read() - - if args.sample =='all': - all_samples = list(h5file_in.root.samples.samples.read(field="sampleID")) - - out_path = os.path.abspath(args.output) - - print "[INIT] Preparing to export all samples (%d samples) to %s" % (len(all_samples), out_path) - for sample in all_samples: - try: - outfile_fn = out_path + "/" + sample + ".bed" - outfile_f = open(outfile_fn,'w') - except IOError as e: - print '[ERROR] Cannot open output file for writing: ', outfile_fn - sys.exit(0) - print "[RUNNING] Exporting %s" % sample - - cf.export_sample(h5file_in,sample,probes,outfile_f) - outfile_f.close() - - elif len(args.sample) == 1: - out_path = os.path.abspath(args.output) - sample = args.sample[0] - print "[INIT] Preparing to export sampleID %s to %s" % (args.sample[0], out_path) - try: - if os.path.isdir(out_path): - outfile_fn = out_path + "/" + sample + ".bed" - else: - outfile_fn = out_path - outfile_f = open(outfile_fn,'w') - except IOError as e: - print '[ERROR] Cannot open output file for writing: ', outfile_fn - sys.exit(0) - print "[RUNNING] Exporting %s to %s" % (sample, outfile_fn) - - cf.export_sample(h5file_in,sample,probes,outfile_f) - outfile_f.close() - - else: - out_path = os.path.abspath(args.output) - print "[INIT] Preparing to export %d samples to %s" % (len(args.sample), out_path) - for sample in args.sample: - try: - if os.path.isdir(out_path): - outfile_fn = out_path + "/" + sample + ".bed" - else: - outfile_fn = out_path - outfile_f = open(outfile_fn,'w') - except IOError as e: - print '[ERROR] Cannot open output file for writing: ', outfile_fn - sys.exit(0) - print "[RUNNING] Exporting %s to %s" % (sample, outfile_fn) - - cf.export_sample(h5file_in,sample,probes,outfile_f) - outfile_f.close() - sys.exit(0) - -def CF_call(args): - try: - h5file_in_fn = str(args.input) - # h5file_in = openFile(h5file_in_fn, mode='r') - h5file_in = open_file(h5file_in_fn, mode='r') - except IOError as e: - print '[ERROR] Cannot open CoNIFER input file for reading: ', h5file_in_fn - sys.exit(0) - - try: - callfile_fn = str(args.output) - callfile_f = open(callfile_fn, mode='w') - except IOError as e: - print '[ERROR] Cannot open output file for writing: ', callfile_fn - sys.exit(0) - - chrs_to_process = [] - for chr in h5file_in.root: - if chr._v_title not in ('probes','samples'): - chrs_to_process.append(chr._v_title.replace("chr","")) - - h5file_in.close() - - print '[INIT] Initializing caller at threshold = %f' % (args.threshold) - - r = cf.rpkm_reader(h5file_in_fn) - - all_calls = [] - - for chr in chrs_to_process: - print '[RUNNING] Now processing chr%s' % chr - data = r.getExonValuesByRegion(chr) - - #raw_data = copy.copy(data) - _ = data.smooth() - - mean= np.mean(data.rpkm,axis=1) - sd = np.std(data.rpkm,axis=1) - - for sample in r.getSampleList(): - sample_data = data.getSample([sample]).flatten() - #sample_raw_data = raw_data.getSample([sample]).flatten() - - dup_mask = sample_data >= args.threshold - del_mask = sample_data <= -1*args.threshold - - dup_bkpoints = cf.getbkpoints(dup_mask) #returns exon coordinates for this chromosome (numpy array coords) - del_bkpoints = cf.getbkpoints(del_mask) - - - dups = [] - for start,stop in dup_bkpoints: - try: new_start = np.max(np.where(sample_data[:start] < (mean[:start] + 3*sd[:start]))) - except ValueError: new_start = 0 - try: new_stop = stop + np.min(np.where(sample_data[stop:] < (mean[stop:] + 3*sd[stop:]))) - except ValueError: new_stop = data.shape[1]-1 - dups.append({"sampleID":sample,"chromosome": cf.chrInt2Str(chr), "start":data.exons[new_start]["start"], "stop": data.exons[new_stop]["stop"], "state": "dup"}) - - dels = [] - for start,stop in del_bkpoints: - try: new_start = np.max(np.where(sample_data[:start] > (-1*mean[:start] - 3*sd[:start]))) - except ValueError: new_start = 0 - try: new_stop = stop + np.min(np.where(sample_data[stop:] > (-1*mean[stop:] - 3*sd[stop:]))) - except ValueError: new_stop = data.shape[1]-1 - dels.append({"sampleID":sample,"chromosome": cf.chrInt2Str(chr), "start":data.exons[new_start]["start"], "stop": data.exons[new_stop]["stop"], "state": "del"}) - - dels = cf.mergeCalls(dels) #merges overlapping calls - dups = cf.mergeCalls(dups) - - #print sampleID, len(dels), len(dups) - - all_calls.extend(list(dels)) - all_calls.extend(list(dups)) - - # print calls to file - header = ['sampleID','chromosome','start','stop','state'] - - callfile_f.write('\t'.join(header) + "\n") - for call in all_calls: - print "%s\t%s\t%d\t%d\t%s" % (call["sampleID"], call["chromosome"], call["start"], call["stop"], call["state"]) - callfile_f.write("%s\t%s\t%d\t%d\t%s\n" % (call["sampleID"], call["chromosome"], call["start"], call["stop"], call["state"])) - - sys.exit(0) - -def CF_plot(args): - try: - import locale - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import pylab as P - from matplotlib.lines import Line2D - from matplotlib.patches import Rectangle - _ = locale.setlocale(locale.LC_ALL, '') - except: - print "[ERROR] One or more of the required modules for plotting cannot be loaded! Are matplotlib and pylab installed?" - sys.exit(0) - - - chr, start, stop = cf.parseLocString(args.region) - - r = cf.rpkm_reader(args.input) - - data = r.getExonValuesByRegion(chr,start,stop) - _ = data.smooth() - - plt.gcf().clear() - fig = plt.figure(figsize=(10,5)) - ax = fig.add_subplot(111) - - - ax.plot(data.rpkm, linewidth = 0.3, c='k') - - - if args.sample != 'none': - cnt = 1 - coloriter = iter(['r','b','g','y']) - for sample in args.sample: - try: - color, sampleID = sample.split(":") - except: - color =coloriter.next() - sampleID = sample - - ax.plot(data.getSample([sampleID]), linewidth = 1, c=color, label = sampleID) - - if cnt == 1: - cf.plotRawData(ax, r.getExonValuesByRegion(chr,start,stop,sampleList=[sampleID]).getSample([sampleID]),color=color) - cnt +=1 - plt.legend(prop={'size':10},frameon=False) - - cf.plotGenes(ax, data) - cf.plotGenomicCoords(plt,data) - plt.xlim(0,data.shape[1]) - plt.ylim(-3,3) - - plt.title("%s: %s - %s" % (cf.chrInt2Str(chr),locale.format("%d",start, grouping=True),locale.format("%d",stop, grouping=True))) - plt.xlabel("Position") - plt.ylabel("SVD-ZRPKM Values") - - plt.savefig(args.output) - - sys.exit(0) - -def CF_plotcalls(args): - try: - import matplotlib - matplotlib.use('Agg') - import matplotlib.pyplot as plt - import pylab as P - from matplotlib.lines import Line2D - from matplotlib.patches import Rectangle - except: - print "[ERROR] One or more of the required modules for plotting cannot be loaded! Are matplotlib and pylab installed?" - sys.exit(0) - - import locale - try: - _ = locale.setlocale(locale.LC_ALL, 'en_US') - except: - _ = locale.setlocale(locale.LC_ALL, '') - - try: - callfile_fn = str(args.calls) - callfile_f = open(callfile_fn, mode='r') - except IOError as e: - print '[ERROR] Cannot open call file for reading: ', callfile_fn - sys.exit(0) - - all_calls = [] - header = callfile_f.readline() - - for line in callfile_f: - sampleID, chr, start, stop, state = line.strip().split() - chr = cf.chrStr2Int(chr) - all_calls.append({"chromosome":int(chr), "start":int(start), "stop":int(stop), "sampleID":sampleID}) - - r = cf.rpkm_reader(args.input) - - for call in all_calls: - chr = call["chromosome"] - start = call["start"] - stop = call["stop"] - sampleID = call["sampleID"] - - exons = r.getExonIDs(chr,int(start),int(stop)) - - - window_start = max(exons[0]-args.window,0) - window_stop = exons[-1]+args.window - - data = r.getExonValuesByExons(chr,window_start, window_stop) - _ = data.smooth() - - plt.gcf().clear() - fig = plt.figure(figsize=(10,5)) - ax = fig.add_subplot(111) - - - ax.plot(data.rpkm, linewidth = 0.3, c='k') - - - ax.plot(data.getSample([sampleID]), linewidth = 1, c='r', label = sampleID) - cf.plotRawData(ax, r.getExonValuesByExons(chr,window_start, window_stop,sampleList=[sampleID]).getSample([sampleID]),color='r') - - plt.legend(prop={'size':10},frameon=False) - - cf.plotGenes(ax, data) - cf.plotGenomicCoords(plt,data) - - exon_start = np.where(data.exons["start"] == start)[0][0] - exon_stop = np.where(data.exons["stop"] == stop)[0][0] - _ = ax.add_line(matplotlib.lines.Line2D([exon_start,exon_stop],[2,2],color='k',lw=6,linestyle='-',alpha=1,solid_capstyle='butt')) - - _ = plt.xlim(0,data.shape[1]) - _ = plt.ylim(-3,3) - - plt.title("%s: %s - %s" % (cf.chrInt2Str(chr),locale.format("%d",start, grouping=True),locale.format("%d",stop, grouping=True))) - plt.xlabel("Position") - plt.ylabel("SVD-ZRPKM Values") - outfile = "%s_%d_%d_%s.png" % (cf.chrInt2Str(chr), start, stop, sampleID) - plt.savefig(args.outputdir + "/" + outfile) - -def CF_bam2RPKM(args): - try: - import pysam - except: - print '[ERROR] Cannot load pysam module! Make sure it is insalled' - sys.exit(0) - try: - # read probes table - probe_fn = str(args.probes[0]) - probes = cf.loadProbeList(probe_fn) - num_probes = len(probes) - print '[INIT] Successfully read in %d probes from %s' % (num_probes, probe_fn) - except IOError as e: - print '[ERROR] Cannot read probes file: ', probe_fn - sys.exit(0) - - try: - rpkm_f = open(args.output[0],'w') - except IOError as e: - print '[ERROR] Cannot open rpkm file for writing: ', args.output - sys.exit(0) - - print "[RUNNING] Counting total number of reads in bam file..." - total_reads = float(pysam.view("-c", args.input[0])[0].strip("\n")) - print "[RUNNING] Found %d reads" % total_reads - - f = pysam.Samfile(args.input[0], "rb" ) - - # if not f._hasIndex(): - if not f.has_index(): - print "[ERROR] No index found for bam file (%s)!\n[ERROR] You must first index the bam file and include the .bai file in the same directory as the bam file!" % args.input[0] - sys.exit(0) - - # will be storing values in these arrays - readcount = np.zeros(num_probes) - exon_bp = np.zeros(num_probes) - probeIDs = np.zeros(num_probes) - counter = 0 - - # detect contig naming scheme here # TODO, add an optional "contigs.txt" file or automatically handle contig naming - bam_contigs = f.references - probes_contigs = [str(p) for p in set(map(operator.itemgetter("chr"),probes))] - - probes2contigmap = {} - - for probes_contig in probes_contigs: - if probes_contig in bam_contigs: - probes2contigmap[probes_contig] = probes_contig - elif cf.chrInt2Str(probes_contig) in bam_contigs: - probes2contigmap[probes_contig] = cf.chrInt2Str(probes_contig) - elif cf.chrInt2Str(probes_contig).replace("chr","") in bam_contigs: - probes2contigmap[probes_contig] = cf.chrInt2Str(probes_contig).replace("chr","") - else: - print "[ERROR] Could not find contig '%s' from %s in bam file! \n[ERROR] Perhaps the contig names for the probes are incompatible with the bam file ('chr1' vs. '1'), or unsupported contig naming is used?" % (probes_contig, probe_fn) - sys.exit(0) - - print "[RUNNING] Calculating RPKM values..." - - # loop through each probe - for p in probes: - - # f.fetch is a pysam method and returns an iterator for reads overlapping interval - - p_chr = probes2contigmap[str(p["chr"])] - - p_start = p["start"] - p_stop = p["stop"] - try: - iter = f.fetch(p_chr,p_start,p_stop) - except: - print "[ERROR] Could not retrieve mappings for region %s:%d-%d. Check that contigs are named correctly and the bam file is properly indexed" % (p_chr,p_start,p_stop) - sys.exit(0) - - for i in iter: - if i.pos+1 >= p_start: #this checks to make sure a read actually starts in an interval - readcount[counter] += 1 - - exon_bp[counter] = p_stop-p_start - probeIDs[counter] = counter +1 #probeIDs are 1-based - counter +=1 - - #calcualte RPKM values for all probes - rpkm = (10**9*(readcount)/(exon_bp))/(total_reads) - - out = np.vstack([probeIDs,readcount,rpkm]) - - np.savetxt(rpkm_f,out.transpose(),delimiter='\t',fmt=['%d','%d','%f']) - - rpkm_f.close() - - - -VERSION = "0.2.2" -parser = argparse.ArgumentParser(prog="CoNIFER", description="This is CoNIFER %s (Copy Number Inference From Exome Reads), designed to detect and genotype CNVs and CNPs from exome sequence read-depth data. See Krumm et al., Genome Research (2012) doi:10.1101/gr.138115.112 \nNiklas Krumm, 2012\n nkrumm@uw.edu" % VERSION) -parser.add_argument('--version', action='version', version='%(prog)s ' + VERSION) -subparsers = parser.add_subparsers(help='Command to be run.') - -# rpkm command -rpkm_parser= subparsers.add_parser('rpkm', help='Create an RPKM file from a BAM file') -rpkm_parser.add_argument('--probes',action='store', required=True, metavar='/path/to/probes_file.txt', nargs=1,help="Probe definition file") -rpkm_parser.add_argument('--input',action='store', required=True, metavar='sample.bam',nargs=1, help="Aligned BAM file") -rpkm_parser.add_argument('--output',action='store', required=True, metavar='sample.rpkm.txt',nargs=1, help="RPKM file to write") -rpkm_parser.set_defaults(func=CF_bam2RPKM) - -# analyze command -analyze_parser= subparsers.add_parser('analyze', help='Basic CoNIFER analysis. Reads a directory of RPKM files and a probe list and outputs a HDF5 file containing SVD-ZRPKM values.') -analyze_parser.add_argument('--probes',action='store', required=True, metavar='/path/to/probes_file.txt', nargs=1,help="Probe definition file") -analyze_parser.add_argument('--rpkm_dir',action='store', required=True, metavar='/path/to/folder/containing/rpkm_files/',nargs=1, help="Location of folder containing RPKM files. Folder should contain ONLY contain RPKM files, and all readable RPKM files will used in analysis.") -analyze_parser.add_argument('--output','-o', required=True, metavar='/path/to/output_file.hdf5', type=str, help="Output location of HDF5 file to contain SVD-ZRPKM values") -analyze_parser.add_argument('--svd', metavar='12', type=int, nargs='?', default = 12,help="Number of components to remove") -analyze_parser.add_argument('--min_rpkm', metavar='1.00', type=float, nargs="?", default = 1.00,help="Minimum population median RPKM per probe.") -analyze_parser.add_argument('--write_svals', metavar='SingularValues.txt', type=str, default= "", help="Optional file to write singular values (S-values). Used for Scree Plot.") -analyze_parser.add_argument('--plot_scree', metavar='ScreePlot.png', type=str, default= "", help="Optional graphical scree plot. Requires matplotlib.") -analyze_parser.add_argument('--write_sd', metavar='StandardDeviations.txt', type=str, default= "", help="Optional file with sample SVD-ZRPKM standard deviations. Used to filter noisy samples.") -analyze_parser.set_defaults(func=CF_analyze) - -# export command -export_parser= subparsers.add_parser('export', help='Export SVD-ZRPKM values from a HDF5 file to bed or vcf format.') -export_parser.add_argument('--input','-i',action='store', required=True, metavar='CoNIFER_SVD.hdf5',help="HDF5 file from CoNIFER 'analyze' step") -export_parser.add_argument('--output','-o',action='store', required=False, default='.', metavar='output.bed',help="Location of output file[s]. When exporting multiple samples, top-level directory of this path will be used.") -export_parser.add_argument('--sample','-s',action='store', required=False, metavar='sampleID', default='all', nargs="+",help="SampleID or comma-separated list of sampleIDs to export. Default: export all samples") -export_parser.set_defaults(func=CF_export) - -# plot command -plot_parser= subparsers.add_parser('plot', help='Plot SVD-ZRPKM values using matplotlib') -plot_parser.add_argument('--input','-i',action='store', required=True, metavar='CoNIFER_SVD.hdf5',help="HDF5 file from CoNIFER 'analyze' step") -plot_parser.add_argument('--region',action='store', required=True, metavar='chr#:start-stop',help="Region to plot") -plot_parser.add_argument('--output',action='store', required=True, metavar='image.png',help="Output path and filetype. PDF, PNG, PS, EPS, and SVG are supported.") -plot_parser.add_argument('--sample',action='store', required=False, metavar='sampleID',nargs="+",default='none',help="Sample[s] to highlight. The following optional color spec can be used: <color>:<sampleID>. Available colors are r,b,g,y,c,m,y,k. The unsmoothed SVD-ZRPKM values for the first sample in this list will be drawn. Default: No samples highlighted.") -plot_parser.set_defaults(func=CF_plot) - -# make calls command -call_parser= subparsers.add_parser('call', help='Very rudimentary caller for CNVs using SVD-ZRPKM thresholding.') -call_parser.add_argument('--input','-i',action='store', required=True, metavar='CoNIFER_SVD.hdf5',help="HDF5 file from CoNIFER 'analyze' step") -call_parser.add_argument('--output',action='store', required=True, metavar='calls.txt',help="Output file for calls") -call_parser.add_argument('--threshold', metavar='1.50', type=float, nargs='?', required=False, default = 1.50,help="+/- Threshold for calling (minimum SVD-ZRPKM)") -call_parser.set_defaults(func=CF_call) - -# plotcalls command -plotcalls_parser= subparsers.add_parser('plotcalls', help='Make basic plots from call file from "call" command.') -plotcalls_parser.add_argument('--input','-i',action='store', required=True, metavar='CoNIFER_SVD.hdf5',help="HDF5 file from CoNIFER 'analyze' step") -plotcalls_parser.add_argument('--calls',action='store', required=True, metavar='calls.txt',help="File with calls from 'call' command.") -plotcalls_parser.add_argument('--outputdir',action='store', required=True, metavar='/path/to/directory',help="Output directory for plots") -plotcalls_parser.add_argument('--window',action='store', required=False, metavar='50',default=50,help="In exons, the amount of padding to plot around each call") -plotcalls_parser.set_defaults(func=CF_plotcalls) - -args = parser.parse_args() -args.func(args)
--- a/conifer_functions.py Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,371 +0,0 @@ -####################################################################### -####################################################################### -# CoNIFER: Copy Number Inference From Exome Reads -# Developed by Niklas Krumm (C) 2012 -# nkrumm@gmail.com -# -# homepage: http://conifer.sf.net -# This program is described in: -# Krumm et al. 2012. Genome Research. doi:10.1101/gr.138115.112 -# -# This file is part of CoNIFER. -# CoNIFER is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see <http://www.gnu.org/licenses/>. -####################################################################### -####################################################################### - -import csv -from tables import * -import numpy as np -import operator - -class rpkm_value(IsDescription): - probeID = UInt32Col(pos=0) - rpkm = FloatCol(pos=1) - -class probe(IsDescription): - probeID = UInt32Col(pos=0) - start = UInt32Col(pos=1) # start of probe - stop = UInt32Col(pos=2) # stop of probe - name = StringCol(20,pos=3) # 20-character String - -def chrInt2Str(chromosome_int): - if int(chromosome_int) == 23: - return 'chrX' - elif int(chromosome_int) == 24: - return 'chrY' - else: - return 'chr' + str(chromosome_int) - - -def chrStr2Int(chromosome_str): - chr = chromosome_str.replace('chr','') - if chr == 'X': - return 23 - elif chr == 'Y': - return 24 - else: - return int(chr) - -def parseLocString(locstr): - try: - chr,locstr = locstr.split(":") - start, stop = locstr.split("-") - except: - chr, start, stop = locstr.split("\t") - - chr = chrStr2Int(chr) - start = int(start) - stop = int(stop) - return (chr,start,stop) - -def zrpkm(rpkm,median,sd): - return (rpkm - median) / sd - - - -class sample(IsDescription): - sampleID = StringCol(100,pos=0) # 20-char string (sampleID) - -def loadProbeList(CF_probe_filename): - # Load data files - probefile = open(CF_probe_filename, 'rb') - s = csv.Sniffer() - header = s.has_header(probefile.read(1024)) - probefile.seek(0) - dialect = s.sniff(probefile.read(1024)) - probefile.seek(0) - if header: - r = csv.DictReader(probefile, dialect=dialect) - else: - r = csv.DictReader(probefile, dialect=dialect, fieldnames=['chr','start','stop','name']) - - probes = [] - probeID = 1 - for row in r: - probes.append({'probeID': probeID, 'chr':chrStr2Int(row['chr']),'start':int(row['start']),'stop':int(row['stop']), 'name':row['name']}) - probeID +=1 - - if len(probes) == 0: - raise Exception("No probes in probe file") - - return probes - - -def export_sample(h5file_in,sample,probes,outfile_f): - dt = np.dtype([('chr','|S10'),('start', '<u4'), ('stop', '<u4'), ('name', '|S20'),('SVDZRPKM',np.float)]) - for chr in h5file_in.root: - if chr._v_title in ('probes','samples'): - continue - - out_data = np.empty(len(probes[chr._v_title]),dtype=dt) - out_data["SVDZRPKM"] = chr._f_getChild("sample_" + sample).read(field='rpkm') - out_data["chr"] = np.repeat(chr._v_title,len(out_data)) - out_data["start"] = probes[chr._v_title]["start"] - out_data["stop"] = probes[chr._v_title]["stop"] - out_data["name"] = probes[chr._v_title]["name"] - np.savetxt(outfile_f, out_data,fmt=["%s","%d","%d","%s","%f"], delimiter="\t") - - - -def plotGenes(axis, rpkm_data, levels=5,x_pos=-2,text_pos='right',line_spacing=0.1,text_offset=0.25,data_range=None): - from matplotlib.lines import Line2D - counter = 0 - prev_gene = "" - if data_range is not None: - exon_set = rpkm_data.exons[data_range] - else: - exon_set = rpkm_data.exons - for gene in exon_set["name"]: - if gene == prev_gene: - continue - elif gene == 'None': - continue - start = np.min(np.where(exon_set["name"] == gene)) - stop = np.max(np.where(exon_set["name"] == gene)) + 1 - _ = axis.add_line(Line2D([start-0.5,stop-0.5],[x_pos - (counter * line_spacing),x_pos - (counter * line_spacing)],color=(102/255.,33/255.,168/255.,0.6),linewidth=5,linestyle='-',alpha=0.5,solid_capstyle='butt')) - _ = axis.text(stop+text_offset, x_pos - (counter * line_spacing), gene, ha='left',va='center',fontsize=6) - counter +=1 - prev_gene = gene - if counter > 5: - counter = 0 - - -def plotGenomicCoords(plt, rpkm_data,fontsize=10,rotation=0): - import operator - import locale - exon_set = rpkm_data.exons - genomic_coords = np.array(map(operator.itemgetter("start"),exon_set)) - - ticks = range(0,len(exon_set),len(exon_set)/5) - - ticks[-1] -= 1 # the last tick is going to be off the chart, so we estimate it as the second to last genomic coord. - labels = [locale.format("%d", genomic_coords[i], grouping=True) for i in ticks if i < len(genomic_coords)] - if rotation != 0: - ha = "right" - else: - ha = "center" - _ = plt.xticks(ticks,labels,fontsize=fontsize,rotation=rotation,ha=ha) - - -def plotRawData(axis, rpkm_data, color='r',linewidth=0.7): - zero_stack = np.zeros(len(rpkm_data)) - positions = np.repeat(np.arange(0,len(rpkm_data)),3) - logr = np.vstack([zero_stack,rpkm_data.flatten(),zero_stack]).transpose().ravel() - axis.plot(positions,logr,color=color,marker=None,linewidth=1) - -def getbkpoints(mask): - bkpoints = np.nonzero(np.logical_xor(mask[0:-1],mask[1:]))[0]+1 - if mask[0] == 1: - bkpoints = np.hstack([0,bkpoints]) - if mask[-1] == 1: - bkpoints = np.hstack([bkpoints,len(mask)]) - return bkpoints.reshape(len(bkpoints)/2,2) - -def mergeCalls(calls): - if len(calls) == 0: - return [] - - out_calls = [] - calls=np.array(calls)[np.argsort(np.array(map(operator.itemgetter("start"),calls),dtype=np.int))] - pstart = calls[0]["start"] - pstop = calls[0]["stop"] - for d in calls: - if d["start"] <= pstop: - pstop = max(d["stop"],pstop) - else: - out_calls.append({"sampleID": d["sampleID"], "chromosome": d["chromosome"], "start":pstart, "stop":pstop, "state": d["state"]}) - pstart = d["start"] - pstop = d["stop"] - - out_calls.append({"sampleID": d["sampleID"], "chromosome": d["chromosome"], "start":pstart, "stop":pstop, "state": d["state"]}) - return out_calls - -class rpkm_data: - def __init__(self): - self.rpkm = None - self.samples = None - self.exons = None - self.isGenotype = False - self.calls = [] - self.refined_calls = [] - - def smooth(self, window = 15, padded = False): #todo, fix the padding here - if self.isGenotype: - print "Warning: the data in this rpkm_data container are single genotype values. Smoothing will have no effect!" - return self.rpkm - - if window > 0: - weightings=np.blackman(window) - weightings = weightings/weightings.sum() - smoothed_data = np.array([]) - for row in self.rpkm.transpose(): - smoothed = np.convolve(row, weightings)[(window-1)/2:-((window-1)/2)] - if len(smoothed_data) == 0: - smoothed_data = smoothed - else: - smoothed_data = np.vstack([smoothed_data,smoothed]) - - self.rpkm = smoothed_data.transpose() - return self.rpkm - else: - return self.rpkm - - def getSample(self, sampleIDs): - sample_array = np.array(self.samples) - if isinstance(sampleIDs,list): - mask = np.zeros(len(sample_array),dtype=np.bool) - for sampleID in sampleIDs: - mask = np.logical_or(mask, sample_array == str(sampleID)) - - return self.rpkm[:,mask] - else: - mask = sample_array == str(sampleID) - return self.rpkm[:,mask] - - def getSamples(self, sampleIDs): - return self.getSample(sampleIDs) - - @property - def shape(self): - if self.isGenotype: - return [len(self.samples), 1] - else: - return [len(self.samples), len(self.exons)] - - -class rpkm_reader: - def __init__(self, rpkm_fn=None): - """Initialize an rpkm_reader instance. Specify the location of the data file""" - - if rpkm_fn == None: - print "Must specify RPKM HDF5 file!" - return 0 - # set up file access - # self.h5file = openFile(rpkm_fn, mode='r') - self.h5file = open_file(rpkm_fn, mode='r') - self.sample_table = self.h5file.root.samples.samples - - def __del__(self): - self.h5file.close() - - def getExonValuesByExons(self, chromosome, start_exon, stop_exon, sampleList=None,genotype=False): - - probe_tbl = self.h5file.root.probes._f_getChild("probes_chr" + str(chromosome)) - #table_rows = probe_tbl.getWhereList('(start >= %d) & (stop <= %d)' % (start,stop)) - start_exon = max(start_exon,0) - stop_exon = min(stop_exon, probe_tbl.nrows) - #print start_exon, stop_exon - table_rows = np.arange(start_exon,stop_exon,1) - data_tbl = self.h5file.root._f_getChild("chr" + str(chromosome)) - - if sampleList == None: - num_samples = data_tbl._v_nchildren - samples = data_tbl - else: - num_samples = len(sampleList) - samples = [data_tbl._f_getChild("sample_" + s) for s in sampleList] - - data = np.empty([num_samples,len(table_rows)],dtype=np.float) - - out_sample_list = [] - cnt = 0 - for sample_tbl in samples: - d = sample_tbl.readCoordinates(table_rows,field="rpkm") - data[cnt,:] = d - cnt +=1 - out_sample_list.append(sample_tbl.title) - - d = rpkm_data() - if genotype: # return average #todo-- implement median and SD? - d.rpkm = data.transpose().mean(axis=0) - d.isGenotype = True - else: #return all data points - d.rpkm = data.transpose() - d.samples = out_sample_list - d.exons = probe_tbl.readCoordinates(table_rows) - - return d - - def getExonValuesByRegion(self, chromosome, start=None, stop=None, sampleList=None,genotype=False): - probe_tbl = self.h5file.root.probes._f_getChild("probes_chr" + str(chromosome)) - if (start is not None) and (stop is not None): - table_rows = probe_tbl.getWhereList('(start >= %d) & (stop <= %d)' % (start,stop)) - else: - table_rows = probe_tbl.getWhereList('(start >= 0) & (stop <= 1000000000)') - - data_tbl = self.h5file.root._f_getChild("chr" + str(chromosome)) - - if sampleList == None: - num_samples = data_tbl._v_nchildren - samples = data_tbl - else: - num_samples = len(sampleList) - samples = [data_tbl._f_getChild("sample_" + s) for s in sampleList] - - data = np.empty([num_samples,len(table_rows)],dtype=np.float) - - out_sample_list = [] - cnt = 0 - for sample_tbl in samples: - d = sample_tbl.readCoordinates(table_rows,field="rpkm") - data[cnt,:] = d - cnt +=1 - out_sample_list.append(sample_tbl.title) - - d = rpkm_data() - if genotype: # return average #todo-- implement median and SD? - d.rpkm = data.transpose().mean(axis=0) - d.isGenotype = True - else: #return all data points - d.rpkm = data.transpose() - d.samples = out_sample_list - d.exons = probe_tbl.readCoordinates(table_rows) - - return d - - def getSampleList(self,cohort=None,sex=None,ethnicity=None,custom=None): - """Return a list of available samples in the current data file. Specifying no arguments will return all available samples""" - - readWhereStr = "" - if custom != None: - readWhereStr = custom - else: - if cohort != None: - if isinstance(cohort,list): - for c in cohort: - readWhereStr += "(cohort=='%s') | " % c - readWhereStr = readWhereStr.strip(" |") - readWhereStr += " & " - else: - readWhereStr += "(cohort=='%s') " % cohort - if sex != None: - if sex not in ['M','F']: - sex = sex.upper()[0] - readWhereStr += " (sex=='%s') &" % sex - if ethnicity != None: - readWhereStr += " (ethnicity=='%s') &" % ethnicity - - readWhereStr = readWhereStr.strip(" &") # remove leading or trailing characters - if readWhereStr != "": - #print readWhereStr - sampleIDs = self.sample_table.readWhere(readWhereStr,field='sampleID') - else: - sampleIDs = self.sample_table.read(field='sampleID') - - return sampleIDs - - def getExonIDs(self, chromosome, start, stop): - probe_tbl = self.h5file.root.probes._f_getChild("probes_chr" + str(chromosome)) - exons = probe_tbl.getWhereList('(start >= %d) & (stop <= %d)' % (start,stop)) - return exons
--- a/conifer_wrapper.pl Mon Feb 06 10:56:20 2017 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,51 +0,0 @@ -#!/usr/bin/env perl - -# Execute CoNIFER plotcalls and -# returns a HTML page with links to PNG plots - -use strict; -use warnings; -use Getopt::Long; -use File::Basename; -use File::Path qw(make_path remove_tree); - -my $command; -my $dir=$ENV{'CONIFER_PATH'}; - -our ($multiple, $input, $regions, $sample, $window, $html_file, $html_folder, $verbose); - -GetOptions('multiple'=>\$multiple, 'input=s'=>\$input, 'regions=s'=>\$regions, - 'sample:s'=>\$sample, 'window:i'=>\$window, 'verbose'=>\$verbose, - 'html_file=s'=>\$html_file, 'html_folder=s'=>\$html_folder); - -make_path($html_folder); - -# Build command -if ($multiple){ - # Reformat file with regions as required by CoNIFER plotcalls - system("awk '{print \$5,\$1,\$2,\$3,\$4}' OFS=\"\t\" $regions > regions_sorted"); - - $command = "python ".$dir."/conifer.py plotcalls --input $input --calls regions_sorted --window $window --outputdir $html_folder 2>&1"; -}else{ - my $sample_command = ($sample eq "") ? "" : "--sample $sample"; - my $plot_name = $regions; - $plot_name =~ s/[:-]/_/g; - $command = "python ".$dir."/conifer.py plot --input $input --region $regions $sample_command --output $html_folder/$plot_name.png 2>&1"; -} - -# Run CoNIFER -system($command); -$verbose and print $command,"\n"; - -# Write HTML file -open(HTML, ">$html_file"); -print HTML "<html><head><title>CoNIFER: Copy Number Analysis for Targeted Resequencing</title></head><body><h3>CoNIFER Output Files:</h3><p><ul>\n"; -opendir(DIR, $html_folder); - -my @FILES= grep { /png$/ } readdir(DIR); -closedir(DIR); -foreach my $file (@FILES) { - print HTML "<li><a href=$file>$file</a><img src=\"$file\" height=\"50\" width=\"100\"></li>\n"; -} -print HTML "</ul></p></body></html>\n"; -close(HTML);