Mercurial > repos > chmaramis > testirprofiler
changeset 9:f4edb9437b95 draft
Deleted selected files
| author | chmaramis |
|---|---|
| date | Sun, 18 Mar 2018 07:06:56 -0400 |
| parents | 2669fa191052 |
| children | 5339fb9d8aee |
| files | cmpb2016/ext_repertoire_V.py cmpb2016/ext_repertoire_V.xml cmpb2016/ngs_filtering.py cmpb2016/ngs_filtering.xml cmpb2016/pub_clono_CDR3.py cmpb2016/pub_clono_CDR3.xml |
| diffstat | 6 files changed, 0 insertions(+), 798 deletions(-) [+] |
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--- a/cmpb2016/ext_repertoire_V.py Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Jun 20 14:58:08 2014 - -@author: chmaramis -""" - -from __future__ import division -import numpy as np -from pandas import * -import functools as ft -import sys -import time - -frm = lambda x,y: '{r}/{l}'.format(r=x,l=y) - -def repertoireComputation(inp_name, fname): - - df = DataFrame() - df = read_csv(inp_name, sep='\t', index_col=0 ) - #tp = read_csv(inp_name, iterator=True, chunksize=5000,sep='\t', index_col=0 ) - #df = concat([chunk for chunk in tp]) - - - vgroup = df.groupby(['V-GENE']) - vdi = vgroup.size() - rep = DataFrame(list(vdi.index), columns=['V-GENE']) - rep['Reads'] = vdi.values - #rep['Reads/Total'] = ['{r}/{l}'.format(r=p , l = len(df)) for p in vdi.values] - rep['Reads/Total'] = rep['Reads'].map(ft.partial(frm, y=len(df))) - rep['Frequency %'] = (100*rep['Reads']/len(df)).map('{:.4f}'.format) - - rep = rep.sort_values(by = ['Reads'] , ascending = False) - rep.index = range(1,len(rep)+1) - - su = rep[['V-GENE','Frequency %']].head(10) - spl = fname.split('_') - summdf = DataFrame([su['V-GENE'].values[0],su['Frequency %'].values[0]], - index = ['Dominant V-GENE','Frequency'], columns = [spl[0]]) - summdf['%'] = '' - - return (rep, su, summdf) - - -if __name__ == '__main__': - - start=time.time() - - # Parse input arguments - inp_name = sys.argv[1] - outrep = sys.argv[2] - summ_rep = sys.argv[3] - summ_rep2 = sys.argv[4] - fname = sys.argv[5] - - # Execute basic function - rep, su, summdf = repertoireComputation(inp_name, fname) - - # Save output to CSV files - if not rep.empty: - rep.to_csv(outrep, sep = '\t') - if not su.empty: - su.to_csv(summ_rep, sep = '\t') - if not summdf.empty: - summdf.to_csv(summ_rep2, sep = '\t') - - # Print execution time - stop=time.time() - print('Runtime:' + str(stop-start))
--- a/cmpb2016/ext_repertoire_V.xml Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,24 +0,0 @@ -<tool id="extRepV" name="V-Gene Repertoire Extraction" version="0.9"> - <description>Compute repertoire of V-genes</description> - <command interpreter="python">ext_repertoire_V.py $input $clonos $topcl $summ ${input.name}</command> - <inputs> - <param format="tabular" name="input" type="data" label="File of clonotypes"/> - - - </inputs> - -<outputs> - <data name="clonos" format="tabular" label="${input.name}_repertoire"/> - <data name="topcl" format="tabular" label="${input.name}_top10rep"/> - <data name="summ" format="tabular" label="${input.name}_Summary4"/> - - - - </outputs> - - - <help> -This tool computes the repertoire of V-genes (i.e. , the number of clonotypes using each V-gene over the total number of clonotypes). - </help> - -</tool>
--- a/cmpb2016/ngs_filtering.py Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,452 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Sep 4 18:41:42 2013 - -@author: chmaramis -""" - -from __future__ import division -import string as strpy -import numpy as np -from pandas import * -from numpy import nan as NA -import time -import sys - - -def filter_condition_AAjunction(x): - x= x.strip() - if ' ' in x: - return x.split(' ')[0] - else: - return x - -#-----------frame creation--------------------- -def filtering(inp,cells,psorf,con,prod,CF,Vper,Vgene,laa1,laa2,conaa,Jgene,Dgene,fname): - - try: - path=inp - frame = DataFrame() - seqlen = [] - head = [] - tp = read_csv(path, iterator=True, chunksize=5000,sep='\t', index_col=0 ) - frame = concat([chunk for chunk in tp]) - - frcol = list(frame.columns) - #print frcol[-1] - if 'Unnamed' in frcol[-1]: - del frcol[-1] - frame=frame[frcol] - - frame.index = range(1,len(frame)+1) - - head.append('Total reads of raw data') - seqlen.append(len(frame)) - - #------------drop nulls-------------------- - filtered = DataFrame() - filtall = DataFrame() - summ_df = DataFrame() - filtered = frame[isnull(frame['AA JUNCTION']) | isnull(frame['V-GENE and allele'])] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = "NoResults" - frame = frame[frame['AA JUNCTION'].notnull()] - frame = frame[frame['V-GENE and allele'].notnull()] - - head.append('Not Null CDR3/V') - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - filtered = DataFrame() - - if psorf.startswith('y') or psorf.startswith('Y'): - - cc0=np.array(frame['V-GENE and allele'].unique()) - - - for x in cc0: - x1=x.split('*') - try: - if (x1[1].find('P')>-1) or (x1[1].find('ORF')>-1): - filtered = filtered.append(frame[frame['V-GENE and allele'] == x]) - frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,NA) - elif x.find('or')>-1: - posa=x.count('or') - x2=x.split('or') - x4='' - genelist=[] - for cnt in range(0, posa+1): - x3=x2[cnt].split('*') - x3[0]=x3[0].strip()#kobei ta space - k=x3[0].split(' ')# holds only TRBV - if cnt==0: - genelist.append(k[1]) - x4+=k[1] - elif ((str(k[1]) in genelist) == False) & (x3[1].find('P')==-1):# check for P in x3 - genelist.append(k[1]) - x4+=' or ' - x4+=k[1] - x3=None - k1=None - genelist=None - - frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,x4) - - else: - s=x1[0].split(' ') - frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,s[1]) - except IndexError as e: - print('V-gene is already been formed') - continue - - x=None - x1=None - s=None - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'P or ORF' - frame = frame[frame['V-GENE and allele'].notnull()] - - head.append('Functional TRBV') - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - filtered = DataFrame() - - - - #------------FILTERING for data quality-------------------- - if con.startswith('y') or con.startswith('Y'): - filtered = frame [frame['AA JUNCTION'].str.contains('X') | - frame['AA JUNCTION'].str.contains('#') | - frame['AA JUNCTION'].str.contains('[*]')] - - - - frame = frame [~frame['AA JUNCTION'].str.contains('X') & - ~frame['AA JUNCTION'].str.contains('#') & - ~frame['AA JUNCTION'].str.contains('[*]') ] - - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'X,#,*' - head.append('Not Containing X,#,*') - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - filtered = DataFrame() - - # Set label of functionality column, taking into account current & past IMGT Summary column label - functionality_label = 'Functionality' - if 'V-DOMAIN Functionality' in frame.columns: - functionality_label = 'V-DOMAIN Functionality' - - if prod.startswith('y') or prod.startswith('Y'): - filtered = frame[~frame[functionality_label].str.startswith('productive')] - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'not productive' - - - frame=frame[frame[functionality_label].str.startswith('productive')] - - head.append('Productive') - head.append('filter out') - seqlen.append(len(frame)) - - seqlen.append(len(filtered)) - - - frame['AA JUNCTION'] = frame['AA JUNCTION'].map(filter_condition_AAjunction) - - if CF.startswith('y') or CF.startswith('Y'): - if cells == 'TCR': - filtered = DataFrame() - filtered = frame[~frame['AA JUNCTION'].str.startswith('C') | - ~frame['AA JUNCTION'].str.endswith('F')] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'Not C..F' - - frame = frame[frame['AA JUNCTION'].str.startswith('C') & - frame['AA JUNCTION'].str.endswith('F')] - - head.append('CDR3 landmarks C-F') - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - filtered = DataFrame() - elif cells == 'BCR': - filtered = DataFrame() - filtered = frame[~frame['AA JUNCTION'].str.startswith('C') | - ~frame['AA JUNCTION'].str.endswith('W')] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'Not C..W' - - frame = frame[frame['AA JUNCTION'].str.startswith('C') & - frame['AA JUNCTION'].str.endswith('W')] - - head.append('CDR3 landmarks C-W') - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - filtered = DataFrame() - else: - print('TCR or BCR type') - - - filtered = DataFrame() - - filtered = frame[frame['V-REGION identity %'] < Vper] - - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'identity < {iden}%'.format(iden = Vper) - - frame=frame[frame['V-REGION identity %']>= Vper] - head.append('Identity >= {iden}%'.format(iden = Vper)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - head.append('Total filter out A') - head.append('Total filter in A') - seqlen.append(len(filtall)) - seqlen.append(len(frame)) - - ############################### - if Vgene != 'null': - - filtered = DataFrame() - - filtered = frame[frame['V-GENE and allele'] != Vgene] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'V-GENE != {} '.format(Vgene) - - - frame = frame[frame['V-GENE and allele'] == Vgene] - - - - head.append('V-GENE = {} '.format(Vgene)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - - - ############################### - if (laa1 != 'null') or (laa2 != 'null'): - if int(laa2) == 0: - low = int(laa1) - high = 100 - elif int(laa1) > int(laa2): - low = int(laa2) - high = int(laa1) - else: - low = int(laa1) - high = int(laa2) - - filtered = DataFrame() - criteria = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) < low) - criteria2 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) > high) - filtered = frame[criteria | criteria2] - - filtall = filtall.append(filtered) - if int(laa2)==0: - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'CDR3 length not bigger than {}'.format(low) - else: - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'CDR3 length not from {} to {}'.format(low,high) - - criteria3 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) >= low) - criteria4 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) <= high) - frame = frame[criteria3 & criteria4] - - if int(laa2)==0: - head.append('CDR3 length bigger than {}'.format(low)) - else: - head.append('CDR3 length from {} to {} '.format(low,high)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - ############################### - if conaa != 'null': - if conaa.islower(): - conaa = conaa.upper() - filtered = DataFrame() - - filtered = frame[~frame['AA JUNCTION'].str.contains(conaa)] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'CDR3 not containing {}'.format(conaa) - - frame = frame[frame['AA JUNCTION'].str.contains(conaa) ] - - head.append('CDR3 containing {}'.format(conaa)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - - - - #####------------keep the small J gene name-------------------- - #frame['J-GENE and allele'] = frame['J-GENE and allele'].map(filter_condition_Jgene) - cc2=np.array(frame['J-GENE and allele'].unique()) - - for x in cc2: - try: - if notnull(x): - x1=x.split('*') - # print(x) - # print (x1[0]) - trbj=x1[0].split(' ') - frame['J-GENE and allele']=frame['J-GENE and allele'].replace(x,trbj[1]) - except IndexError as e: - print('J-Gene has been formed') - - - - x=None - x1=None - - - #------------keep the small D gene name-------------------- - cc1=np.array(frame['D-GENE and allele'].unique()) - for x in cc1: - try: - if notnull(x): - x1=x.split('*') - trbd=x1[0].split(' ') - frame['D-GENE and allele']=frame['D-GENE and allele'].replace(x,trbd[1]) - else: - frame['D-GENE and allele']=frame['D-GENE and allele'].replace(x,'none') - except IndexError as e: - print('D-gene has been formed') - - - x=None - x1=None - - - if Jgene != 'null': - - filtered = DataFrame() - - filtered = frame[frame['J-GENE and allele'] != Jgene] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'J-GENE not {} '.format(Jgene) - - - frame = frame[frame['J-GENE and allele'] == Jgene] - - - - head.append('J-GENE = {} '.format(Jgene)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - - - if Dgene != 'null': - - filtered = DataFrame() - - filtered = frame[frame['D-GENE and allele'] != Dgene] - - filtall = filtall.append(filtered) - if len(filtall) > 0: - filtall.loc[filtered.index,'Reason'] = 'D-GENE not {} '.format(Dgene) - - - frame = frame[frame['D-GENE and allele'] == Dgene] - - - - head.append('D-GENE = {} '.format(Dgene)) - head.append('filter out') - seqlen.append(len(frame)) - seqlen.append(len(filtered)) - - - head.append('Total filter out') - head.append('Total filter in') - seqlen.append(len(filtall)) - seqlen.append(len(frame)) - summ_df = DataFrame(index = head) - col = fname - - summ_df[col] = seqlen - frame=frame.rename(columns = {'V-GENE and allele':'V-GENE', - 'J-GENE and allele':'J-GENE','D-GENE and allele':'D-GENE'}) - - - frcol.append('Reason') - - filtall = filtall[frcol] - - #--------------out CSV--------------------------- - frame.index = range(1,len(frame)+1) - if not summ_df.empty: - summ_df['%'] = (100*summ_df[summ_df.columns[0]]/summ_df[summ_df.columns[0]][summ_df.index[0]]).map(('{:.4f}'.format)) - return(frame,filtall,summ_df) - except KeyError as e: - print('This file has no ' + str(e) + ' column') - return(frame,filtall,summ_df) - - -if __name__ == '__main__': - - start=time.time() - - # Parse input arguments - inp = sys.argv[1] - cells = sys.argv[2] - psorf = sys.argv[3] - con = sys.argv[4] - prod = sys.argv[5] - CF = sys.argv[6] - Vper = float(sys.argv[7]) - Vgene = sys.argv[8] - laa1 = sys.argv[9] - conaa = sys.argv[10] - filterin = sys.argv[11] - filterout = sys.argv[12] - Sum_table = sys.argv[13] - Jgene = sys.argv[14] - Dgene = sys.argv[15] - laa2 = sys.argv[16] - fname = sys.argv[17] - - # Execute basic function - fin,fout,summ = filtering(inp,cells,psorf,con,prod,CF,Vper,Vgene,laa1,laa2,conaa,Jgene,Dgene,fname) - - # Save output to CSV files - if not summ.empty: - summ.to_csv(Sum_table, sep = '\t') - if not fin.empty: - fin.to_csv(filterin , sep = '\t') - if not fout.empty: - fout.to_csv(filterout, sep= '\t') - - # Print execution time - stop=time.time() - print('Runtime:' + str(stop-start)) -
--- a/cmpb2016/ngs_filtering.xml Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,139 +0,0 @@ -<tool id="ngsFilt" name="IMGT Report Filtering" version="0.9" profile="15.10"> - <description>Filter IMGT Summary Data</description> - <command interpreter="python">ngs_filtering.py $input $TCR_or_BCR $Vfun $spChar $prod $delCF $threshold $Vg.Vgid $clen.cdr3len1 $cdp.cdr3part $filtin $filtout $summ $Jg.Jgid $Dg.Dgid $clen.cdr3len2 $process_id - - </command> - <inputs> - <param format="txt" name="input" type="data" label="IMGT Summary Output"/> - <param format="txt" name="process_id" type="text" label="Process ID"/> - <param name="TCR_or_BCR" type="select" label="T-cell or B-cell option"> - <option value="TCR">T-cell</option> - <option value="BCR">B-cell</option> - </param> - <param name="Vfun" type="select" label="Only Take Into Account Fuctional V-GENE? "> - <option value="y">yes</option> - <option value="n">no</option> - </param> - - <param name="spChar" type="select" label="Only Take Into Account CDR3 with no Special Characters (X,*,#)? "> - <option value="y">yes</option> - <option value="n">no</option> - </param> - - <param name="prod" type="select" label="Only Take Into Account Productive Sequences? "> - <option value="y">yes</option> - <option value="n">no</option> - </param> - - <param name="delCF" type="select" label="Only Take Into Account CDR3 with valid start/end landmarks? "> - <option value="y">yes</option> - <option value="n">no</option> - </param> - - <param name="threshold" type="float" size="3" value="0" min="0" max="100" label="V-REGION identity %" /> - - <conditional name="Vg"> - - <param name="Vg_select" type="select" label="Select Specific V gene?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param format="txt" name="Vgid" type="text" label="Type V gene"/> - </when> - - <when value="n"> - <param name="Vgid" type="hidden" value="null" /> - </when> - - </conditional> - - <conditional name="Jg"> - - <param name="Jg_select" type="select" label="Select Specific J gene?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param format="txt" name="Jgid" type="text" label="Type J gene"/> - </when> - - <when value="n"> - <param name="Jgid" type="hidden" value="null" /> - </when> - - </conditional> - - <conditional name="Dg"> - - <param name="Dg_select" type="select" label="Select Specific D gene?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param format="txt" name="Dgid" type="text" label="Type D gene"/> - </when> - - <when value="n"> - <param name="Dgid" type="hidden" value="null" /> - </when> - - </conditional> - - - - <conditional name="clen"> - - <param name="clen_select" type="select" label="Select CDR3 length range?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param name="cdr3len1" type="integer" size="3" value="0" min="0" max="100" label="CDR3 Length Lower Threshold" /> - <param name="cdr3len2" type="integer" size="3" value="0" min="0" max="100" label="CDR3 Length Upper Threshold" /> - </when> - - <when value="n"> - <param name="cdr3len1" type="hidden" value="null" /> - <param name="cdr3len2" type="hidden" value="null" /> - </when> - - </conditional> - - <conditional name="cdp"> - - <param name="cdp_select" type="select" label="Only select CDR3 containing specific amino-acid sequence?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param format="txt" name="cdr3part" type="text" label="Type specific amino-acid sequence"/> - </when> - - <when value="n"> - <param name="cdr3part" type="hidden" value="null" /> - </when> - </conditional> - - </inputs> - - - <outputs> - <data name="filtin" format="tabular" label="${process_id}_filterin"/> - <data name="filtout" format="tabular" label="${process_id}_filterout"/> - <data name="summ" format="tabular" label="${process_id}_Summary"/> - - - </outputs> - - - <help> -This tool filters IMGT Summary Data based on a combination of criteria. - </help> - -</tool>
--- a/cmpb2016/pub_clono_CDR3.py Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Dec 21 18:26:01 2015 - -@author: chmaramis -""" - -from __future__ import division -import numpy as np -from pandas import * -from numpy import nan as NA -import sys -import time - - - -def publicCDR3Func(inputs,thres): - - cdr3=DataFrame() - - for x in range(0,len(inputs),2): - cl = DataFrame() - cl = read_csv(inputs[x] , sep = '\t' , index_col = 0) - #tp = read_csv(inp_name, iterator=True, chunksize=5000,sep='\t', index_col=0 ) - #cl = concat([chunk for chunk in tp]) - - if (thres != 'null'): - cl = cl[cl['Reads'] > int(thres)] - - x1 = inputs[x+1].split('_') - - del cl['Reads'] - cl.columns = [cl.columns[0], x1[0]+' '+cl.columns[1], x1[0]+' Relative '+cl.columns[2]] - - if cdr3.empty: - cdr3 = cl - else: - cdr3 = cdr3.merge(cl, how='outer', on='AA JUNCTION') - - - col = cdr3.columns - freqs = col.map(lambda x: 'Frequency' in x) - reads = col.map(lambda x: 'Reads/Total' in x) - - cdr3[col[freqs]] = cdr3[col[freqs]].fillna(0) - cdr3[col[reads]] = cdr3[col[reads]].fillna('0/*') - - cdr3['Num of Patients']= cdr3[col[freqs]].apply(lambda x: np.sum(x != 0), axis=1) - - cdr3 = cdr3[cdr3['Num of Patients'] > 1] - - cdr3.index = range(1,len(cdr3)+1) - - return cdr3 - - -if __name__ == '__main__': - - start=time.time() - - # Parse input arguments - threshold = sys.argv[2] - arg = sys.argv[3:] - output = sys.argv[1] - - # Execute basic function - mer = publicCDR3Func(arg,threshold) - - # Save output to CSV files - if not mer.empty: - mer.to_csv(output , sep = '\t') - - # Print execution time - stop=time.time() - print('Runtime:' + str(stop-start))
--- a/cmpb2016/pub_clono_CDR3.xml Sun Mar 18 07:06:48 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,39 +0,0 @@ -<tool id="pubClonoCDR3" name="Public CDR3 Clonotypes Computation" version="0.9"> -<description>Compute Public CDR3 Clonotypes</description> -<command interpreter="python"> -pub_clono_CDR3.py "$output1" "$Th.thres" -#for x in $clono_files - "$x.clfile" - "$x.clfile.name" -#end for -</command> -<inputs> - <conditional name="Th"> - - <param name="thres_select" type="select" label="Remove CDR3 With Reads Fewer Than Threshold?"> - <option value="y">Yes</option> - <option value="n" selected="true">No</option> - </param> - - <when value="y"> - <param name="thres" type="integer" size="4" value="1" min="1" label="Keep CDR3 with Number of Reads more than"/> - </when> - - <when value="n"> - <param name="thres" type="hidden" value="null" /> - </when> - - </conditional> - <repeat name="clono_files" title="Files to be append" min="2"> - <param name="clfile" type="data" label="File of CDR3 Frequencies" format="tabular"/> - </repeat> - -</inputs> - -<outputs> -<data format="tabular" name="output1" label="Public_CDR3"/> -</outputs> -<help> -This tool computes the public CDR3 clonotypes among a number of patients along with their frequencies for each patient. -</help> -</tool>