| Next changeset 1:d8547ff82697 (2016-03-18) |
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classifier/._.DS_Store classifier/._classifier.py classifier/._eqtl_genes_positions_plot.txt classifier/._readme.txt classifier/classifier.py classifier/classifier.xml classifier/eqtl_genes_positions_plot.txt classifier/readme.txt classifier/test-data/._.DS_Store classifier/test-data/input/._.DS_Store classifier/test-data/input/._chr_summary.txt classifier/test-data/input/._eqtl.txt classifier/test-data/input/._gene_positions_sense_antisense_EST_gDNA.txt classifier/test-data/input/._lookup.txt classifier/test-data/input/chr_summary.txt classifier/test-data/input/eqtl.txt classifier/test-data/input/gene_positions_sense_antisense_EST_gDNA.txt classifier/test-data/input/lookup.txt classifier/test-data/output/._.DS_Store classifier/test-data/output/._all_classification_test.txt classifier/test-data/output/._chr_summary_v2.txt classifier/test-data/output/._cis_classification_test.txt classifier/test-data/output/._classification_summary_test.txt classifier/test-data/output/._eQTL_gene_position_plot.pdf classifier/test-data/output/._eQTL_per_gene_summary.txt classifier/test-data/output/._gene_pos_v2.txt classifier/test-data/output/._trans_classification_test.txt classifier/test-data/output/all_classification_test.txt classifier/test-data/output/chr_summary_v2.txt classifier/test-data/output/cis_classification_test.txt classifier/test-data/output/classification_summary_test.txt classifier/test-data/output/eQTL_gene_position_plot.pdf classifier/test-data/output/eQTL_per_gene_summary.txt classifier/test-data/output/gene_pos_v2.txt classifier/test-data/output/trans_classification_test.txt classifier/tool_dependencies.xml |
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| diff -r 000000000000 -r ef9c2044d86a classifier/classifier.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/classifier.py Fri Mar 18 05:15:29 2016 -0400 |
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| b'@@ -0,0 +1,597 @@\n+"""\n+@summary: Classify eQTLs as cis or trans - calculate "hybrid" overlap score\n+@author: nanette.coetzer@gmail.com\n+@version 5\n+\n+"""\n+import optparse, sys\n+import tempfile\n+import os, re\n+import subprocess\n+\n+def stop_err( msg ):\n+ sys.stderr.write( "%s\\n" % msg )\n+ sys.exit()\n+ \n+def __main__():\n+ #Parse Command Line\n+ parser = optparse.OptionParser()\n+ parser.add_option("-m", "--rscript", default=None, dest="rscript", \n+ help="R script")\n+ parser.add_option("-i", "--input1", default=None, dest="input1", \n+ help="eQTLs file")\n+ parser.add_option("-j", "--input2", default=None, dest="input2", \n+ help="Chr summary file")\n+ parser.add_option("-k", "--input3", default=None, dest="input3", \n+ help="Gene positions file")\n+ parser.add_option("-l", "--input4", default=None, dest="input4", \n+ help="Lookup file")\n+ \n+ #parser.add_option("-m", "--score_type", default=2, dest="score_type", \n+ # help="Select dM scale (1) or hybrid (2)")\n+\n+ parser.add_option("-o", "--output1", default=None, dest="output1", \n+ help="All classification file")\n+ parser.add_option("-p", "--output2", default=None, dest="output2", \n+ help="Cis classification file")\n+ parser.add_option("-q", "--output3", default=None, dest="output3", \n+ help="Trans classification file")\n+ parser.add_option("-r", "--output4", default=None, dest="output4", \n+ help="Classification summary file")\n+ parser.add_option("-s", "--output5", default=None, dest="output5", \n+ help="Chr summary file v2")\n+ parser.add_option("-t", "--output6", default=None, dest="output6", \n+ help="Gene positions file v2")\n+ parser.add_option("-u", "--output7", default=None, dest="output7", \n+ help="eQTLs per gene summary file")\n+ parser.add_option("-v", "--output8", default=None, dest="output8", \n+ help="eQTL vs gene position plot")\n+ (options, args) = parser.parse_args()\n+ \n+ try:\n+ open(options.input1, "r").close()\n+ except TypeError, e:\n+ stop_err("You need to supply the eQTL file:\\n" + str(e))\n+ except IOError, e:\n+ stop_err("Can not open the eQTL file:\\n" + str(e))\n+\n+ try:\n+ open(options.input2, "r").close()\n+ except TypeError, e:\n+ stop_err("You need to supply the Chr summary file:\\n" + str(e))\n+ except IOError, e:\n+ stop_err("Can not open the Chr summary file:\\n" + str(e))\n+ \n+ try:\n+ open(options.input3, "r").close()\n+ except TypeError, e:\n+ stop_err("You need to supply the Gene positions file:\\n" + str(e))\n+ except IOError, e:\n+ stop_err("Can not open the Gene positions file:\\n" + str(e))\n+ \n+ try:\n+ open(options.input4, "r").close()\n+ except TypeError, e:\n+ stop_err("You need to supply the Lookup file:\\n" + str(e))\n+ except IOError, e:\n+ stop_err("Can not open the Lookup file:\\n" + str(e))\n+\n+ ##############################################\n+ lookup_list = []\n+ \n+ # Convert eQTL input file, change marker number of last interval in each chromosome\n+ check2 = ""\n+ prev_m = "0"\n+ prev_c = "1"\n+ r = re.compile("^(\\d+)\\s+(\\d+)\\s+(\\d+.\\d+)\\s+")\n+ indict = {}\n+ max_cM_dict = {}\n+ lookup = open(options.input4,\'r\')\n+ for line in lookup:\n+ l = line.strip().split("\\t")\n+ if l[6] == "0":\n+ #print l\n+ max_cM_dict[l[1]] = l[4]\n+ lookup_list.append(l)\n+ if l[0] != "id" and l[6] != "0":\n+ c = l[1]\n+ if prev_c != c:\n+ indict[prev_c+"\\t"+str(float(p))] = prev_c+"\\t"+m+"\\t"+str(float(p))\n+ m = l[2]\n+ p = l[3]\n+ prev_c = c\n+ #print max_cM_dict\n+ \n+ #a'..b'ene_dict[g][4])+"\\t"+str(gene_dict[g][5])+"\\t"+str(gene_dict[g][6])+"\\t"+str(gene_bin_dict[g])+"\\n")\n+ # num all eQTL\n+ if int(gene_dict[g][3]) != 0:\n+ genes_with_eqtl += 1\n+ eqtl_count += int(gene_dict[g][3])\n+ # num cis eQTL\n+ if int(gene_dict[g][4]) != 0:\n+ genes_with_cis += 1\n+ cis_count += int(gene_dict[g][4])\n+ # num trans eQTL\n+ if int(gene_dict[g][5]) != 0:\n+ genes_with_trans += 1\n+ trans_count += int(gene_dict[g][5])\n+ if (int(gene_dict[g][4]) != 0) and (int(gene_dict[g][5]) == 0):\n+ only_cis += 1\n+ if (int(gene_dict[g][5]) != 0) and (int(gene_dict[g][4]) == 0):\n+ only_trans += 1\n+ if (int(gene_dict[g][5]) != 0) and (int(gene_dict[g][4]) != 0):\n+ cis_and_trans += 1\n+ if (int(gene_dict[g][5]) != 0) or (int(gene_dict[g][4]) != 0):\n+ cis_or_trans += 1\n+ \n+ ave_num_eqtl_per_gene = eqtl_count/float(genes_with_eqtl)\n+ ave_num_cis_eqtl_per_gene = cis_count/float(genes_with_cis)\n+ ave_num_trans_eqtl_per_gene = trans_count/float(genes_with_trans)\n+ #print "Average number of eQTLs per gene with eQTL\\t"+str(round(ave_num_eqtl_per_gene,1))\n+ #print "Average number of cis eQTLs per gene with cis eQTL\\t"+str(round(ave_num_cis_eqtl_per_gene,1))\n+ #print "Average number of trans eQTLs per gene with trans eQTL\\t"+str(round(ave_num_trans_eqtl_per_gene,1))\n+ #print "Number of genes with only cis eQTL (no trans)\\t"+str(only_cis)\n+ #print "Number of genes with only trans eQTL (no cis)\\t"+str(only_trans)\n+ #print "Number of genes with cis and trans eQTL\\t"+str(cis_and_trans)\n+ #print "Number of genes with cis or trans eQTL\\t"+str(cis_or_trans)\n+ \n+ eqtl_per_gene = open(options.output7,\'w\')\n+ eqtl_per_gene.write("Average number of eQTLs per gene with eQTL\\t"+str(round(ave_num_eqtl_per_gene,1))+"\\n")\n+ eqtl_per_gene.write("Average number of cis eQTLs per gene with cis eQTL\\t"+str(round(ave_num_cis_eqtl_per_gene,1))+"\\n")\n+ eqtl_per_gene.write("Average number of trans eQTLs per gene with trans eQTL\\t"+str(round(ave_num_trans_eqtl_per_gene,1))+"\\n")\n+ eqtl_per_gene.write("Number of genes with only cis eQTL (no trans)\\t"+str(only_cis)+" ("+str(round((int(only_cis)/float(cis_or_trans))*100,1))+"%)\\n")\n+ eqtl_per_gene.write("Number of genes with only trans eQTL (no cis)\\t"+str(only_trans)+" ("+str(round((int(only_trans)/float(cis_or_trans))*100,1))+"%)\\n")\n+ eqtl_per_gene.write("Number of genes with cis and trans eQTL\\t"+str(cis_and_trans)+" ("+str(round((int(cis_and_trans)/float(cis_or_trans))*100,1))+"%)\\n")\n+ eqtl_per_gene.write("Number of genes with cis or trans eQTL\\t"+str(cis_or_trans)+" ("+str(round((int(cis_and_trans)/float(cis_and_trans))*100,1))+"%)\\n")\n+ eqtl_per_gene.close()\n+ \n+ gene_pos_v2.close()\n+ \n+ # Create temp direcotry\n+ tempdir = tempfile.mkdtemp()\n+\n+ # copy INPUT file to the temp directory\n+ s = "cp %s %s/all_classification.txt" %(options.output1, tempdir)\n+ subprocess.call(s, shell=True)\n+ s = "cp %s %s/lookup.txt" %(options.input4, tempdir)\n+ subprocess.call(s, shell=True)\n+\n+ # create R script => save in temp directory\n+ # generate new header \n+ new_script = open(tempdir+"/new_script.r","w")\n+ header = "setwd(\\"%s\\")" %tempdir\n+ new_script.write(header+"\\n")\n+ # add script body\n+ script = open(options.rscript,"r")\n+ for line in script:\n+ new_script.write(line.strip()+"\\n")\n+ new_script.close()\n+ \n+ # run R script from temp directory\n+ s = "R CMD BATCH %s/new_script.r out.txt" %tempdir\n+ subprocess.call(s, shell=True)\n+ \n+ # run R script from temp directory\n+ s = "R CMD BATCH %s/new_script.r out.txt" %tempdir\n+ subprocess.call(s, shell=True)\n+ \n+ os.system("mv %s/Rplot_eQTL_genes_positions.pdf %s" %(tempdir,options.output8))\n+ \n+if __name__=="__main__": \n+ __main__()\n+\n+\n+\n+\n' |
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| diff -r 000000000000 -r ef9c2044d86a classifier/classifier.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/classifier.xml Fri Mar 18 05:15:29 2016 -0400 |
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| b'@@ -0,0 +1,167 @@\n+<tool id="classifier5" name="Classify eQTLs" version="5.0.0">\n+\t<description> as cis or trans</description>\n+\t<command interpreter="python">\n+ classifier.py --rscript \\$R_SCRIPT_PATH/classifier/eqtl_genes_positions_plot.txt --input1 $input1 --input2 $input2 --input3 $input3 --input4 $input4 --output1 $output1 --output2 $output2 --output3 $output3 --output4 $output4 --output5 $output5 --output6 $output6 --output7 $output7 --output8 $output8\n+\t</command>\n+ <inputs>\n+ <param label="eQTL results file" name="input1" type="data" format="tabular" help="A tabular file with the mapped eQTLs and its associated statistics"></param>\n+\t <param label="Chr summary file" name="input2" type="data" format="tabular" help="A tabular file with a data summary per chromosome (bp)"></param>\n+\t <param label="Gene positions file" name="input3" type="data" format="tabular" help="A tabular file with the positions (bp) of each gene"></param>\n+ <param label="Lookup table file" name="input4" type="data" format="tabular" help="A tabular file with cM and bp positions for each interval"></param>\n+ </inputs>\n+\t<outputs>\n+ <data format="tabular" name="output1" />\n+\t\t<data format="tabular" name="output2" />\n+\t\t<data format="tabular" name="output3" />\n+\t\t<data format="tabular" name="output4" />\n+\t\t<data format="tabular" name="output5" />\n+\t\t<data format="tabular" name="output6" />\n+\t\t<data format="tabular" name="output7" />\n+\t\t<data format="pdf" name="output8" />\n+\t</outputs>\n+\t<requirements>\n+\t\t<requirement type="set_environment">R_SCRIPT_PATH</requirement>\n+\t</requirements>\n+\t<tests>\n+ <test>\n+ </test>\n+\t</tests>\n+\t<help>\n+\t\t\n+**What it does**\n+\n+Calculates the average genetic interval size across all eQTLs.\n+\n+Classifies an eQTL as \'cis\' if it maps within half the above mentioned interval size of the gene exhibiting the eQTL.\n+\n+Classifies an eQTL as \'trans\' if it maps to a different region on the genome than the location of the gene exhibiting the eQTL (further away than half the above mentioned interval size from the gene).\n+\n+Classifies an eQTL as \'no_result\' if the location of the target gene is not known. \n+\n+-------\n+\n+**Example input files**\n+\n+eQTL results file, each row correspond to an eQTL (21 columns; only a part of the file is shown)::\n+\n+ trait_name trait_number eQTL_number chr peak_marker\tpeak_position\tpeak_LR\t\tpeak_LOD\tR2\t\tTR2\t\tS\t\tadditive dominance LOD1_L_m\tLOD1_L_pos LOD1_R_m\tLOD1_R_pos LOD2_L_m\tLOD2_L_pos LOD2_R_m\tLOD2_R_pos\n+ geneA\t 106\t\t 2\t 10\t 4\t \t0.5206\t\t13.0002477\t2.821053751\t0.1067186\t0.2802598\t2741.216084\t-80.0805117\t0\t3\t0.4045\t \t5\t0.6791\t\t3\t0.3583\t\t5\t0.7505\n+ geneB\t 434\t\t 3\t 6\t 3\t\t0.1455\t\t13.000651\t2.821141267\t0.0881461\t0.3710748\t38.650035\t502.7692948\t0\t2\t0.0847\t\t3\t0.2153\t\t1\t0.0112\t\t3\t0.2763\n+ geneC\t 343\t\t 2\t 4\t 10\t\t1.1039\t\t13.0012249\t2.821265803\t0.1168611\t0.3068127\t42.9667077\t-101.8310204\t0\t10\t1.0217\t\t10\t1.1078\t\t9\t0.9838\t\t10\t1.1118\n+ geneD\t 384\t\t 1\t 1\t 19\t\t2.3414\t\t13.0022994\t2.82149897\t0.1372476\t0.1985604\t2.1933164\t-688.0268455\t0\t19\t2.1956\t\t20\t2.4956\t\t19\t2.0883\t\t20\t2.5488\n+ geneD\t 267\t\t 2\t 9\t 8\t\t1.2052\t\t13.0026682\t2.821578999\t0.0862225\t0.3794662\t55.4157254\t278.1351403\t0\t7\t1.2023\t\t8\t1.2277\t\t7\t1.1994\t\t8\t1.2466\n+\n+\n+Chromosome summary file, each row correspond to a chromosome (6 columns; only a part of the file is shown). The last row gives the total across the genome::\n+\n+ chr\tmarkers\tcM\tbp\tint_positions\tbins\n+ 1\t27\t324.4\t301354135\t177\t176\n+ 2\t14\t169.11\t237068873\t92\t91\n+ 3\t19\t221.29\t232140174\t123\t122\n+ 4\t20\t188.37\t241473504\t105\t104\n+ 5\t20\t203.82\t217872852\t110\t109\n+ 6\t17\t195.85\t169174353\t106\t105\n+ Total \t117\t1302.84\t1399083891\t713\t707\n+ \n+Gene positions file, each row correspond to a gene (4 columns; only a part of the file is shown)::\n+\n+ gene chr start_bp end_bp\n+ geneA\t1\t33214735\t33217244\n+ geneB\t2\t216416829\t216433258\n+ geneC\t6\t162556092\t162559012\n+ geneD\t4\t197750322\t19'..b'2\t0.3181244\t0.3909106 parentB no_result\t904 \t904\n+ geneI\t8\t9\t7\t1.0982\t\t9\t1.3079\t\t8\t1.2052\t\t20.3966235\t0.1305025\t0.4233788\tparentA cis\t\t977 \t969\n+ \n+eQTL cis classification file, each row correspond to a cis eQTL (16 columns; only a part of the file is shown)::\n+\n+ gene\tindex chr start_marker start_int end_marker\t end_int peak_marker\tpeak_int\tpeakLR\t\trsq\t\trtsq\tparent_up_reg classification\t eQTL_bin gene_bin\n+ geneC\t3\t9\t8\t1.2052\t\t8\t1.2452\t\t8\t1.2052\t\t16.6822024\t0.1244943\t0.314542\tparentA cis\t\t917 \t920\n+ geneD\t4\t9\t1\t0.0001\t\t2\t0.2395\t\t1\t0.1201\t\t19.531317\t0.1753893\t0.4300621\tparentA cis\t\t860 \t862\n+ geneI\t8\t9\t7\t1.0982\t\t9\t1.3079\t\t8\t1.2052\t\t20.3966235\t0.1305025\t0.4233788\tparentA cis\t\t977 \t969\n+\n+eQTL trans classification file, each row correspond to a trans eQTL (16 columns; only a part of the file is shown)::\n+\n+ gene\tindex chr start_marker start_int end_marker\t end_int peak_marker\tpeak_int\tpeakLR\t\trsq\t\trtsq\tparent_up_reg classification\t eQTL_bin gene_bin\n+ geneA 1\t6\t13\t1.5139\t\t15\t1.6431\t\t13\t1.5539\t\t12.7532485\t0.1337606\t0.3630217\tparentA trans\t691 \t \t800\n+ geneC \t2\t9\t5\t0.8106\t\t6\t0.9614\t\t6\t0.9214\t\t20.344489\t0.1559524\t0.3123026\tparentB trans\t902 \t700\n+ geneH\t5\t1\t1\t0.0001\t\t1\t0.1001\t\t1\t0.0001\t\t19.5727096\t0.1373944\t0.392982\tparentB trans\t939 \t465\n+ geneH\t6\t1\t9\t1.0268\t\t11\t1.2164\t\t10\t1.1261\t\t13.5560176\t0.095168\t0.4823061\tparentB trans\t1000 \t465\n+ \n+Classification summary file, each row correspond to a class (6 columns)::\n+\n+ class number_eQTLs percentage_eQTLs\taverage_peakLR average_rsq average_rtsq\n+ cis\t \t4712\t\t14.93%\t\t36.0 \t0.29 \t0.47\n+ trans\t \t20726\t\t65.69%\t\t36.0 \t0.29 \t0.47\n+ no_result\t6111\t\t19.369%\t\t20.1 \t0.16 \t0.39\n+ total\t \t31549\t\t100.0%\t\t19.5 \t0.16 \t0.38\n+ \n+Chromosome summary v2 file, each row correspond to a chromosome (11 columns; only a part of the file is shown). The last row gives the total across the genome::\n+\n+ chr markers cM \tbp interval.positions bins genes cis eQTL trans eQTL unknown eQTL all eQTL\n+ 1 27 324.4 \t301354135 177 \t176 5185 782 \t3209 761 \t4752\n+ 2 14 169.11 \t237068873 92 \t91 3782 512 \t1897 510 \t \t2919\n+ 3 19 221.29 \t232140174 123 \t122 3608 469 \t2098 614 \t3181\n+ 4 20 188.37 \t241473504 105 \t104 3389 493 \t2006 491 \t2990\n+ 5 20 203.82 \t217872852 110 \t109 3964 657 \t3077 762 \t4496\n+ 6 17 195.85 \t169174353 106 \t105 2744 413 \t \t1933 516 \t2862\n+ Total 117 1302.84\t1399083891\t713\t\t707\t22672\t3326\t\t14220\t3654\t\t21200\n+\n+Gene positions v2 file, each row correspond to a gene (9 columns; only a part of the file is shown)::\n+\n+ gene\tchr\tstart_bp\tend_bp\t num_eQTL num_cis_eQTL num_trans_eQTL num_unknown_eQTL\tgene_bin\n+ geneA 6 155513712 155518148 1 0 \t1 \t0 \t682\n+ geneB 4 230729005 230729064 0 0 \t0 \t0 \t472\n+ geneC 2 172852270 172853086 2 0 \t1 \t1 \t229\n+ geneD 1 282744902 282749375 3 0 \t3 \t0 \t154\n+ geneE 2 6556394 \t 6560322 \t 0 0 \t0 \t0 \t189\n+ \n+eQTL per gene summary file (2 columns)::\n+\n+ Average number of eQTLs per gene with eQTL \t 2.4\n+ Average number of cis eQTLs per gene with cis eQTL 1.0\n+ Average number of trans eQTLs per gene with trans eQTL 1.8\n+ Number of genes with only cis eQTL (no trans) \t 1402 (8.5%)\n+ Number of genes with only trans eQTL (no cis) \t 11042 (66.7%)\n+ Number of genes with cis and trans eQTL \t \t 4121 (24.9%)\n+ Number of genes with cis or trans eQTL \t\t 16565 (100.0%)\n+\n+\n+eQTL vs gene position plot (in pdf format, produced using R).\n+\n+ </help>\n+</tool>\n' |
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| diff -r 000000000000 -r ef9c2044d86a classifier/eqtl_genes_positions_plot.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/eqtl_genes_positions_plot.txt Fri Mar 18 05:15:29 2016 -0400 |
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| @@ -0,0 +1,32 @@ + +#setwd("/Users/nanettecoetzer/Documents/Bioinformatics/MAIZE_project/eQTL_pipeline/April_2012_pipeline_scripts/July_2012/output_July") +eqtl <- read.table("all_classification.txt",sep="\t",header=TRUE) +lookup <- read.table("lookup.txt",sep="\t",header=TRUE) + +trans_eqtl_bins <- eqtl[eqtl[,14]=="trans",15] +trans_gene_bins <- eqtl[eqtl[,14]=="trans",16] +cis_eqtl_bins <- eqtl[eqtl[,14]=="cis",15] +cis_gene_bins <- eqtl[eqtl[,14]=="cis",16] + +# cis and trans in colour + +pdf("Rplot_eQTL_genes_positions.pdf") +plot(trans_eqtl_bins, trans_gene_bins, type="p", pch=".", cex=2, main="", xlab="eQTL position", ylab="Gene position",col="green", xaxt='n', yaxt='n') +points(cis_eqtl_bins, cis_gene_bins, type="p", pch=".", cex=2, col="blue") + +chr.nr = max(lookup[,2]) +chr.mid = c() +chr.sides = c(0) +chr_labels = c() +for (c in 1:chr.nr) { + chr_labels <- c(chr_labels,paste("chr",c,sep="")) + pos <- length(lookup[lookup[,2]==c,1])/2 + end <- max(lookup[lookup[,2]==c,1]) + chr.mid = c(chr.mid,pos+min(lookup[lookup[,2]==c,1])-1) + chr.sides = c(chr.sides, end) +} +abline(v=chr.sides, lty=3, lwd=2) + +axis(side=1, at=chr.mid, labels= chr_labels) +dev.off() + |
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| diff -r 000000000000 -r ef9c2044d86a classifier/readme.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/readme.txt Fri Mar 18 05:15:29 2016 -0400 |
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| @@ -0,0 +1,27 @@ +====================================================================== +classification +====================================================================== +### This is the second tool in the eQTL backend pipeline: +lookup, classification, frequency, sliding window frequency, hotspots, GO enrichment + +Link to the workflow (for import into Galaxy): http://chewbacca.bi.up.ac.za:8080/u/nanette/w/back-end-workflow-2 + +Calculates the average genetic interval size across all eQTLs. + +Classifies an eQTL as 'cis' if it maps within half the above mentioned interval size of the gene exhibiting the eQTL. + +Classifies an eQTL as 'trans' if it maps to a different region on the genome than the location of the gene exhibiting the eQTL (further away than half the above mentioned interval size from the gene). + +Classifies an eQTL as 'no_result' if the location of the target gene is not known. + +--------------- +Installation +--------------- + +The eQTL backend pipeline is available for: +* command line usage +* integration into Galaxy servers + + +Requirements: Python 2.7 + R 3.1.1 \ No newline at end of file |
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| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/input/chr_summary.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/input/chr_summary.txt Fri Mar 18 05:15:29 2016 -0400 |
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| @@ -0,0 +1,12 @@ +chr markers cM bp interval positions bins +1 27 324.4 301354135 177 176 +2 14 169.11 237068873 92 91 +3 19 221.29 232140174 123 122 +4 20 188.37 241473504 105 104 +5 20 203.82 217872852 110 109 +6 17 195.85 169174353 106 105 +7 12 111.16 176764762 63 62 +8 14 141.31 175793759 77 76 +9 11 158.78 156750706 85 84 +10 13 147.48 150189435 81 80 +Total 167 1861.57 2058582553 1019 1009 |
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| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/input/gene_positions_sense_antisense_EST_gDNA.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/input/gene_positions_sense_antisense_EST_gDNA.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
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|
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/input/lookup.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/input/lookup.txt Fri Mar 18 05:15:29 2016 -0400 |
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| b'@@ -0,0 +1,20207 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|
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/chr_summary_v2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/chr_summary_v2.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
| @@ -0,0 +1,12 @@ +chr markers cM bp interval positions bins genes cis eQTL trans eQT unknown eQTL all eQTL +1 27 324.4 301354135 177 176 5185 759 1954 470 3183 +2 14 169.11 237068873 92 91 3782 426 1127 319 1872 +3 19 221.29 232140174 123 122 3608 430 1112 310 1852 +4 20 188.37 241473504 105 104 3389 458 1252 325 2035 +5 20 203.82 217872852 110 109 3964 629 1834 465 2928 +6 17 195.85 169174353 106 105 2744 349 787 229 1365 +7 12 111.16 176764762 63 62 2721 375 832 207 1414 +8 14 141.31 175793759 77 76 2869 465 1076 277 1818 +9 11 158.78 156750706 85 84 2416 264 1142 260 1666 +10 13 147.48 150189435 81 80 2228 325 1423 325 2073 +Total 167 1861.57 2058582553 1019 1009 32906 4480 12539 3187 20206 |
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/cis_classification_test.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/cis_classification_test.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
| b'@@ -0,0 +1,4481 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|
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/classification_summary_test.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/classification_summary_test.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
| @@ -0,0 +1,5 @@ +class number_eQTLs percentage_eQTLs average_peakLR average_rsq average_rtsq +cis 4480 22.17% 36.4 0.3 0.5 +trans 12539 62.06% 18.2 0.15 0.42 +unknown 3187 15.77% 23.5 0.2 0.46 +all 20206 100.0% 23.1 0.19 0.44 |
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/eQTL_gene_position_plot.pdf |
| b |
| Binary file classifier/test-data/output/eQTL_gene_position_plot.pdf has changed |
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/eQTL_per_gene_summary.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/eQTL_per_gene_summary.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
| @@ -0,0 +1,7 @@ +Average number of eQTLs per gene with eQTL 1.9 +Average number of cis eQTLs per gene with cis eQTL 1.0 +Average number of trans eQTLs per gene with trans eQTL 1.5 +Number of genes with only cis eQTL (no trans) 2244 (20.8%) +Number of genes with only trans eQTL (no cis) 6316 (58.6%) +Number of genes with cis and trans eQTL 2218 (20.6%) +Number of genes with cis or trans eQTL 10778 (100.0%) |
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/gene_pos_v2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/gene_pos_v2.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
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|
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/test-data/output/trans_classification_test.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/test-data/output/trans_classification_test.txt Fri Mar 18 05:15:29 2016 -0400 |
| b |
| b'@@ -0,0 +1,12540 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|
| b |
| diff -r 000000000000 -r ef9c2044d86a classifier/tool_dependencies.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/classifier/tool_dependencies.xml Fri Mar 18 05:15:29 2016 -0400 |
| b |
| @@ -0,0 +1,6 @@ +<tool_dependency> + <set_environment version="1.0"> + <environment_variable name="R_SCRIPT_PATH" action="set_to">$REPOSITORY_INSTALL_DIR</environment_variable> + </set_environment> +</tool_dependency> + |