extract table

codescripts/extract_tables.py

 import json
 import pandas as pd
+from functools import reduce
 import sys, argparse, os
 
 parser = argparse.ArgumentParser(description="This script is to get information from multiqc and sentieon, output the raw fastq, bam and variants calling (precision and recall) quality metrics")
 parser.add_argument('-fastqscreen', '--fastqscreen', type=str, help='multiqc_fastq_screen.txt',  required=True)
 parser.add_argument('-hap', '--happy', type=str, help='multiqc_happy_data.json',  required=True)
 
+parser.add_argument('-project', '--project_name', type=str, help='project_name',  required=True)
+
 args = parser.parse_args()
 
 # Rename input:
 fastqscreen_file = args.fastqscreen
 hap_file = args.happy
 
+project_name = args.project_name
+
 #############################################
 # fastqc
 fastqc = pd.read_table(fastqc_file)
 
 
 ############################
-dat = pd.read_table(aln_metrics_file)
+dat = pd.read_table(aln_metrics_file,index_col=False)
 dat['PCT_ALIGNED_READS'] = dat["PF_READS_ALIGNED"]/dat["TOTAL_READS"]
 aln_metrics = dat[["Sample", "PCT_ALIGNED_READS","PF_MISMATCH_RATE"]]
 aln_metrics = aln_metrics * 100
+aln_metrics['Sample'] = [x[-1] for x in aln_metrics['Sample'].str.split('/')]
 
-dat = pd.read_table(is_metrics_file)
+dat = pd.read_table(is_metrics_file,index_col=False)
 is_metrics = dat[['Sample', 'MEDIAN_INSERT_SIZE']]
+is_metrics['Sample'] = [x[-1] for x in is_metrics['Sample'].str.split('/')]
 
-dat = pd.read_table(quality_yield_file)
+dat = pd.read_table(quality_yield_file,index_col=False)
 dat['%Q20'] = dat['Q20_BASES']/dat['TOTAL_BASES']
 dat['%Q30'] = dat['Q30_BASES']/dat['TOTAL_BASES']
 quality_yield = dat[['Sample','%Q20','%Q30']]
 quality_yield = quality_yield * 100
+quality_yield['Sample'] = [x[-1] for x in quality_yield['Sample'].str.split('/')]
 
-dat = pd.read_table(wgs_metrics_file)
+dat = pd.read_table(wgs_metrics_file,index_col=False)
 wgs_metrics = dat[['Sample','MEDIAN_COVERAGE','PCT_1X', 'PCT_5X', 'PCT_10X','PCT_30X']]
 wgs_metrics['PCT_1X'] = wgs_metrics['PCT_1X'] * 100
 wgs_metrics['PCT_5X'] = wgs_metrics['PCT_5X'] * 100
 wgs_metrics['PCT_10X'] = wgs_metrics['PCT_10X'] * 100
 wgs_metrics['PCT_30X'] = wgs_metrics['PCT_30X'] * 100
+wgs_metrics['Sample'] = [x[-1] for x in wgs_metrics['Sample'].str.split('/')]
 
 data_frames = [aln_metrics, is_metrics, quality_yield, wgs_metrics]
 post_alignment_dat = reduce(lambda  left,right: pd.merge(left,right,on=['Sample'],how='outer'), data_frames)
+post_alignment_dat.columns = ['Sample', '%Mapping', '%Mismatch Rate', 'Mendelian Insert Size','%Q20', '%Q30', 'Median Coverage', 'PCT_1X', 'PCT_5X', 'PCT_10X','PCT_30X']
+post_alignment_dat = post_alignment_dat.round(2)
+post_alignment_dat.to_csv('post_alignment.txt',sep="\t",index=0)
+
 
-# benchmark
+
+#########################################
+# variants calling
 with open(hap_file) as hap_json:
 	happy = json.load(hap_json)
 dat =pd.DataFrame.from_records(happy)
 dat = dat.loc[:, dat.columns.str.endswith('ALL')]
 dat_transposed = dat.T
-benchmark = dat_transposed.loc[:,['sample_id','METRIC.Precision','METRIC.Recall']]
-benchmark.columns = ['Sample','Precision','Recall']
-
-#output
-fastqc_all.to_csv('fastqc.final.result.txt',sep="\t",index=0)
-fastqscreen.to_csv('fastqscreen.final.result.txt',sep="\t",index=0)
-qualimap_stat.to_csv('qualimap.final.result.txt',sep="\t",index=0)
-benchmark.to_csv('benchmark.final.result.txt',sep="\t",index=0)
-
+dat_transposed = dat_transposed.loc[:,['sample_id','QUERY.TOTAL','METRIC.Precision','METRIC.Recall']]
+indel = dat_transposed[['INDEL' in s for s in dat_transposed.index]]
+snv = dat_transposed[['SNP' in s for s in dat_transposed.index]]
+indel.reset_index(drop=True, inplace=True)
+snv.reset_index(drop=True, inplace=True)
+benchmark = pd.concat([snv, indel], axis=1)
+benchmark = benchmark[["sample_id", 'QUERY.TOTAL', 'METRIC.Precision', 'METRIC.Recall']]
+benchmark.columns = ['Sample','sample_id','SNV number','INDEL number','SNV precision','INDEL precision','SNV recall','INDEL recall']
+benchmark = benchmark[['Sample','SNV number','INDEL number','SNV precision','INDEL precision','SNV recall','INDEL recall']]
+benchmark['SNV precision'] = benchmark['SNV precision'].astype(float)
+benchmark['INDEL precision'] = benchmark['INDEL precision'].astype(float)
+benchmark['SNV recall'] = benchmark['SNV recall'].astype(float)
+benchmark['INDEL recall'] = benchmark['INDEL recall'].astype(float)
+benchmark['SNV precision'] = benchmark['SNV precision'] * 100
+benchmark['INDEL precision'] = benchmark['INDEL precision'] * 100
+benchmark['SNV recall'] = benchmark['SNV recall'] * 100
+benchmark['INDEL recall'] = benchmark['INDEL recall']* 100
+benchmark = benchmark.round(2)
+benchmark.to_csv('variants.calling.qc.txt',sep="\t",index=0)
+
+all_rep = [x.split('_')[6] for x in benchmark['Sample']]
+rep = list(set(all_rep))
+columns = ['Family','Average Precision','Average Recall','Precison SD','Recall SD']
+df_ = pd.DataFrame(columns=columns)
+for i in rep:
+	string = "_" + i + "_"
+	sub_dat = benchmark[benchmark['Sample'].str.contains('_1_')]
+	mean = list(sub_dat.mean(axis = 0, skipna = True))
+	sd = list(sub_dat.std(axis = 0, skipna = True))
+	family_name = project_name + "." + i + ".SNV"
+	df_ = df_.append({'Family': family_name, 'Average Precision': mean[0], 'Average Recall': mean[2], 'Precison SD': sd[0], 'Recall SD': sd[2] }, ignore_index=True)
+	family_name = project_name + "." + i + ".INDEL"
+	df_ = df_.append({'Family': family_name, 'Average Precision': mean[1], 'Average Recall': mean[3], 'Precison SD': sd[1], 'Recall SD': sd[3] }, ignore_index=True)
+df_ = df_.round(2)
+df_.to_csv('precision.recall.txt',sep="\t",index=0)
 
 
 

codescripts/merge_two_family_with_genotype.py

 snv_quartet = snv_family_mendelian/snv_family_all
 indel_quartet = indel_family_mendelian/indel_family_all
 outcolumn =  'Family\tReproducibility_D5_D6\tMendelian_Concordance_Quartet\n'
-indel_outResult = family + '_INDEL' + '\t' + str(indel_sister) + '\t' + str(indel_quartet) + '\n'
-snv_outResult = family + '_SNV' + '\t' + str(snv_sister) + '\t' + str(snv_quartet) + '\n'
+indel_outResult = family + '.INDEL' + '\t' + str(indel_sister) + '\t' + str(indel_quartet) + '\n'
+snv_outResult = family + '.SNV' + '\t' + str(snv_sister) + '\t' + str(snv_quartet) + '\n'
 summary_file.write(outcolumn)
 summary_file.write(indel_outResult)
 summary_file.write(snv_outResult)

tasks/extract_tables.wdl

 	File fastqscreen
 	File hap
 
+	String project
 	String docker
 	String cluster_config
 	String disk_size
 
 	command <<<
 
-
+		python /opt/extract_tables.py -quality ${quality_yield_summary} -depth ${wgs_metrics_summary} -aln ${aln_metrics_summary} -is ${is_metrics_summary} -fastqc ${fastqc} -fastqscreen ${fastqscreen} -hap ${hap} -project ${projecg}
 
 	>>>
 
 	}
 
 	output {
-		File fastqc_result = "fastqc.final.result.txt"
-		File fastqscreen_result = "fastqscreen.final.result.txt"
-		File qualimap_result = "qualimap.final.result.txt"
-		File hap_result = "benchmark.final.result.txt"
+		File pre_alignment = "pre_alignment.txt"
+		File post_alignment = "post_alignment.txt"
+		File variant_calling = "variants.calling.qc.txt"
+		File precision_recall = "precision.recall.txt"
 	}
 }

tasks/merge_sentieon_metrics.wdl

 		cat ${sep=" " aln_metrics_header} | sed -n '1,1p' | cat - ${sep=" " aln_metrics_data} > aln_metrics_all
 		ls ${sep=" " aln_metrics_data} | cut -d '.' -f1 | cat sample_column - | paste - aln_metrics_all > ${project}.aln_metrics.txt
 
-
 		cat ${sep=" " is_metrics_header} | sed -n '1,1p' | cat - ${sep=" " is_metrics_data} > is_metrics_all
 		ls ${sep=" " is_metrics_data} | cut -d '.' -f1 | cat sample_column - | paste - is_metrics_all > ${project}.is_metrics.txt
 

workflow.wdl

 import "./tasks/benchmark.wdl" as benchmark
 import "./tasks/multiqc.wdl" as multiqc
 import "./tasks/merge_sentieon_metrics.wdl" as merge_sentieon_metrics
+import "./tasks/extract_tables.wdl" as extract_tables
 import "./tasks/mendelian.wdl" as mendelian
 import "./tasks/merge_mendelian.wdl" as merge_mendelian
 import "./tasks/quartet_mendelian.wdl" as quartet_mendelian
 import "./tasks/fastqc.wdl" as fastqc
 import "./tasks/fastqscreen.wdl" as fastqscreen
-import "./tasks/extract_tables.wdl" as extract_tables
 import "./tasks/D5_D6.wdl" as D5_D6
 import "./tasks/merge_family.wdl" as merge_family
 
 		fastqc=multiqc.fastqc,
 		fastqscreen=multiqc.fastqscreen,
 		hap=multiqc.hap,
+		project=project,
 		docker=MULTIQCdocker,
 		cluster_config=SMALLcluster_config,
 		disk_size=disk_size