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===3. Haplotype analysis in the UMR regions=== *The previous analysis that I did was focusing on the LMS clusters, which is a small subset of UMRs. Perhaps the target selection was too strict and I might have missed other informative regions. Therefore, I decided to expand the net and search more broadly. *I wrote a script to report all haplotypes in a list of target from a RRBS bam file. I then obtained all haplotypes for all samples within the 49Mb RRBS target regions. /home/kunzhang/CpgMIP/Data/MONOD/[[Media:mergedBam2hapInfo.txt|mergedBam2hapInfo.pl]] /home/kunzhang/CpgMIP/Data/MONOD/MONOD_primary_tumor_RRBS_targets.BED.txt /media/Ext12T/DD_Ext12T/RRBS_MONOD/Bam_Merged/6-P-10.merged.bam >6-P-10.hapInfo.txt & Batch processing script: [[Media: 1407-combined_expanded_step1_batch_command.txt|1407-combined_expanded_step1_batch_command.sh]] *Then I attempted to classify each haplotype as blood unmethylated haplotype or a cancer methylated haplotype, based on the methylation level of the CpG sites within UMR in the healthy plasma. **First I merged all the NC plasma methylFreq files, and created a BED file that represent the average methylation for all the NC plasma samples. Then I use bedtools intersect to extract the sites within blood UMRs. **Second I wrote a script to report the following three numbers for each target region: (i) the number of haplotypes; (ii) the number of methylated haplotypes; (iii) the sum negative log-p of all haplotypes. All these numbers for one sample are reported in one regionHap.txt file. **Finally I wrote another script to combine the regionHap.txt files for multiple samples, and generate two matrix: HMH_load (the fraction of methylated haplotypes, a measurement of cancer load); HMH_NLP (the sum negative log-p, an indicator for significance) /home/kunzhang/CpgMIP/Data/MONOD/regionMethHapAnalysis.pl /home/kunzhang/CpgMIP/Data/MONOD/MONOD_nc_plasma_RRBS_combined_UMRs.BED.txt 6-P-10.hapInfo.txt>6-P-10.regionHap.txt Batch processing script: [[Media: 1407-combined_expanded_step2_batch_command.txt|1407-combined_expanded_step2_batch_command.sh]] ../../report_regionHap_matrix.pl UCSD_MCC_PancreasCancer PC-P-10.regionHap.txt PC-P-1.regionHap.txt PC-P-2.regionHap.txt PC-P-3.regionHap.txt PC-P-4.regionHap.txt PC-P-5.regionHap.txt PC-P-6.regionHap.txt PC-P-7.regionHap.txt PC-P-8.regionHap.txt PC-P-9.regionHap.txt PC-T-1_1.regionHap.txt PC-T-1_2.regionHap.txt PC-T-2_1.regionHap.txt PC-T-2_2.regionHap.txt PC-T-4_1.regionHap.txt PC-T-4_2.regionHap.txt PC-T-6_1.regionHap.txt PC-T-6_2.regionHap.txt PC-T-7_1.regionHap.txt PC-T-7_2.regionHap.txt ../../report_regionHap_matrix.pl UCSD_MCC_LungCancer 7-P-10.regionHap.txt 7-P-1.regionHap.txt 7-P-2.regionHap.txt 7-P-3.regionHap.txt 7-P-4.regionHap.txt 7-P-5.regionHap.txt 7-P-6.regionHap.txt 7-P-7.regionHap.txt 7-P-8.regionHap.txt 7-P-9.regionHap.txt 7-T-1_1.regionHap.txt 7-T-1_2.regionHap.txt 7-T-2_1.regionHap.txt 7-T-2_2.regionHap.txt 7-T-3_1.regionHap.txt 7-T-3_2.regionHap.txt 7-T-4_1.regionHap.txt 7-T-4_2.regionHap.txt 7-T-5_1.regionHap.txt 7-T-5_2.regionHap.txt ../../report_regionHap_matrix.pl UCSD_MCC_ColonCancer 6-P-10.regionHap.txt 6-P-1.regionHap.txt 6-P-2.regionHap.txt 6-P-3.regionHap.txt 6-P-4.regionHap.txt 6-P-5.regionHap.txt 6-P-6.regionHap.txt 6-P-7.regionHap.txt 6-P-8.regionHap.txt 6-P-9.regionHap.txt 6-T-1_1.regionHap.txt 6-T-1_2.regionHap.txt 6-T-2_1.regionHap.txt 6-T-2_2.regionHap.txt 6-T-3_1.regionHap.txt 6-T-3_2.regionHap.txt 6-T-4_1.regionHap.txt 6-T-4_2.regionHap.txt 6-T-5_1.regionHap.txt 6-T-5_2.regionHap.txt ../../report_regionHap_matrix.pl Plasma 6-P-10.regionHap.txt 6-P-1.regionHap.txt 6-P-2.regionHap.txt 6-P-3.regionHap.txt 6-P-4.regionHap.txt 6-P-5.regionHap.txt 6-P-6.regionHap.txt 6-P-7.regionHap.txt 6-P-8.regionHap.txt 6-P-9.regionHap.txt 7-P-10.regionHap.txt 7-P-1.regionHap.txt 7-P-2.regionHap.txt 7-P-3.regionHap.txt 7-P-4.regionHap.txt 7-P-5.regionHap.txt 7-P-6.regionHap.txt 7-P-7.regionHap.txt 7-P-8.regionHap.txt 7-P-9.regionHap.txt NC-P-1.regionHap.txt NC-P-2.regionHap.txt NC-P-3.regionHap.txt NC-P-5.regionHap.txt NC-P-6.regionHap.txt NC-P-7.regionHap.txt NC-P-8.regionHap.txt NC-P-9.regionHap.txt PC-P-10.regionHap.txt PC-P-1.regionHap.txt PC-P-2.regionHap.txt PC-P-3.regionHap.txt PC-P-4.regionHap.txt PC-P-5.regionHap.txt PC-P-6.regionHap.txt PC-P-7.regionHap.txt PC-P-8.regionHap.txt PC-P-9.regionHap.txt ../../report_regionHap_matrix.pl Primary_tumor 6-T-1_1.regionHap.txt 6-T-1_2.regionHap.txt 6-T-2_1.regionHap.txt 6-T-2_2.regionHap.txt 6-T-3_1.regionHap.txt 6-T-3_2.regionHap.txt 6-T-4_1.regionHap.txt 6-T-4_2.regionHap.txt 6-T-5_1.regionHap.txt 6-T-5_2.regionHap.txt 7-T-1_1.regionHap.txt 7-T-1_2.regionHap.txt 7-T-2_1.regionHap.txt 7-T-2_2.regionHap.txt 7-T-3_1.regionHap.txt 7-T-3_2.regionHap.txt 7-T-4_1.regionHap.txt 7-T-4_2.regionHap.txt 7-T-5_1.regionHap.txt 7-T-5_2.regionHap.txt PC-T-1_1.regionHap.txt PC-T-1_2.regionHap.txt PC-T-2_1.regionHap.txt PC-T-2_2.regionHap.txt PC-T-4_1.regionHap.txt PC-T-4_2.regionHap.txt PC-T-6_1.regionHap.txt PC-T-6_2.regionHap.txt PC-T-7_1.regionHap.txt PC-T-7_2.regionHap.txt CTT-FFPE-100ng_1.regionHap.txt CTT-FFPE-100ng_2.regionHap.txt CTT-FFPE-5ng_1.regionHap.txt CTT-FFPE-5ng_2.regionHap.txt CTT-frozen-100ng_1.regionHap.txt CTT-frozen-100ng_2.regionHap.txt CTT-frozen-5ng_1.regionHap.txt CTT-frozen-5ng_2.regionHap.txt *To take a quick look at the HMH load in the primary tumors, I took the Primary_tumor.HMH_load.matrix file, manually filtered out the regions that have average load below 0.1 (just to reduce the number of regions from 20k+ to ~5k. Then made a heatmap with R (note that I removed the Biochain CTT samples to avoid variability of sample source). Based on the heatmap, there seems to be a decent level of heterogeneity even for the samples of the same tumor types. library(gplots) x=read.table("MCC_Primary_tumor_mean_0.1_up.HMH_load.matrix.txt",header=TRUE,row.names=1); heatmap.2(as.matrix(x), col=bluered(128), scale="none", cexCol=0.8, cexRow=0.1 ,key=T, symkey=F,density.info="histogram",trace="none",dendrogram="both") [[Image:MCC_primary_tumor_mean_0.1_up_heatmap.png|800px]]
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