Dinh/Dinh 2014/NOTES/2014-9-15: Difference between revisions

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* Tumor deconvolution paper: http://genomebiology.com/2014/15/8/419
* Tumor deconvolution paper: http://genomebiology.com/2014/15/8/419
* Python package: https://pypi.python.org/pypi/MethylPurify/2.0-20140819
* Python package: https://pypi.python.org/pypi/MethylPurify/2.0-20140819
* Installed MethylPurify on genome-miner. The scripts dont take much memory & finishes relatively quickly.
* Installed MethylPurify on genome-miner. The scripts dont take much memory & finish relatively quickly.
== Test run with WGBS data and BSMAP aligner ==
== Test run with WGBS data and BSMAP aligner ==
* I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio.
* I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio.
Line 64: Line 64:
   [[File:lung_cancer_methylpurify_informativeCGIs_merged.txt]]
   [[File:lung_cancer_methylpurify_informativeCGIs_merged.txt]]
   [[File:pancreatic_cancer_methylpurify_informativeCGIs_merged.txt]]
   [[File:pancreatic_cancer_methylpurify_informativeCGIs_merged.txt]]
* I uploaded the BED files to GREAT & used default settings to get the biological processes that are enriched. This may be because the regions are all CGIs, so not certain whether the signal is real or not.
* Results from GREAT:
  [[File:colon_cancer_informative_CGIs_biologicalprocess.png| 500px]]
  [[File:lung_cancer_informative_CGIs_biologicalprocess.png| 500px]]
  [[File:pancreatic_cancer_informative_CGIs_biologicalprocess.png| 500px]]
== Overlap of informative CGIs with blood UMR/LMRs ==
* Blood UMRs and LMRs were identified using MethylSeekR
* I used bedtools intersect and required that 50% of the informative bins be overlapped by UMRs/LMRs
* Results:
{| class="wikitable"
| align="center" style="background:#f0f0f0;"|''''''
| align="center" style="background:#f0f0f0;"|'''colon'''
| align="center" style="background:#f0f0f0;"|'''lung'''
| align="center" style="background:#f0f0f0;"|'''pancreatic'''
|-
| Number of informative CGIs||2852||1248||1798
|-
| overlap with UMR||2274||1144||1699
|-
| overlap with LMR||12||14||14
|-
| %overlap with UMR||79.73%||91.67%||94.49%
|-
| %overlap with LMR||0.42%||1.12%||0.78%
|-
| % no overlap||19.85%||7.21%||4.73%
|}
* Majority of informative CGIs are in UMRs.
* Since only a fraction of informative CGIs are overlapping with LMRs, I looked for region/functional enrichment using GREAT with the CGIs overlapping LMRs as test data and all the informative CGIs as background. No term was enriched.
* Since most of the informative CGIs are overlapping with UMRs, no term was enriched if I used all informative CGIs as background. So I used the whole genome was background. The GREAT results show developmental associated terms enriched in biological processes.
* GREAT results:
  [[File:colon_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]]
  [[File:lung_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]]
  [[File:pancreatic_cancer_informative_CGIs_UMRs_biologicalprocess.png| 500px]]
== Methylation status of informative bins in normal lung, colon, and pancreas ==
* Define "informative bins" : regions identified using MethylPurify which can indicate the level of heterogeneity in tumor samples AND these regions are also overlapping with blood UMRs (meaning that they are unmethylated in blood tissues).
* Strategy: (1) Download ENCODE RRBS data for normal tissues (2) Generate a methylation matrix of normal tissues at these bins
=== Download ENCODE data ===
* GEO experiment id: GSE27584
* Downloaded the tar of bed file.
* Normal tissues (33 normal samples, 66 libraries) and Cancer tissues (19 cancer samples, 49 libraries)
{| class="wikitable"
| align="center" style="background:#f0f0f0;"|'''GSM'''
| align="center" style="background:#f0f0f0;"|'''Sample ID'''
| align="center" style="background:#f0f0f0;"|'''Cell karyotype'''
|-
| GSM683779||BJ||normal
|-
| GSM683770||H1-hESC||normal
|-
| GSM683927||GM12878||relatively normal
|-
| GSM683803||BC_Uterus_BN0765||Normal
|-
| GSM683911||BC_Uterus_BN0765||Normal
|-
| GSM683781||BC_Testis_N30||Normal
|-
| GSM683851||HRCEpiC||normal
|-
| GSM683789||BC_Skeletal_Muscle_01-11002||Normal
|-
| GSM683941||HSMM||normal
|-
| GSM683876||BC_Leukocyte_UHN00204||Normal
|-
| GSM683865||BC_Skin_01-11002||Normal
|-
| GSM683847||Fibrobl||normal
|-
| GSM683841||GM12878-XiMat||relatively normal
|-
| GSM683879||H1-hESC||normal
|-
| GSM683782||BC_Liver_01-11002||Normal
|-
| GSM683883||BC_Adrenal_Gland_H12803N||Normal
|-
| GSM683811||BC_Skeletal_Muscle_01-11002||Normal
|-
| GSM683834||BC_Breast_02-03015||Normal
|-
| GSM683801||HSMMtube||normal
|-
| GSM683760||PrEC||Normal
|-
| GSM683838||PrEC||Normal
|-
| GSM683763||BJ||normal
|-
| GSM683821||BC_Lung_01-11002||Normal
|-
| GSM683878||BC_Kidney_01-11002||Normal
|-
| GSM683796||BC_Placenta_UHN00189||Normal
|-
| GSM683765||Astrocy||normal
|-
| GSM683786||BC_Adrenal_Gland_H12803N||Normal
|-
| GSM683798||BC_Brain_H11058N||Normal
|-
| GSM683939||HTR8svn||normal
|-
| GSM683901||HRCEpiC||normal
|-
| GSM683882||Astrocy||normal
|-
| GSM683908||BC_Brain_H11058N||Normal
|-
| GSM683892||BC_Skin_01-11002||Normal
|-
| GSM683867||HTR8svn||normal
|-
| GSM683819||BC_Pancreas_H12817N||Normal
|-
| GSM683906||GM12878||relatively normal
|-
| GSM683928||Osteobl||normal
|-
| GSM683947||BC_Stomach_01-11002||Normal
|-
| GSM683820||BC_Pericardium_H12529N||Normal
|-
| GSM683846||BC_Left_Ventricle_N41||Normal
|-
| GSM683899||BC_Pericardium_H12529N||Normal
|-
| GSM683848||BC_Pancreas_H12817N||Normal
|-
| GSM683774||GM12878-XiMat||relatively normal
|-
| GSM683914||BC_Liver_01-11002||Normal
|-
| GSM683759||BC_Leukocyte_UHN00204||Normal
|-
| GSM683870||BC_Kidney_01-11002||Normal
|-
| GSM683877||BC_Stomach_01-11002||Normal
|-
| GSM683916||SAEC||normal
|-
| GSM683766||Fibrobl||normal
|-
| GSM683943||HRE||normal
|-
| GSM683850||BC_Testis_N30||Normal
|-
| GSM683764||Hepatocytes||normal
|-
| GSM683761||BC_Breast_02-03015||Normal
|-
| GSM683895||BC_Left_Ventricle_N41||Normal
|-
| GSM683931||BC_Skeletal_Muscle_H12817N||Normal
|-
| GSM683831||HRE||normal
|-
| GSM683893||BC_Placenta_UHN00189||Normal
|-
| GSM683868||BC_Skeletal_Muscle_H12817N||Normal
|-
| GSM683907||HSMM||normal
|-
| GSM683945||HSMMtube||normal
|-
| GSM683818||HSMM||normal
|-
| GSM683872||Hepatocytes||normal
|-
| GSM683833||SAEC||normal
|-
| GSM683940||BC_Lung_01-11002||Normal
|-
| GSM683799||HSMMtube||normal
|-
| GSM683881||Osteobl||normal
|}
{| class="wikitable"
| align="center" style="background:#f0f0f0;"|'''GSM'''
| align="center" style="background:#f0f0f0;"|'''Sample ID'''
| align="center" style="background:#f0f0f0;"|'''Cell karyotype'''
|-
| GSM683792|| Jurkat||cancer
|-
| GSM980577|| ovcar-3||cancer
|-
| GSM720353|| MCF-7||cancer
|-
| GSM683806|| Jurkat||cancer
|-
| GSM720354|| HEK293||cancer
|-
| GSM683769|| HEK293||cancer
|-
| GSM683857|| NB4||cancer
|-
| GSM980582|| Caco-2||cancer
|-
| GSM683946|| LNCaP||cancer
|-
| GSM683863|| LNCaP||cancer
|-
| GSM980580|| A549||cancer
|-
| GSM683856|| K562||cancer
|-
| GSM980575|| MCF-7||cancer
|-
| GSM683884|| SK-N-MC||cancer
|-
| GSM683788|| HCT-116||cancer
|-
| GSM683828|| HeLa-S3||cancer
|-
| GSM1048458|| PANC-1||cancer
|-
| GSM720352|| MCF-7||cancer
|-
| GSM683918|| SK-N-MC||cancer
|-
| GSM683898|| HCT-116||cancer
|-
| GSM683827|| NB4||cancer
|-
| GSM720351|| MCF-7||cancer
|-
| GSM683795|| HepG2||cancer
|-
| GSM683793|| MCF-7||cancer
|-
| GSM683855|| HepG2||cancer
|-
| GSM683924|| LNCaP||cancer
|-
| GSM683909|| PFSK-1||cancer
|-
| GSM683866|| PANC-1||cancer, male
|-
| GSM683800|| SK-N-SH_RA||cancer
|-
| GSM683920|| PANC-1||cancer, male
|-
| GSM720355|| HEK293||cancer
|-
| GSM683926|| HeLa-S3||cancer
|-
| GSM980576|| HL-60||cancer
|-
| GSM683768|| LNCaP||cancer
|-
| GSM980574|| MCF-7||cancer
|-
| GSM720349|| NT2-D1||cancer
|-
| GSM1048457|| HepG2||cancer
|-
| GSM683787|| MCF-7||cancer
|-
| GSM683825|| HEK293||cancer
|-
| GSM683862|| LNCaP||cancer
|-
| GSM683832|| NT2-D1||cancer
|-
| GSM683776|| LNCaP||cancer
|-
| GSM980581|| UCH-1||cancer
|-
| GSM683929|| NT2-D1||cancer
|-
| GSM683804|| NT2-D1||cancer
|-
| GSM683780|| K562||cancer
|-
| GSM720350|| MCF-7||cancer
|-
| GSM683919|| SK-N-SH_RA||cancer
|-
| GSM683810|| PFSK-1||cancer
|}
===Generate methylation matrices over Informative Bins ===
* Majority of informative bins are unmethylated in normal tissues
* Two particular cell lines were more methylated:
**HTR: Trophoblast (HTR-8/SVneo) cell line. A thin layer of ectoderm that forms the wall of many mammalian blastulas and functions in the nutrition and implantation of the embryo, cell karyotype: normal, cell lineage: Blastula
**GM12878-XiMat: lymphoblastoid, cell karyotype: relatively normal, cell lineage: International HapMap Project - CEPH/Utah - European Caucasion; Epstein-Barr Virus;Cloned for maternal X inactivation
* Heatmap (normal tissues, showing replicates)
Colon cancer
[[File:ColonCancerBins_normalTissues_heatmap.png | 600px]]
Lung cancer
[[File:LungCancerBins_normalTissues_heatmap.png | 600px]]
Pancreatic cancer
[[File:PancreaticCancerBins_normalTissues_heatmap.png | 600px]]
* Heatmaps ( all replicates were merged into one file )
Colon cancer: normal tissues, cancer cell lines
[[File:ColonCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:ColonCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]]
Lung cancer: normal tissues, cancer cell lines
[[File:LungCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:LungCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]]
Pancreatic cancer: normal tissues, cancer cell lines
[[File:PancreaticCancerBins_normalTissues_heatmap_mergedReps.png | 600px]][[File:PancreaticCancerBins_cancerCellLines_heatmap_mergedReps.png | 600px]]
==="Normal" reference bins===
* Dr. Zhang suggested to use only "BC_" samples that we are sure are 100% normal tissues. There are 17 of these. After plotting the heatmaps, placenta and testis should be removed.
* Median methylation levels across normal tissues (removed placenta, testis)
** [[File:Colon.bins.median-methylation-normal.txt]] [[File:Colon.heatmap.qqplot.pdf]]
** [[File:Lung.bins.median-methylation-normal.txt]] [[File:Lung.heatmap.qqplot.pdf]]
** [[File:Pancreatic.bins.median-methylation-normal.txt]] [[File:Pancreatic.heatmap.qqplot.pdf]]
[[File:median-methylation-at-informative-bins-in-normal.png | 300px]]
* To get the "normal" reference:
{| class="wikitable"
| align="center" style="background:#f0f0f0;"|''''''
| align="center" style="background:#f0f0f0;"|'''Colon'''
| align="center" style="background:#f0f0f0;"|'''Lung'''
| align="center" style="background:#f0f0f0;"|'''Pancreatic'''
|-
| Number of starting bins||2274||1144||1699
|-
| Number of bins with >=15 normal tissues coverage||2138||1068||1622
|-
| Number of bins with median methylation in normal <0.2||2070 (96.82%)||1017(95.22%)||1597(98.46%)
|-
| Number of bins with median methylation in normal <0.1||1944(90.93%)||932(87.27%)||1504(92.73%)
|-
| Number of bins with median methylation in normal <0.05||1677(78.43%)||716(67.04%)||1238(76.33%)
|-
| %bins(out of all orig bins, if 0.2 cutoff)||91.03%||88.90%||94.00%
|}
== Divide haplotypes into clusters in solid tumor samples ==
* Go back to solid tumor data and try to identify haplotype clusters.
* Try to estimate tumor purify based on the fraction of cancer haplotypes (these should be partially or fully methylated).
== Discussion ==
* Discussion with Dr. Zhang (9/12/14):
** Intersect informative bins with blood UMRs, what are those regions enriched for in GREAT? (DONE)
** How to tell whether the minor or major component is the tumor DNA ? It appears that these tumor references have varying amount of minor component.
*** Ave. methyl colon cancer tumors: major = 0.7348, minor = 0.1602
*** Ave. methyl lung cancer tumors:  major = 0.6393, minor = 0.2574
*** Ave. methyl pancreatic cancer tumors: major = 0.7623, minor = 0.1181
*** Although majority of informative CGIs are overlapping with blood UMRs, the minor components are not always unmethylated, thus there may be other normal cells in the mixture.
** Use MethylPurify to generate cancer reference from primary tumors. So we can create a database of true cancer references (after substracting out normal cells). We can use the pure cancer reference for methylation haplotyping to detect cancer reads in blood.

Latest revision as of 19:13, 7 October 2014

MethylPurify[edit]

Test run with WGBS data and BSMAP aligner[edit]

  • I tried running MethylPurify on our RRBS MONOD bam files, but could never get the program to complete running/obtain informative bins and mixing ratio.
  • Two major issues with MethylPurify: requires enough reads to get minimum depth of coverage over informative bins and may need to realign data with BSMAP.
  • Downloaded WGBS sequencing data from the paper: SRR1232303 (BioSample: SAMN02725471; lung adenocarcinoma tissue, male), containing ~20 million PE reads x 90 bp each.
  • Downsampled to get ~25% of only reads 1 to get 5,077,371 x 90 bp single end reads to perform testing.
  • Performed mapping with BSMAP as that aligner is listed by MethylPurify to get the necessary bam input file.
  • BSMAP mapping command:
 /bsmap-2.74/bsmap -u -s 12 -v 0.04 -p 4 -a downsampled.SRR1232303_1.fastq -d hg19.fa -o downsampled.SRR1232303_1.fastq.sam
  • BSMAP took 36,221 seconds (10.06 hours) to complete mapping for ~5 million SE x 90 bp reads with 4 CPU threads.
  • Performed deconvolution with MethylPurify:
 python2.7 /home/ddiep/Downloads/MethylPurify-2.0-20140819/methylpurify/bin/MethylPurify -f downsampled.SRR1232303_1.fastq.bam 
      -g hg19.fa -i /home/ddiep/Downloads/MethylPurify-2.0/methylpurify/db/CGI_hg19_slop1000.bed -c 10 -s 50 -b 300
  • MethylPurify outputs:
 ...
 The predicted mixing ratio is:  0.265
 The number of informative bins is:  29.0
 The number of informative bins is less than 400, too less for the prediction of mixing ratio, continue
 INFO  @ Mon, 15 Sep 2014 09:33:27: Running get_methyl_profile:
 MixingRatio:0.265
  • CGI_hg19_slop1000.bed was only found in the MethylPurify-2.0.tar.gz package, but not the latest package. I couldn't find the genome.sh script that was necessary to generate this, so I need to figure out how to generate this file in the future because I am not 100% confident this is the appropriate file to use.

Testing with MONOD RRBS bam files from BisReadMapper[edit]

  • Since WGBS and RRBS may cover different CGIs, the CGI selected regions by MethylPurify have this depth of coverage:
 samtools depth XXX.CGI.sorted.bam  |  awk '{sum+=$3} END { print "Sum = ", sum, " Average = ",sum/NR}' 
 >> Sum =  41941637  Average =  2.30644
  • Our RRBS data have even better coverage at CGIs, the CGI regions selected by MethylPurify for PC-T-7_2 have this depth of coverage:
 samtools depth PC-T-7_2.merged.CGI.sorted.bam  |  awk '{sum+=$3} END { print "Sum = ", sum, " Average = ",sum/NR}'
 >> Sum =  393749988  Average =  27.9027
  • This could means that MethylPurify failed when I ran on our RRBS bam files because it couldn't understand the alignments from bisReadMapper, but requires the BSMAP alignment format (may have special instructions for MethylPurify). I will figure out the differences in BSMAP and BisReadMapper bam files.
  • BSMAP sam file:
SRR1232303.200105       0       chr1    30557958        255     90M     *       0       0       ATTCCTTTATTTTGGTGGTTGGGGTGTTTTAGGGTTTTTTTTTTAATTAATTTTTTTTTCGTTGTTTTTTTTTTTTTGTTTTATGTTTTT      @CCFFFFFFHHHHJICGHHIJJJJ:DFHIJHJJJHJJJJJJHFDCDDEECEEEDDDDDDDDDDDDDDDDDDDDDDDD38ACD3:(:ADDD      NM:i:3  ZS:Z:++
SRR1232303.200112       16      chr1    30559194        255     90M     *       0       0       ATCCCTTCCCTTTCACAACCTACTCCGAAAACCGCTAACCTAAAAATACAGCAATCTCTACTATCCTAAAAAAACAAAATCCCAAGGAAT      DDDDDDDDBDDEDDDDDDDDCACAFFFHHGIJJIJIHHJIHJJJIIGJJJJJIJGIIJJIJJIHJIHJJJJJJJJJJHHHHHFFFFFCCC      NM:i:2  ZS:Z:-+
  • BSMAP have extra ZS:Z field that's just the strand information. MethylPurify would fail similarly as for BisReadMapper bam files, after I removed the ZS:Z field from the BSMAP bam file. I may just need to generate the ZS:Z field to get MethylPurify to work on BisReadMapper bam files.
  • Encoding for ZS:Z field is as follows:
       ++: forward strand of Watson of reference (BSW)
       +-: reverse strand of Watson of reference (BSWC)
       -+: forward strand of Crick of reference (BSC)  
       --: reverse strand of Crick of reference (BSCC)
  • Make MethylPurify think that BisReadMapper bam files are from BSMAP: Add in NM:i:0 and ZS:Z:-+ (for Crick) and ZS:Z:++ (for Watson).
 samtools view $f | awk '{if($2 == 0) print $0"\tNM:i:0\tZS:Z:++"; if($2 == 16) print $0"\tNM:i:0\tZS:Z:-+"}' | samtools view -bSt /media/2TB_storeA/BisRef/Hg19_plusLamba/hg19_lambda.fa.fai - > mod.$f
  • Run MethylPurify:
 python2.7 /home/ddiep/Downloads/MethylPurify-2.0-20140819/methylpurify/bin/MethylPurify -f ../mod.PC-T-7_2.merged.bam -g hg19.fa -i /home/ddiep/Downloads/MethylPurify-2.0/methylpurify/db/CGI_hg19_slop1000.bed -c 10 -s 50 -b 300
  • MethylPurify outputs:
 ...
 The predicted mixing ratio is:  0.175
 The number of informative bins is:  304.0
 The number of informative bins is less than 400, too less for the prediction of mixing ratio, continue
 INFO  @ Mon, 15 Sep 2014 13:09:12: Running get_methyl_profile:
 MixingRatio:0.175
  • Results from MethylPurify:
 File:MixRatio.MethylPurify test1.png
 File:InformativeBins.MethylPurify test1.png

MethylPurify run on merged duplicates tumor samples[edit]

  • Results from MethylPurify:
 File:MixRatio.MethylPurify testMergedTumors.png
 File:InformativeBins.MethylPurify testMergedTumors.png
  • I merged all of the informative bins for colon cancer, lung cancer, and pancreatic cancers together into one BED file.
 File:Colon cancer methylpurify informativeCGIs merged.txt
 File:Lung cancer methylpurify informativeCGIs merged.txt
 File:Pancreatic cancer methylpurify informativeCGIs merged.txt
  • I uploaded the BED files to GREAT & used default settings to get the biological processes that are enriched. This may be because the regions are all CGIs, so not certain whether the signal is real or not.
  • Results from GREAT:
 File:Colon cancer informative CGIs biologicalprocess.png
 File:Lung cancer informative CGIs biologicalprocess.png
 File:Pancreatic cancer informative CGIs biologicalprocess.png

Overlap of informative CGIs with blood UMR/LMRs[edit]

  • Blood UMRs and LMRs were identified using MethylSeekR
  • I used bedtools intersect and required that 50% of the informative bins be overlapped by UMRs/LMRs
  • Results:
' colon lung pancreatic
Number of informative CGIs 2852 1248 1798
overlap with UMR 2274 1144 1699
overlap with LMR 12 14 14
%overlap with UMR 79.73% 91.67% 94.49%
%overlap with LMR 0.42% 1.12% 0.78%
% no overlap 19.85% 7.21% 4.73%
  • Majority of informative CGIs are in UMRs.
  • Since only a fraction of informative CGIs are overlapping with LMRs, I looked for region/functional enrichment using GREAT with the CGIs overlapping LMRs as test data and all the informative CGIs as background. No term was enriched.
  • Since most of the informative CGIs are overlapping with UMRs, no term was enriched if I used all informative CGIs as background. So I used the whole genome was background. The GREAT results show developmental associated terms enriched in biological processes.
  • GREAT results:
 File:Colon cancer informative CGIs UMRs biologicalprocess.png
 File:Lung cancer informative CGIs UMRs biologicalprocess.png
 File:Pancreatic cancer informative CGIs UMRs biologicalprocess.png

Methylation status of informative bins in normal lung, colon, and pancreas[edit]

  • Define "informative bins" : regions identified using MethylPurify which can indicate the level of heterogeneity in tumor samples AND these regions are also overlapping with blood UMRs (meaning that they are unmethylated in blood tissues).
  • Strategy: (1) Download ENCODE RRBS data for normal tissues (2) Generate a methylation matrix of normal tissues at these bins

Download ENCODE data[edit]

  • GEO experiment id: GSE27584
  • Downloaded the tar of bed file.
  • Normal tissues (33 normal samples, 66 libraries) and Cancer tissues (19 cancer samples, 49 libraries)
GSM Sample ID Cell karyotype
GSM683779 BJ normal
GSM683770 H1-hESC normal
GSM683927 GM12878 relatively normal
GSM683803 BC_Uterus_BN0765 Normal
GSM683911 BC_Uterus_BN0765 Normal
GSM683781 BC_Testis_N30 Normal
GSM683851 HRCEpiC normal
GSM683789 BC_Skeletal_Muscle_01-11002 Normal
GSM683941 HSMM normal
GSM683876 BC_Leukocyte_UHN00204 Normal
GSM683865 BC_Skin_01-11002 Normal
GSM683847 Fibrobl normal
GSM683841 GM12878-XiMat relatively normal
GSM683879 H1-hESC normal
GSM683782 BC_Liver_01-11002 Normal
GSM683883 BC_Adrenal_Gland_H12803N Normal
GSM683811 BC_Skeletal_Muscle_01-11002 Normal
GSM683834 BC_Breast_02-03015 Normal
GSM683801 HSMMtube normal
GSM683760 PrEC Normal
GSM683838 PrEC Normal
GSM683763 BJ normal
GSM683821 BC_Lung_01-11002 Normal
GSM683878 BC_Kidney_01-11002 Normal
GSM683796 BC_Placenta_UHN00189 Normal
GSM683765 Astrocy normal
GSM683786 BC_Adrenal_Gland_H12803N Normal
GSM683798 BC_Brain_H11058N Normal
GSM683939 HTR8svn normal
GSM683901 HRCEpiC normal
GSM683882 Astrocy normal
GSM683908 BC_Brain_H11058N Normal
GSM683892 BC_Skin_01-11002 Normal
GSM683867 HTR8svn normal
GSM683819 BC_Pancreas_H12817N Normal
GSM683906 GM12878 relatively normal
GSM683928 Osteobl normal
GSM683947 BC_Stomach_01-11002 Normal
GSM683820 BC_Pericardium_H12529N Normal
GSM683846 BC_Left_Ventricle_N41 Normal
GSM683899 BC_Pericardium_H12529N Normal
GSM683848 BC_Pancreas_H12817N Normal
GSM683774 GM12878-XiMat relatively normal
GSM683914 BC_Liver_01-11002 Normal
GSM683759 BC_Leukocyte_UHN00204 Normal
GSM683870 BC_Kidney_01-11002 Normal
GSM683877 BC_Stomach_01-11002 Normal
GSM683916 SAEC normal
GSM683766 Fibrobl normal
GSM683943 HRE normal
GSM683850 BC_Testis_N30 Normal
GSM683764 Hepatocytes normal
GSM683761 BC_Breast_02-03015 Normal
GSM683895 BC_Left_Ventricle_N41 Normal
GSM683931 BC_Skeletal_Muscle_H12817N Normal
GSM683831 HRE normal
GSM683893 BC_Placenta_UHN00189 Normal
GSM683868 BC_Skeletal_Muscle_H12817N Normal
GSM683907 HSMM normal
GSM683945 HSMMtube normal
GSM683818 HSMM normal
GSM683872 Hepatocytes normal
GSM683833 SAEC normal
GSM683940 BC_Lung_01-11002 Normal
GSM683799 HSMMtube normal
GSM683881 Osteobl normal
GSM Sample ID Cell karyotype
GSM683792 Jurkat cancer
GSM980577 ovcar-3 cancer
GSM720353 MCF-7 cancer
GSM683806 Jurkat cancer
GSM720354 HEK293 cancer
GSM683769 HEK293 cancer
GSM683857 NB4 cancer
GSM980582 Caco-2 cancer
GSM683946 LNCaP cancer
GSM683863 LNCaP cancer
GSM980580 A549 cancer
GSM683856 K562 cancer
GSM980575 MCF-7 cancer
GSM683884 SK-N-MC cancer
GSM683788 HCT-116 cancer
GSM683828 HeLa-S3 cancer
GSM1048458 PANC-1 cancer
GSM720352 MCF-7 cancer
GSM683918 SK-N-MC cancer
GSM683898 HCT-116 cancer
GSM683827 NB4 cancer
GSM720351 MCF-7 cancer
GSM683795 HepG2 cancer
GSM683793 MCF-7 cancer
GSM683855 HepG2 cancer
GSM683924 LNCaP cancer
GSM683909 PFSK-1 cancer
GSM683866 PANC-1 cancer, male
GSM683800 SK-N-SH_RA cancer
GSM683920 PANC-1 cancer, male
GSM720355 HEK293 cancer
GSM683926 HeLa-S3 cancer
GSM980576 HL-60 cancer
GSM683768 LNCaP cancer
GSM980574 MCF-7 cancer
GSM720349 NT2-D1 cancer
GSM1048457 HepG2 cancer
GSM683787 MCF-7 cancer
GSM683825 HEK293 cancer
GSM683862 LNCaP cancer
GSM683832 NT2-D1 cancer
GSM683776 LNCaP cancer
GSM980581 UCH-1 cancer
GSM683929 NT2-D1 cancer
GSM683804 NT2-D1 cancer
GSM683780 K562 cancer
GSM720350 MCF-7 cancer
GSM683919 SK-N-SH_RA cancer
GSM683810 PFSK-1 cancer

Generate methylation matrices over Informative Bins[edit]

  • Majority of informative bins are unmethylated in normal tissues
  • Two particular cell lines were more methylated:
    • HTR: Trophoblast (HTR-8/SVneo) cell line. A thin layer of ectoderm that forms the wall of many mammalian blastulas and functions in the nutrition and implantation of the embryo, cell karyotype: normal, cell lineage: Blastula
    • GM12878-XiMat: lymphoblastoid, cell karyotype: relatively normal, cell lineage: International HapMap Project - CEPH/Utah - European Caucasion; Epstein-Barr Virus;Cloned for maternal X inactivation
  • Heatmap (normal tissues, showing replicates)
Colon cancer
File:ColonCancerBins normalTissues heatmap.png
Lung cancer
File:LungCancerBins normalTissues heatmap.png
Pancreatic cancer
File:PancreaticCancerBins normalTissues heatmap.png
  • Heatmaps ( all replicates were merged into one file )
Colon cancer: normal tissues, cancer cell lines
File:ColonCancerBins normalTissues heatmap mergedReps.pngFile:ColonCancerBins cancerCellLines heatmap mergedReps.png
Lung cancer: normal tissues, cancer cell lines
File:LungCancerBins normalTissues heatmap mergedReps.pngFile:LungCancerBins cancerCellLines heatmap mergedReps.png
Pancreatic cancer: normal tissues, cancer cell lines
File:PancreaticCancerBins normalTissues heatmap mergedReps.pngFile:PancreaticCancerBins cancerCellLines heatmap mergedReps.png

"Normal" reference bins[edit]

File:Median-methylation-at-informative-bins-in-normal.png

  • To get the "normal" reference:
' Colon Lung Pancreatic
Number of starting bins 2274 1144 1699
Number of bins with >=15 normal tissues coverage 2138 1068 1622
Number of bins with median methylation in normal <0.2 2070 (96.82%) 1017(95.22%) 1597(98.46%)
Number of bins with median methylation in normal <0.1 1944(90.93%) 932(87.27%) 1504(92.73%)
Number of bins with median methylation in normal <0.05 1677(78.43%) 716(67.04%) 1238(76.33%)
%bins(out of all orig bins, if 0.2 cutoff) 91.03% 88.90% 94.00%

Divide haplotypes into clusters in solid tumor samples[edit]

  • Go back to solid tumor data and try to identify haplotype clusters.
  • Try to estimate tumor purify based on the fraction of cancer haplotypes (these should be partially or fully methylated).

Discussion[edit]

  • Discussion with Dr. Zhang (9/12/14):
    • Intersect informative bins with blood UMRs, what are those regions enriched for in GREAT? (DONE)
    • How to tell whether the minor or major component is the tumor DNA ? It appears that these tumor references have varying amount of minor component.
      • Ave. methyl colon cancer tumors: major = 0.7348, minor = 0.1602
      • Ave. methyl lung cancer tumors: major = 0.6393, minor = 0.2574
      • Ave. methyl pancreatic cancer tumors: major = 0.7623, minor = 0.1181
      • Although majority of informative CGIs are overlapping with blood UMRs, the minor components are not always unmethylated, thus there may be other normal cells in the mixture.
    • Use MethylPurify to generate cancer reference from primary tumors. So we can create a database of true cancer references (after substracting out normal cells). We can use the pure cancer reference for methylation haplotyping to detect cancer reads in blood.