Ns126:Calendar/NOTES/2016-2-1: Difference between revisions

From ZhangLabWiki
Jump to navigation Jump to search
>Shicheng
>Shicheng
Line 75: Line 75:
  # find NP-P-high LOD target
  # find NP-P-high LOD target
  cd /home/shg047/monod/mixHap
  cd /home/shg047/monod/mixHap
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt | sort -k 2,2nr > RRBS_target_LOD_CC.txt  
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_CC.txt  
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt  | sort -k 2,2nr  > RRBS_target_LOD_LC.txt  
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt  |sort -k 2,2nr  > RRBS_target_LOD_LC.txt  
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt | sort -k 2,2nr > RRBS_target_LOD_PC.txt  
  perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_PC.txt  
  ./find_NC-P_high-LOD_targets.pl > NC-P_high-LOD_targets.txt
  ./find_NC-P_high-LOD_targets.pl > NC-P_high-LOD_targets.txt



Revision as of 21:59, 22 February 2016

Cancer Specific Methylation haplotype region show increased MHL in the plasma

  • From Dr. Zhang
  • I think I can rationalize the difficulty in detecting tumor haplotypes in plasma at a sufficient high level of sensitivity and specificity.
  • We are sequencing the plasma samples with very limited input DNA, equivalent to only a few hundred copies per sample. With the additional major loss of material during bisulfite conversion and library construction, we are probably sampling one or two dozens of molecules per sample. On the other hand, the fraction of tumor DNA in plasma is typically in the range of 0.1% or lower. So if there are 1,000 regions that has tumor-specific haplotypes at 0.1% level and we are looking at 10 unique molecules per region, we would expect to see one tumor haplotypes in only 10 of 1,000 regions. If you move to the next sample, you would see another 10 regions. The chance of finding a set of common regions where we can consistently detect tumor haplotypes is extremely low, at least at the sample size of <100 that we have.
  • Along this line of thoughts, random forest is not going to be the right approach for such data. Our best bet is to compare data from tumor tissues and normal plasma, identify a set of genomic regions that are clean in plasma and contain certain level of methylated haplotypes in tumor, then ask whether these regions have an elevated level of MHL.

Background

  1. Dr. Zhang's Round 1 analysis: http://genome-tech.ucsd.edu/LabNotes/index.php/Kun:LabNotes/MONOD/2014-8-12#5._Searching_for_primary_tumor_specific_haplotypes

Step

  1. Merge BAM files of primary cancer, cancer plasma and normal plasma
  2. LMS screening in normal plasma samples.
  3. Identify tumor-specific haplotype regions (primary solid tissue vs normal plasma)
  4. Calculate the cumulation/average MHL for these region (tumor specific) for a plasma sample.
  5. Then assign a probability for the plasma is belong to 'cancer' or 'normal' as well as which tissue-of-origin?
  6. A panel of canonical cancer-related methylation region, and a random genomic region as control)?

Round 1: Zero MHL in normal plasma

  • 1, Find out regions that contain HMHs in primary tumor but not in N37 normal tissues or normal plasma.
scp shg047@genome-miner.ucsd.edu:/home/kunzhang/CpgMIP/MONOD/Data/
cat PC-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt  > PC-P_N37_plasma_specific_HMH_regions.bed &
cat 7-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt  > LC-P_N37_plasma_specific_HMH_regions.bed &
cat 6-P-*.methHapCounts.txt | ../scripts/get_tumor_specific_HMH_regions.pl N37_ten_tissues_NC-P-ALL_RRBS-dRRBS.methHapCounts.txt  > CRC-P_N37_plasma_specific_HMH_regions.bed &
samtools view -H 6-P-10.sorted.clipped.bam > header.sam
samtools merge -h header.sam 6-P-10.sorted.clipped.bam 6-P-1.sorted.clipped.bam 6-P-2.sorted.clipped.bam 6-P-3.sorted.clipped.bam 6-P-4.sorted.clipped.bam 6-P-5.sorted.clipped.bam 6-P-6.sorted.clipped.bam 6-P-7.sorted.clipped.bam 6-P-8.sorted.clipped.bam 6-P-9.sorted.clipped.bam  
  • 1,define clean genomic region in normal plasma (low methylation region)

704 haplotype regions were identified as the cancer specific(solid tissue) and were clean in the normal plasma samples. File:Figure 4D.txt

  • 2 Calculate the cumulation/average MHL for these region (tumor specific) for a plasma sample.
    • MHL distribution for above defined regions

File:7AB3.tm.png

      • CP: cancer plasma, CT: cancer tissue, NP: normal plasma
  • 3. assign a probability for the plasma is belong to 'cancer' or 'normal' as well as which tissue-of-origin?
Colon: 740	
> myData	
  type        mean         sd   n          sem           me	
1   CP 0.303570259 0.04889567 740 0.0184808266 2.149566e+00	
2   NP 0.001489827 0.00036459 740 0.0001378021 9.520868e+11	
3   TP 0.004369450 0.01452305 740 0.0054891956 2.759810e+00	
 
Lung: 740	
> myData	
  type        mean         sd   n          sem           me	
1   CP 0.310171625 0.05194363 740 0.0196328452 2.137493e+00	
2   NP 0.001489827 0.00036459 740 0.0001378021 9.520868e+11	
3   TP 0.008964632 0.02154434 740 0.0081429953 2.444373e+00	
 

Round 2: Result

  • Dinh shared her alignment Bam file
  • Calling haploinfo and identify cancer specific hapltype region with Dr.Zhang's previous script
  • Change name to unified format 6-P, 7-P, PC-P code
# merge hapInfo
cd /home/shg047/monod/hapinfo
cat /home/shg047/monod/rrbs_kun/6-P-*.hapInfo.txt > ../mixHap/6-P-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/7-P-*.hapInfo.txt > ../mixHap/7-P-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/PC-P-*.hapInfo.txt > ../mixHap/PC-P-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/NC-P-*.hapInfo.txt > ../mixHap/NC-P-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/6-T-*.hapInfo.txt > ../mixHap/6-T-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/7-T-*.hapInfo.txt > ../mixHap/7-T-ALL.hapInfo.txt
cat /home/shg047/monod/rrbs_kun/PC-T-*.hapInfo.txt > ../mixHap/PC-T-ALL.hapInfo.txt
# find NP-P-high LOD target
cd /home/shg047/monod/mixHap
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_CC.txt 
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt  |sort -k 2,2nr  > RRBS_target_LOD_LC.txt 
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt /home/kunzhang/CpgMIP/MONOD/Public_data/WB_middle_age_UMR.BED.txt |sort -k 2,2nr > RRBS_target_LOD_PC.txt 
./find_NC-P_high-LOD_targets.pl > NC-P_high-LOD_targets.txt
  • Then what about Dr. Zhang's result in Phase 1 Plasma dataset
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 6-T-ALL.hapInfo.txt 6-P-ALL.hapInfo.txt  /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_CC.txt 
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt 7-T-ALL.hapInfo.txt 7-P-ALL.hapInfo.txt   /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr  > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_LC.txt 
perl /home/shg047/monod/bin/mixMethHapAnalysis_19Aug14.pl  NC-P-ALL.hapInfo.txt PC-T-ALL.hapInfo.txt PC-P-ALL.hapInfo.txt   /home/shg047/monod/mixHap/zhang/WB_middle_age_UMR.BED.txt| sort -k 2,2nr > /home/shg047/monod/mixHap/zhang/RRBS_targets_LOD_PC.txt 
cd /home/shg047/monod/mixHap/zhang