Kun:LabNotes/MONOD/2014-6-16: Difference between revisions

From ZhangLabWiki
Jump to navigation Jump to search
(Created page with "==Analysis of the 140613_MiSeq data set== *Noi performed capture of 15 samples (Serum/tumor DNA from Pancreatic cancer patients, plasma from normal controls) using the MONOD V...")
 
 
(7 intermediate revisions by the same user not shown)
Line 3: Line 3:
*A MiSeq run (151+6+151bp) was performed on this batch of libraries. Data folder: /home/kunzhang/seqStore/20140613_MiSeq
*A MiSeq run (151+6+151bp) was performed on this batch of libraries. Data folder: /home/kunzhang/seqStore/20140613_MiSeq
===Low-level analysis===
===Low-level analysis===
*A sample info sheet and a batch processing script was generated.
*A [[Media:140613_MiSeq_sampleInfo.txt|sample info sheet]] and a [[Media:MONOD_batch_processing.txt|batch processing script]] was generated.
*All 15 data sets were mapped and the haploInfo files were generated.
*All 15 data sets were mapped and the haploInfo files were generated.
  ./MONOD_batch_processing.pl 140613_MiSeq_sampleInfo.txt > MONOD_batch_processing.log &
  ../get_probeEfficiency_matrix.pl > 140613_MiSeq_probeEfficiency_matrix.txt
{| {{table}}
| align="center" style="background:#f0f0f0;"|'''Sample_id'''
| align="center" style="background:#f0f0f0;"|'''# raw reads'''
| align="center" style="background:#f0f0f0;"|'''# mapped reads'''
| align="center" style="background:#f0f0f0;"|'''% mapping'''
| align="center" style="background:#f0f0f0;"|'''# on-target reads'''
| align="center" style="background:#f0f0f0;"|'''% on-target'''
| align="center" style="background:#f0f0f0;"|'''# UMIs'''
| align="center" style="background:#f0f0f0;"|'''% clonal'''
|-
| NC-P-10ng_1||1,651,384||821,536||49.7%||703,453||85.6%||47,641||93.23%
|-
| NC-P-10ng_2||2,041,882||721,140||35.3%||487,174||67.6%||40,520||91.68%
|-
| NC-P-5ng_1||2,126,484||653,912||30.8%||422,917||64.7%||22,424||94.70%
|-
| NC-P-5ng_2||1,503,204||583,432||38.8%||456,431||78.2%||24,643||94.60%
|-
| PC-S_10||1,998,558||920,290||46.0%||542,561||59.0%||31,570||94.18%
|-
| PC-S_2||2,084,954||1,000,838||48.0%||658,032||65.7%||49,312||92.51%
|-
| PC-S_3||1,780,602||878,368||49.3%||661,506||75.3%||36,352||94.50%
|-
| PC-S_6||1,788,488||800,280||44.7%||545,346||68.1%||25,537||95.32%
|-
| PC-S_7||2,042,144||578,860||28.3%||194,299||33.6%||12,612||93.51%
|-
| PC-S_9||2,106,932||977,652||46.4%||700,923||71.7%||33,147||95.27%
|-
| PC-T-1||2,643,028||1,773,208||67.1%||1,463,016||82.5%||105,276||92.80%
|-
| PC-T-2||2,036,388||1,328,234||65.2%||1,089,150||82.0%||68,969||93.67%
|-
| PC-T-4||2,632,888||1,567,146||59.5%||1,149,534||73.4%||83,254||92.76%
|-
| PC-T-6||1,073,678||647,684||60.3%||477,805||73.8%||22,304||95.33%
|-
| PC-T-7||2,646,776||1,618,226||61.1%||1,175,527||72.6%||86,816||92.61%
|-
|
|}
  allBED2MatrixFast.pl . > 140613_MiSeq_methylMatrix.txt
  #perform hierarchical clustering in R (need to manually clean up the sample names).
  x=read.table("140613_MiSeq_methylMatrix.txt",header=TRUE,row.names=1);
  x.cor = cor(x,use="pairwise.complete.obs",method="pearson")
  x.d = as.dist(1-x.cor)
  x.h = hclust(x.d,"complete")
  plot(x.h)
  [[Image:140610_15_samples_clustering.png|500px]]
*Clearly healthy plasma and tumor samples are quite different, whereas patient serum cluster more closely to healthy plasma. So the real question is whether we can identify tumor-specific haplotypes from patient serum.

Latest revision as of 22:04, 16 June 2014

Analysis of the 140613_MiSeq data set[edit]

  • Noi performed capture of 15 samples (Serum/tumor DNA from Pancreatic cancer patients, plasma from normal controls) using the MONOD V1N3 probe set:Noi/NOTES/2014-6-10
  • A MiSeq run (151+6+151bp) was performed on this batch of libraries. Data folder: /home/kunzhang/seqStore/20140613_MiSeq

Low-level analysis[edit]

 ./MONOD_batch_processing.pl 140613_MiSeq_sampleInfo.txt > MONOD_batch_processing.log &
 ../get_probeEfficiency_matrix.pl > 140613_MiSeq_probeEfficiency_matrix.txt
Sample_id # raw reads # mapped reads % mapping # on-target reads % on-target # UMIs % clonal
NC-P-10ng_1 1,651,384 821,536 49.7% 703,453 85.6% 47,641 93.23%
NC-P-10ng_2 2,041,882 721,140 35.3% 487,174 67.6% 40,520 91.68%
NC-P-5ng_1 2,126,484 653,912 30.8% 422,917 64.7% 22,424 94.70%
NC-P-5ng_2 1,503,204 583,432 38.8% 456,431 78.2% 24,643 94.60%
PC-S_10 1,998,558 920,290 46.0% 542,561 59.0% 31,570 94.18%
PC-S_2 2,084,954 1,000,838 48.0% 658,032 65.7% 49,312 92.51%
PC-S_3 1,780,602 878,368 49.3% 661,506 75.3% 36,352 94.50%
PC-S_6 1,788,488 800,280 44.7% 545,346 68.1% 25,537 95.32%
PC-S_7 2,042,144 578,860 28.3% 194,299 33.6% 12,612 93.51%
PC-S_9 2,106,932 977,652 46.4% 700,923 71.7% 33,147 95.27%
PC-T-1 2,643,028 1,773,208 67.1% 1,463,016 82.5% 105,276 92.80%
PC-T-2 2,036,388 1,328,234 65.2% 1,089,150 82.0% 68,969 93.67%
PC-T-4 2,632,888 1,567,146 59.5% 1,149,534 73.4% 83,254 92.76%
PC-T-6 1,073,678 647,684 60.3% 477,805 73.8% 22,304 95.33%
PC-T-7 2,646,776 1,618,226 61.1% 1,175,527 72.6% 86,816 92.61%
  allBED2MatrixFast.pl . > 140613_MiSeq_methylMatrix.txt
  #perform hierarchical clustering in R (need to manually clean up the sample names).
  x=read.table("140613_MiSeq_methylMatrix.txt",header=TRUE,row.names=1);
  x.cor = cor(x,use="pairwise.complete.obs",method="pearson")
  x.d = as.dist(1-x.cor)
  x.h = hclust(x.d,"complete")
  plot(x.h)
  File:140610 15 samples clustering.png
  • Clearly healthy plasma and tumor samples are quite different, whereas patient serum cluster more closely to healthy plasma. So the real question is whether we can identify tumor-specific haplotypes from patient serum.