Kun:LabNotes/MONOD/2014-6-16: Difference between revisions
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]
- A sample info sheet and a batch processing script was 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
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.