How to include the maximum number of tumors

How we have 201 tumor samples which have avg_consensus_idx > 0.9 in CC_IFS of ComBat248, and the new ComBat201 has cophenetic = 1.
Thus we can lower the avg_consensus_idx cutoff to include more samples until cophenetic < 1.


24% of M patients benifit from chemo.
30% of D patients may benifit from PI3K?
46% treatment unknow?

tophat

  104   nohup tophat -r 124 -o tophat124  --num-threads=4 hg19 AGS_1_sequence.fastq  AGS_2_sequence.fastq >screen.txt &

The estimated library average fragment size is 280, the read length is 60bp, so the inner distange between paired reads (--mate-inner-dist) is 160.

124=fragment size(insert size) - 2*read length

Good Univ's

University of Calgary Calgary, Alberta
University of Alberta Edmonton, Alberta
Dalhousie University Halifax, Nova Scotia
McMaster University Hamilton, Ontario
Queen's University Kingston, Ontario
University of Western Ontario London, Ontario
University of Ottawa Ottawa, Ontario
University of Toronto Toronto, Ontario
University of Waterloo Waterloo, Ontario
University of British Columbia Vancouver, British Columbia

pvalues for affy probesets

library(affy)
setwd("/home/leiz/CEL/SGII192")
data <- ReadAffy()
eset_pma <- mas5calls(data)
pvalues <- assayDataElement(eset_pma, "se.exprs")
write.table(pvalues, "GC192.pvalues.txt", sep="\t")

A very good data mining website using R

http://www.togaware.com/datamining/survivor/Contents.html

glmnet cox

http://icb.med.cornell.edu/wiki/index.php/Elementolab/R_tutorial

Done