NGS training @GIS

PATH=/usr/local/samtools:/usr/local/samtools/bcftools:/mnt/software/mappers/bwa:$PATH

put files to ~/refgenome/reference.fa ~/prj1/reads.fastq

#1. index step
cd ~/refgenome
bwa index -a is reference.fa
samtools faidx ./reference.fa

#2. align and pileup
cd ~/prj1
bwa aln ../refgenome/reference.fa ./reads.fastq >reads.bwa.aln.sai
bwa samse ../refgenome/reference.fa ./reads.bwa.aln.sai reads.fastq >reads.bwa.aln.sam
samtools import ../refgenome/reference.fa.fai ./reads.bwa.aln.sam reads.bwa.aln.bam
samtools sort reads.bwa.aln.bam reads.bwa.aln.sorted
samtools index reads.bwa.aln.sorted.bam
samtools mpileup -f ../refgenome/reference.fa reads.bwa.aln.sorted.bam >temp.pileup
samtools mpileup -ugf ../refgenome/reference.fa reads.bwa.aln.sorted.bam >reads.bwa.aln.sorted.pileup

#3. call variant file
bcftools view -bcvg reads.bwa.aln.sorted.pileup >var.row.bcf
bcftools view var.row.bcf |vcfutils.pl varFilter -D 100 >var.filt.vcf
more var.filt.vcf

#4. download and install tablet
http://bioinf.scri.ac.uk/tablet/download.shtml
use tablet to view reads.bwa.aln.sorted.bam

Batch Effect in AU

date2col <- function(date.list)
{
 clr.template = c("red", "orange", "yellow", "green", "cyan", "blue", "purple")
 num.dates <- length(date.list)
 clr.list <- vector()
 clr.list[1] <- "red"
 c.index <- 0
 for (i in 2:num.dates) {
  if(date.list[i] == date.list[i-1]) {
  clr.list[i] = clr.list[i-1]
  } else {
  c.index <- c.index+1
  clr.list[i] = clr.template[c.index %% 7+1]
  }
 }
 return(clr.list)
}


wk.dir <- "E:\\CEL\\GastricCancer\\AU\\PM_data_new\\Gastric_Affy_files\\Tumors"
setwd(wk.dir)

file.info.file <- "files.info.user.batch.txt"
file.info <- read.table(file=file.info.file, header=T, row.names=1)
file.info[order(file.info$EXP_DATE),]
file.info <- file.info[order(as.POSIXct(strptime(file.info$EXP_DATE, "%m/%d/%Y"))),]
my.color <- file.info$EXP_DATE
my.color <- date2col(my.color)


data <- read.table(file="AU_GC70.rma.txt", header=T, row.names=1)
data.ctrl <- data[54614:54675, rownames(file.info)]
library("gplots")



data <- sweep(data.ctrl, 1, apply(data.ctrl, 1, median)) #just median centered
data[data < -4] <- -4
data[data > 4] <- 4
hm<-heatmap.2(as.matrix(data), col=greenred(75), scale="none", dendrogram="none", Rowv= T, Colv=F, ColSideColors=my.color, key=TRUE, symkey=FALSE, density.info="none",trace="none", cexRow=0.75,cexCol=0.5)
pdf(file = "Batch_in_CtrlGenes1.pdf", width=10, height=10)
hm<-heatmap.2(as.matrix(data), col=greenred(75), scale="none", dendrogram="none", Rowv= T, Colv=F, ColSideColors=my.color, key=TRUE, symkey=FALSE, density.info="none",trace="none", cexRow=0.75,cexCol=0.5)
dev.off()


data <- t(scale(t(data.ctrl), scale=T)) #standardized by row(gene)
data[data < -3] <- -3
data[data > 3] <- 3
hm<-heatmap.2(as.matrix(data), col=greenred(75), scale="none", dendrogram="none", Rowv= T, Colv=F, ColSideColors=my.color, key=TRUE, symkey=FALSE, density.info="none",trace="none", cexRow=0.75,cexCol=0.5)
pdf(file = "Batch_in_CtrlGenes2.pdf", width=10, height=10)
hm<-heatmap.2(as.matrix(data), col=greenred(75), scale="none", dendrogram="none", Rowv= T, Colv=F, ColSideColors=my.color, key=TRUE, symkey=FALSE, density.info="none",trace="none", cexRow=0.75,cexCol=0.5)
dev.off()



genes<-data.ctrl
genes<-t(genes)
pcs<-prcomp(genes)
summary(pcs) #select first N=10 PCs depending on Cumulative Proportion (e.g. >= 97.7%)
#pcs$x[,1:10]
#write.table(pcs$x[,1:15], file=ctrl.genes.pcs, sep = "\t")
#pcs<-prcomp(data[1:22215,])
library(scatterplot3d)
PC1<-pcs$x[,1]
PC2<-pcs$x[,2]
PC3<-pcs$x[,3]


group.colors <- my.color
group.colors <- file.info$COLOR



scatterplot3d(PC1,PC2,PC3, main="PCA scatterplot before ComBat normalization", color=group.colors, pch=16)

scatterplot3d(PC3,PC2,PC1, main="PCA scatterplot before ComBat normalization", color=group.colors, pch=16)

scatterplot3d(PC2,PC1,PC3, main="PCA scatterplot before ComBat normalization", color=group.colors, pch=16)

Proliferative

http://www.drugbank.ca/drugs/DB01229

harmacodynamics

Paclitaxel is a taxoid antineoplastic agent indicated as first-line and subsequent therapy for the treatment of advanced carcinoma of the ovary, and other various cancers including breast cancer. Paclitaxel is a novel antimicrotubule agent that promotes the assembly of microtubules from tubulin dimers and stabilizes microtubules by preventing depolymerization. This stability results in the inhibition of the normal dynamic reorganization of the microtubule network that is essential for vital interphase and mitotic cellular functions. In addition, paclitaxel induces abnormal arrays or "bundles" of microtubules throughout the cell cycle and multiple asters of microtubules during mitosis.

Mechanism of action

Paclitaxel interferes with the normal function of microtubule growth. Whereas drugs like colchicine cause the depolymerization of microtubules in vivo, paclitaxel arrests their function by having the opposite effect; it hyper-stabilizes their structure. This destroys the cell's ability to use its cytoskeleton in a flexible manner. Specifically, paclitaxel binds to the β subunit of tubulin. Tubulin is the "building block" of mictotubules, and the binding of paclitaxel locks these building blocks in place. The resulting microtubule/paclitaxel complex does not have the ability to disassemble. This adversely affects cell function because the shortening and lengthening of microtubules (termed dynamic instability) is necessary for their function as a transportation highway for the cell. Chromosomes, for example, rely upon this property of microtubules during mitosis. Further research has indicated that paclitaxel induces programmed cell death (apoptosis) in cancer cells by binding to an apoptosis stopping protein called Bcl-2 (B-cell leukemia 2) and thus arresting its function.

http://annonc.oxfordjournals.org/content/18/suppl_12/xii15.short
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2361361/
http://www.genecards.org/cgi-bin/carddisp.pl?gene=MAPT

http://www.springerlink.com/content/m68011x7147r7051/

Resistance to taxanes, related to limited efficacy of systemic therapy in cancer patients, is multifactorial. Among mechanisms of resistance to taxanes, those related to microtubule-associated proteins (MAP), including protein Tau, are of great importance. Protein Tau (50–64 kD) binds to beta-tubulin in the same place as paclitaxel. In preclinical studies, low expression of Tau in cancer cells was associated with increased sensitivity to paclitaxel. High expression of Tau protein in ER-positive breast cancers indicates resistance to taxane-containing chemotherapy and sensitivity to hormonal treatment. This article reviews current knowledge on predictive value of protein Tau in response to taxanes. Better understanding of its role may facilitate patients selection to this sort of treatment and lead to treatment optimization.

YGC gastric cancer

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13861

Test of propotions

inv <- c(54, 13) pro <- c(82, 29) meta <- c(56, 14) patients <- c(192, 56) prop.test(inv, patients) prop.test(pro, patients) prop.test(meta, patients)

silhouette width

library(cluster)
setwd("E:\\Projects\\8.ComBAT\\ComBat248")
rmafile <- "ComBat248T_filtered.stdized.txt"
data <- read.table(rmafile, header = T, sep = "\t", row.names=1)

setwd("E:\\Projects\\8.ComBAT\\ComBat248\\CCP_FIS_SG248_top10000_ZL20120301\\Run3")
feature.file <- "K3_limma_geneset4.txt"
feature <- read.table(feature.file, header = T, sep = "\t", row.names=1)

data <- data[feature$ID,]
plot (hclust(dist(t(data)))) # Hclust plot of data

dmat <- dist(t(data))
hc <- hclust(dmat, "average")
hsil <- silhouette(cutree(hc, k=3), dmat)
plot(hsil)
write.table(hsil, "silhouette_width.avg_linkage.txt", sep="\t")