Year 2019: Difference between revisions
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imported>Lab |
imported>Lab |
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=TD Project= | =TD Project= | ||
* Credit: Christopher Panlasigui | * Credit: Christopher Panlasigui | ||
* | * Brief computational/statistical steps: | ||
# Quantile normalization between all replicates (with log2 transformatuon) | # Quantile normalization between all replicates (with log2 transformatuon) | ||
# Linear model among the 3 groups | # Linear model among the 3 groups | ||
| Line 7: | Line 7: | ||
# Map gene names | # Map gene names | ||
# Cluster and show by interactive heatmaps in R | # Cluster and show by interactive heatmaps in R | ||
# | <syntaxhighlight lang="bash"> | ||
## [http://diverge.hunter.cuny.edu/~weigang/ | # Quantile normalization | ||
library(preprocessCore) | |||
w2.mat <- as.matrix(w2[,3:17]) #convert original data to matrix | |||
w2.mat.norm <- normalize.quantiles.robust(w2.mat, copy = TRUE, use.log2 = TRUE) | |||
rownames(w2.mat.norm) <- w2$Geneid | |||
# linear model | |||
fits <- lmList(log.counts ~ group | geneID, data=w2.norm.melt) | |||
lm.sum <- lapply(fits, function(x){ | |||
out<-summary(x); | |||
fstat <- out$fstatistic; | |||
pf(fstat[1], fstat[2], fstat[3], lower.tail=F) | |||
}) | |||
p.df <- data.frame(gene=names(fits), p.val=as.numeric(lm.sum)) | |||
w2.out <- cbind(w2[,1:2], w2.mat.norm, p.df[,1:2]) | |||
# heatmap | |||
heatmaply(td.mat, scale = "none" , cexRow = 0.50, colors = colorspace::diverge_hsv(16), branches_lwd = 0.3) | |||
</syntaxhighlight> | |||
* Results | |||
# [http://diverge.hunter.cuny.edu/~weigang/heatmap_td.html Heatmap 1. p<1e-5, showing Fold Change over mean of wild type] | |||
Latest revision as of 17:24, 8 February 2019
TD Project
- Credit: Christopher Panlasigui
- Brief computational/statistical steps:
- Quantile normalization between all replicates (with log2 transformatuon)
- Linear model among the 3 groups
- Select top significant genes
- Map gene names
- Cluster and show by interactive heatmaps in R
# Quantile normalization
library(preprocessCore)
w2.mat <- as.matrix(w2[,3:17]) #convert original data to matrix
w2.mat.norm <- normalize.quantiles.robust(w2.mat, copy = TRUE, use.log2 = TRUE)
rownames(w2.mat.norm) <- w2$Geneid
# linear model
fits <- lmList(log.counts ~ group | geneID, data=w2.norm.melt)
lm.sum <- lapply(fits, function(x){
out<-summary(x);
fstat <- out$fstatistic;
pf(fstat[1], fstat[2], fstat[3], lower.tail=F)
})
p.df <- data.frame(gene=names(fits), p.val=as.numeric(lm.sum))
w2.out <- cbind(w2[,1:2], w2.mat.norm, p.df[,1:2])
# heatmap
heatmaply(td.mat, scale = "none" , cexRow = 0.50, colors = colorspace::diverge_hsv(16), branches_lwd = 0.3)
- Results