spearman correlation heatmap for RNA-seq

[emolinari]

Hi All!!!

I am trying to draw a correlation heatmap for my RNA-seq data. I would like to input the whole transcriptome of each sample (n=20) and see the similarities of each dataset using pearson and spearman correlations.
I have some issues in running my script on R: I could do it for a small dataset (300 genes) but not for the whole transcriptome...it takes forever and simply doesn't produce anything after 20 minutes!


Can anyone help with this?

Thanks!
Manu
here is my script (for spearman):

`cumulus_cluster` <- read.csv("transcriptome_corr.csv")
data.raw<- `cumulus_cluster`
data<-data.matrix(data.raw)

genes<-data[,2:15220]

install.packages("gplots")

library("gplots")

data.corr<-cor(genes,method="spearman")

heatmap.2(data.corr)
> sessionInfo()
R version 3.2.1 (2015-06-18)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.3 (Yosemite)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] reshape2_1.4.1 ggplot2_1.0.1 edgeR_3.10.2 limma_3.24.13

loaded via a namespace (and not attached):
[1] Rcpp_0.11.6 digest_0.6.8 MASS_7.3-42 grid_3.2.1 plyr_1.8.3
[6] gtable_0.1.2 magrittr_1.5 scales_0.2.5 stringi_0.5-5 proto_0.3-10
[11] tools_3.2.1 stringr_1.0.0 munsell_0.4.2 colorspace_1.2-6

[EarlyAdapter]

heatmap alternative in R

Hi emolinari,

I generate correlation and hierarchical clustering heatmaps using the pheatmap package. I find it very flexible and fairly intuitive (well, for R users, to be fair).

The default behavior is to cluster data but it can also be set up for basic correlation.

library(pheatmap)
counts<-read.csv("norm.count.data",header=T,sep="\t")
data<-counts[-1]
pheatmap(cor(data))


links:

https://cran.r-project.org/web/packages/pheatmap/index.html

https://www.biostars.org/p/66079/

[emolinari]

Hi emolinari,

I generate correlation and hierarchical clustering heatmaps using the pheatmap package. I find it very flexible and fairly intuitive (well, for R users, to be fair).

The default behavior is to cluster data but it can also be set up for basic correlation.

library(pheatmap)
counts<-read.csv("norm.count.data",header=T,sep="\t")
data<-counts[-1]
pheatmap(cor(data))


links:

https://cran.r-project.org/web/packages/pheatmap/index.html

https://www.biostars.org/p/66079/

Hi EarlyAdapter,

I used Pheatmap recently too, and I really like it. In my opinion makes way better heat maps than heatmap.2. Also, I figured out the mistake in my plot -rows and columns were inverted...silly!
Thanks for the heads up!
Manu

[daanum]

Hi,

How do I interpret the image of a hierarchical tree attached here?
The tree is created with euclidean distance using complete linkage method.

Thanks.