Question: yes, I probably do not quite understand heatmap
gravatar for Ming Tang
5.0 years ago by
Ming Tang2.6k
Houston/MD Anderson Cancer Center
Ming Tang2.6k wrote:

Hi Biostars,

I recently read a post on heatmap by Mick Watson

also see a previous post at biostars When Using Heatmap.2 From R To Make A Heatmap Of Microarray Data, How Are The Genes Clustered?

they cleared some of my confusions on heatmap. 

However, I still have questions on heatmap. Now, I have a microarray gene expression matrix (control vs Knockdown, triplicates for each condition) , and I want to draw a heatmap. there are several ways to select a subset of the genes to make a heatmap:

1) use genefilter package, to select the first 100 great varied genes.

e<- exprs(eset)
rv<- rowVars(e)
idx<- order(-rv)[1:100]
mat<- e[idx,]

2) select the genes that are differentially expressed with a p.adjust < 0.05 and ab(logFC) >1 

So, what do you use for selecting genes?  I will stick using pearson correlation as distance measure and scale row by using scale="row" in the heatmap.2 function.

ideally, I should see Knockdown samples cluster together, control samples cluster together. and green(down-regulated) and red(up-regulated) colors cluster together (by scaling with row, we are looking at the "shape" for each gene as in the Mick's post) . But my heatmap still looks not as I expected(I could not see clear patterns going no). Do you have any suggestions?

Thanks so much!


heatmap • 2.5k views
ADD COMMENTlink modified 5.0 years ago by Zhilong Jia1.5k • written 5.0 years ago by Ming Tang2.6k
gravatar for Zhilong Jia
5.0 years ago by
Zhilong Jia1.5k
Zhilong Jia1.5k wrote:

Many reasons for the unclear classification of samples. Such as the data itself. At the same time, you can try spearman or other distances. Also considering the normalization methods for the expression profiling.

ADD COMMENTlink written 5.0 years ago by Zhilong Jia1.5k
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