How to create PCA plot and heatmaps using only significant genes in edgeR ?
How to create PCA plot and heatmaps using only significant genes in edgeR ?
I have 10 genes at signficant different levels between comparison groups. How can I create PCA and heatmaps with only those 10 genes.
Suggestions on R code will be appreciated !
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
How to generate a 2D PCA plot from bulk RNA-seq data (log2 CPM) using the PCAtools?
written by vHelmholtz •
-
How to make circos plot in R for miRNA and target genes retrieved from targetscan?
written by Björn •
-
How to create heatmaps only with DE genes in edgeR
written by Björn •
-
Why DE genes are not among highly variable genes between groups
written by Björn •
-
Stripchart to show expression of individual miRNA
written by Björn •
-
How to create design matrix in edgeR
written by Björn •
-
Two color microarray Heatmap
written by stomcan •
-
How to visualize gene difference with PCA?
written by carl.h •
-
Expression variability between 2 groups of genes
written by kintany •
-
DESeq PCA plot and edgeR MDS plot
written by tonja.r
Hey Björn, Have you not searched for how to do this? There is extensive material across the World Wide Web (and on Biostars) about how to do this. Kevin
Thanks Kevin, I used standard edgeR code for PCA but wanted to create PCA for 50 DE genes out of 2000. Use only 50 genes out of 2000 in PCA plot.
Okay. You could just filter your logCPM data to include only your genes of interest, and then regenerate the PCA bi-plot. I have also put code here: A: PCA plot from read count matrix from RNA-Seq
wouldn't it be more or at least equally interesting to see where the data is in the PCA on all data?
A good point, Carambakaracho
Actually, this is not appropriate. If you are taking genes which you a priori know that are different between your samples and use those for a PCA then you will of course get a separation between your experimental groups. That's a circular analysis or "double dipping".
have a look at varistran R package, specifically at