I've taken this out of the comment section, as I'm trying to provide an answer, so here goes:
No offense, but I think you still want to take a little bit of time to consider what you really want to show in your heatmap, and where those number would come from.
For instance, in your scenario: if you want to show differences in expression between a few (two) groups (organs, for you), you probably want to show the relative expression of the genes of interest from all of your samples, and not not a two column matrix.
Consider googling around a bit for heatmaps, gene expression and R, or find one of your favorite publications from recent memory that have a heatmap figure you particularly liked -- what are they showing?
You should also read through some of the RNA-seq workflows, which also have their own expression-based heatmap section, such as:
- RNA-seq ... with limma, Glimma, and edgeR
- edgeR QLF
These begin from different starting (data) points, but the heatmap is constructed from some sort of normalized count data, which correspond to the RPKM values you have, which has been log transformed.
Once you understand what is being plotted in those vignettes, you might just go with the tools they used there, or learn how to use ComplexHeatmap and really up your game -- it's really a remarkable package with incredible documentation.
This is difficult to provide help with, because the answer depends on what you are trying to show in your heatmap. Once you figure that out, the values you need for the heatmap will be more obvious. You'll also need to know more about the data that you got, ie. whether not it's normalized, if so: how it was normalized, etc.
Also, I'm not sure that the title you chose for your post is all that informative ... what does this have to do with RStudio?
Dear Steve, I appreciate your prompt reply. My samples come from two different organs of mice which are treated with drug in different time points. I have received a normalized set of data in form of Excel file. There are columns including RPKM of each individual sample, base mean of samples vs each other (COLUMN B: brain vs heart baseman), pvalue of samples vs each other (COLUMN D: brain vs heart value) and finally logfc of samples vs each other(brain vs heart logfc). I aim to simply show the changes of expression from one organ to another organ for each individual gene in two columns side by side. However I can not do this with logfc values as they are values of one organ vs another organ. and I am not sure whether my input in R studio should be mean of RPKM (of biological replicates).
Are you talking about the pheatmap package for RNA-seq expression ? Like in this link ? https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/
Yes exactly! Although I am not sure about the input. What should be inside the excel file to import the R.
Here are all possible inputs for DESeq: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#input-data
Basically you require a table (of raw counts), with columns like this:
If you have this in a excel spreadsheet, you read the excel sheet and make sure everything is correct, and create a DESeq object by following the link above.