Dear all, I am so confused, I would really appreciate help.
I have a table of read counts from RNASeq data (i.e. just a table, where each column is a sample, and each row is a gene, and the cells are read counts that range from 0 to say 10,000). I want to convert these to TPM values and output a matrix/table of TPM gene expression values (each row still a gene name, each column still a sample name).
I was advised that DESeq2 could do this. I was reading the manual: https://bioc.ism.ac.jp/packages/2.14/bioc/vignettes/DESeq2/inst/doc/beginner.pdf. I thought section 2.4.3 "Starting from count tables" would have all the information I needed. However, the example seems to jump straight from reading in count data, to producing differentially expressed genes. I've found this to be the case with a few places I've read, and I can't seem to find a simple answer anywhere.
Is it possible to input just a table of read counts, and output a table of TMP gene expression values.
The code would be like:
library("DESeq2") count_data <-read.table("MatrixOfReadCount",header=T)
And then I was trying to do something like:
countData <-assay(read_table) (but I was getting errors: Unable to find an inherited method for function ‘assay’ for signature ‘"data.frame", "missing"’)
(I also have other examples of what I tried, but there were all pointless).
I'm just so confused. I've also read other places and forums, and I cannot find how to simply "read in a matrix of read counts (row names = genes, column names = sample), and output a matrix of TPM gene expression values (row names = genes, column names = sample), without doing any differential expression analysis".
If someone could help in any way, I would appreciate it. Can this be done?