TobiTekath
commented
12 months ago Hi Yoav,
thank you for your continued interest in DTUrtle.
If you already have an aggregated csv file containing transcript counts for all your samples, you can omit the import_counts() step.
You can see from the help pages of run_drimseq() which formats for counts data are accepted.
In your case, it would be:
(sparse) matrix with feature counts, where the rows correspond to features (e.g. transcripts). One column per sample/cell with the count data for the specified features must be present (The names of these columns must match with the identifiers in id_col).So, you can easily read-in and correctly format your csv file, e.g. like this:
# read counts as data.frame
cts <- read.csv("transcript_matrix.csv")
# add TranscriptID as rownames
rownames(cts) <- cts$TranscriptID
# remove TranscriptID-column for matrix conversion
cts$TranscriptID <- NULL
# convert data.frame to matrix
cts <- as.matrix(cts)Please note: For statistically valid results, DTUrtle relies on actual raw count data. Direct use of abundance estimates (e.g. FPKM/RPKM or TPM etc.) is not recommended.
I hope this solves you problem.
Best, Tobi
yoavhadas
Hello again, I opened a new issue as a followup to: https://github.com/TobiTekath/DTUrtle/issues/6#issuecomment-1124182002
In my csv file, I have barcodes as columns and transcripts as rows:
TranscriptID,CCGCAGTCAGGACTCA,TCATACCACTTGAGTT,TGCGCGTTAATTACCA,AAGTAGAGTACTAAGT Transcript1,0,0,0,0 Transcript2,0,0,0,0 Transcript3,0,0,3.93,0
I can import it to R using read.csv("transcript_matrix.csv"); however, I am not sure what is the proper structure of the matrix and what you mean by: "specify the columns with the data manually."
Any help will be appreciated. Thanks Yoav.