# "A,0,1.33447078154433,0,0,0,0,0,0,0.We began the editing process in the last episode, covering some of the basic adjustments you can make in Lightroom. # "gene_to_human_" "gene_to_human_"įor (sedfile in sedfiles) system2("sed", c("-i", "-f", sedfile, "example3.csv")) For this example, I'll limit it to 5 at a time, though sed can handle many more than that: Sys.which("split") If so, this can absolutely be done piecemeal.
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Lastly, if there are many many many translations to do, I don't know if sed is going to crack under pressure of too many commands. $ echo "hello world, goodbye world" | sed -e 's/world/globe/g' These are all replacing the first instance, you can make it global replacement: $ echo "hello world, goodbye world" | sed -e 's/world/globe/' $ echo "hello world, goodbye world" | sed -e 's/hell/quuz/' -e 's/good/bad/' Multiple commands can be executed many ways, including: $ echo "hello world, goodbye world" | sed -e 's/hell/quux/ s/good/bad/' The sed command we're using is the s/old/new/ format, where on the command-line, $ echo "hello world" | sed -e 's/hell/h-e-hockeysticks/' Or if you need to split it: $ split -l 5 gene_to_d gene_to_human_d. (Assuming that you've created "gene_to_d".) $ curl -s -o example.csv Though until you're happy/comfortable with things, I think keeping the original file around is likely "A Good Thing (tm)". If your drive space is tight and/or you feel daring and want to modify it truly in-place (overwriting the original), then add the -i argument to sed: readLines("example.csv", n = 3) There should be no compromise either way, so use whatever you're comfortable with. I'm doing this from R, but frankly it can just as easily be done from a command-line ( cmd.exe, bash, whatever). Typically, if any of the values in a column require quotes, then all of the values will be quoted, but that's just empirical on my part.Īssuming there are more than "a few" genes to transfer, it's likely better to put the translations into a file and tell sed to read commands from there. I'll assume none of the genes have spaces (which might encourage some CSV-editing tools to use quotes) or commas (requiring quotes). Since the source data doesn't have quotes, simple patterns should suffice. (BTW: the fact that the gene is in the first column makes this much more feasible and robust.) genes <- unique(fread("example.csv", select = 1))] Forgive me for not knowing those off the top of my head. I'm going to assume that you have a translation from gene to human (gene). Thanks for any thoughts or ideas you have! I know that you can read in a specific column with data.table, but I was wondering if there was a way to write a single column to replace the one from the original csv? It just seems that would be more efficient than reading in an entire dataset just to fix one column. This is an example of what the data should look like where I want to change the values from the gene column to the human equivalent. I have a really large CSV that is a normalized gene expression matrix for a huuuuuuge single cell RNAseq dataset, and I made the mistake of not changing the mouse gene names to the human equivalents.