How to estimate the computing power needed for scRNA-seq

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10 months ago

official.profile ▴ 20

I am planning to perform a scRNA-seq analysis of a few million cells collected from various experiments. However, I need to estimate what computational power will be approximately needed to process bam and fastq files up to the DEGs, UMAPs, etc.

I know that this is a multidimensional problem, but I am looking for some estimations or any information that would help me with this issue (disk size, RAM, number of cores, etc.)

scRNA-seq computing-power • 566 views

ADD COMMENT • link updated 10 months ago by Ram 43k • written 10 months ago by official.profile ▴ 20

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Do you plan to have all these millions of cells in the same analysis or is it some sort of meta-analysis approach? Add some details please. Is it going to be R or python?

ADD REPLY • link 10 months ago by ATpoint 82k

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Thank you for your reply. It's hard to tell at this point what exact strategy I will adopt. Some significant integration will take place for sure, for example by tissue. Regarding the R and python - I don't think it is that important in terms of the computing power, but lets assume that Seurat, Harmony and MAST will be employed.

ADD REPLY • link 10 months ago by official.profile ▴ 20

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