How to estimate the computing power needed for scRNA-seq
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.)
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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?
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.