I received a large sc-RNA-seq data matrix, as of the nature of sc-RNA-seq compared to bulk-RNA-seq the data matrix is very sparse. Moreover, due to the fact that this is single cell data the cell progress stages become more dominant and might be dominant in finding the PC's.

I want to apply a dimensionality reduction method, such as PCA or tSNE, in order to enter it to some working algorithm to find principle curve for pathway analysis.

Ive encountered Bailey paper: "Principal Component Analysis with Noisy and/or Missing Data", but not sure if it will help me in the case of sc-RNA-seq.

Would appreciate some reference link or other help Thanks

  • $\begingroup$ Welcome to BioinfoSE. This is a fairly standard question, and its fairly easy as everything is in Seurat v3. Beyond that you will need to give information on the data set shape and the field (human genetics?). $\endgroup$ – Michael Aug 4 '19 at 9:37
  • $\begingroup$ @MichaelG. I'm not familiar with Seurat v3, is it a package that handle sparse data? regarding the data, its expression dataset of tumors it is size ~(20000, 500) but with a lot of empty entries; sorry if I'm not that clear I'm just getting into the field. $\endgroup$ – David S Aug 4 '19 at 14:44
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    $\begingroup$ This tutorial would be a good start: satijalab.org/seurat/v3.0/pbmc3k_tutorial.html $\endgroup$ – Mack123456 Aug 4 '19 at 21:02

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