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FertigLab / CoGAPS

Bayesian MCMC matrix factorization algorithm
https://www.bioconductor.org/packages/release/bioc/html/CoGAPS.html
BSD 3-Clause "New" or "Revised" License
61 stars 17 forks source link

RunCoGAPS on Seurat object #61

Closed YuliaInn closed 2 years ago

YuliaInn commented 2 years ago

Hello,

I found the idea of finding patterns and projecting them into similar datasets very promising in such a challenging part of scRNA-seq analysis as cell identification. I am trying to annotate my own data using CoGAPS and I found this tutorial that shows how to run CoGAPS on Seurat objects. The function that was used there is called RunCoGAPS, but I can't find it in the package documentation. I have several questions about that function:

  1. does it use all genes or only highly variable ones?
  2. which data slot does it use? normalized I guess?
  3. will the cell embeddings from RunCoGAPS be similar to the result of scCoGAPS on a scRNA-seq normalized count matrix?

thank you, Yulia

ejfertig commented 2 years ago

Elana J. Fertig, PhD Director of the Quantitative Sciences Division co-Director Convergence Institute Associate Cancer Center Director of Quantitative Sciences Daniel Nathans Scientific Innovator Associate Professor of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics Johns Hopkins University https://fertiglab.com @FertigLab

On Jan 14, 2022, at 10:52 AM, YuliaInn @.**@.>> wrote:

Hello,

I found the idea of finding patterns and projecting them into similar datasets very promising in such a challenging part of scRNA-seq analysis as cell identification. I am trying to annotate my own data using CoGAPS and I found this tutorialhttps://nam02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhtmlpreview.github.io%2F%3Fhttps%3A%2F%2Fgithub.com%2Fsatijalab%2Fseurat-wrappers%2Fblob%2Fmaster%2Fdocs%2Fcogaps.html&data=04%7C01%7Cejfertig%40jhmi.edu%7C894877db0a8c4850bc2108d9d775d20c%7C9fa4f438b1e6473b803f86f8aedf0dec%7C0%7C0%7C637777723314348656%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=l2j%2BOJdG2eQy%2FVFBov6g4q3jgrCf4%2F2KFLRAZyrsYYA%3D&reserved=0 that shows how to run CoGAPS on Seurat objects. The function that was used there is called RunCoGAPS, but I can't find it in the package documentation. I have several questions about that function:

  1. does it use all genes or only highly variable ones?

You can select either. Using all genes will increase run time but provide more information. Genes selected need to have non-zero variance for the algorithm to work.

  1. which data slot does it use? normalized I guess?

Correct, normalized data.

  1. will the cell embeddings from RunCoGAPS be similar to the result of scCoGAPS on a scRNA-seq normalized count matrix?

They should be identical.

thank you, Yulia

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YuliaInn commented 2 years ago

thank you very much for your prompt reply