scRNA reference matrix issues

[SBaek613]

Hi, thanks for the great tool.

I have been trying this tool out for deconvolution of TCGA bulk-RNA dataset with scRNA reference matrix of my own.

I am mainly using Seurat for scRNA-seq data and as always, the main problem is that it's impossible to extract non-sparse matrix from Seurat due to limitation of matrix format (with maximum number of elements being ~2^31).

Originally, I have about 180,000 cells with 40000 features and I was able to filter features down to about ~20000 genes. I would like to keep the cell number as same as possible but that is probably impossible to do. As far as I know, this tool does not accept sparse matrix with dgCMatrix format or accept unloaded 'txt' files stored in the drive.

So I came up with a couple of 'work-around' to this situation but not sure which one would be the best fitting for this tool.

1. Randomly subset the data. Bringing it down to about 1/3 of original cell counts would give me non-sparse matrix without errors.
2. Make collapsed count matrix for each major cell type. I read from the tutorial that I can use collapsed count matrix, but I am a bit worried that doing that might impact the results. And if I were to use the collapsed matrix, would I just combine counts of each gene for each cell type?

I would appreciate any advice!

[tinyi]

Thank you for your interest in our work. Yes. The second solutions is more recommended. By collapsing, simply add up the reads within each cell state(subtypes) to make a gene expression profile (use the argument input.type=“GEP”), and label each row of GEP as the corresponding cell state or cell type. By doing so, the result will be the same as using the raw count. Best, Tinyi

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On Wed, Jun 8, 2022 at 10:11 PM Seungbyn ***@***.***> wrote: Hi, thanks for the great tool. I have been trying this tool out for deconvolution of TCGA bulk-RNA dataset with scRNA reference matrix of my own. I am mainly using Seurat for scRNA-seq data and as always, the main problem is that it's impossible to extract non-sparse matrix from Seurat due to limitation of matrix format (with maximum number of elements being ~2^31). Originally, I have about 180,000 cells with 40000 features and I was able to filter features down to about ~20000 genes. I would like to keep the cell number as same as possible but that is probably impossible to do. As far as I know, this tool does not accept sparse matrix with dgCMatrix format or accept unloaded 'txt' files stored in the drive. So I came up with a couple of 'work-around' to this situation but not sure which one would be the best fitting for this tool. 1. Randomly subset the data. Bringing it down to about 1/3 of original cell counts would give me non-sparse matrix without errors. 2. Make collapsed count matrix for each major cell type. I read from the tutorial that I can use collapsed count matrix, but I am a bit worried that doing that might impact the results. And if I were to use the collapsed matrix, would I just combine counts of each gene for each cell type? I would appreciate any advice! — Reply to this email directly, view it on GitHub <#26>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AB4NHS5SCXHJL26CZ5YQXZTVOFHEJANCNFSM5YIODODA> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>