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The student who was working on scRNA seq of KO and WT lines has made a mistake and he mixed both lines and generate the final sequencing data. Now, we are having gene expression data but don't know which is KO and which is WT.

It's a genetic KO. Looking at the presence of absence of the KO gene might not be helpful here as its not deep sequenced.

Cells were extracted from two stable line of animal; one wild type and other Chd2 gene knocked out. As a sequencing output I am having one fastq file in the format 28+10+10+90 . I can generate a count matrix out of it; but I dont know which barcode from WT and with one from KO.

Is there any way to segregate the output based on the KO gene expression?

Thanks

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    $\begingroup$ You might be able to detect the genetic KO by looking for CNVs at the relevant loci, e.g. via infercnv (or infercnvpy), or copykat. You might get separate clusters on the UMAP from KO/WT, but it's hard to know $\endgroup$
    – Chris_Rands
    Jul 13, 2021 at 12:55

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If the samples were from different individual organisms you could try looking for mitochondrial variants that differentiate between them. You should have reasonable coverage of the entire mitochondrial mRNA transcriptome even in 10x datasets.

This tool might help: https://bioconductor.org/packages/devel/bioc/html/mitoClone2.html

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Answer from @haci, converted from comment:

If your mutation of interest is "covered" by the "sample read", you should be able to assign which cell comes from which line. By "sampe read", I mean some 90+ bp read that is used for sequencing the insert (no the read for the cell barcode & UMI).

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