https://github.com/greenelab/mito-filtering
An analysis that once lived in greenelab/sc-cancer-hgsc/analyses/differential_expression/smart_QC.Rmd has morphed into a potential preprint, so I'm moving the analyses related to that here.
https://github.com/greenelab/mito-filtering
analysis quality-control single-cell single-cell-rna-seq
Last synced: 7 months ago
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An analysis that once lived in greenelab/sc-cancer-hgsc/analyses/differential_expression/smart_QC.Rmd has morphed into a potential preprint, so I'm moving the analyses related to that here.
- Host: GitHub
- URL: https://github.com/greenelab/mito-filtering
- Owner: greenelab
- Created: 2020-03-03T19:14:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-11T02:20:25.000Z (over 4 years ago)
- Last Synced: 2025-01-13T00:42:17.250Z (9 months ago)
- Topics: analysis, quality-control, single-cell, single-cell-rna-seq
- Language: HTML
- Homepage:
- Size: 125 MB
- Stars: 0
- Watchers: 5
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# mito-filtering
When we were doing my first scRNA-seq analyses, it became clear that some cells had a very high amount of expression of mitochondrial genes, which almost always indicates that the cell has been ruptured/damaged during the isolation process.
People usually throw out all cells with more than x% of reads from mtRNA, usually 5% or 10%, but that was extremely stringent for our tumors and didn't leave us with much to analyze.
We ended up looking at library complexity along with % mitochondria of individual cells, and generating a linear mixture model to determine if a cell was likely displaying characteristics of a healthy or a compromised cell.
Now, we're explanding that work into a tool called miQC, which has numerous advantages over the simple cutoff approach.
This analysis once lived in greenelab/sc-cancer-hgsc/analyses/differential_expression/smart_QC.Rmd.