Comments (7)
Hi, @MartinaVillanueva,
What exactly are you plotting here?
Are these the mutation rates?
Assuming those are what you are plotting here, it is likely due to how UMI deduplication works. When reads with the same cell BC and UMI that maps to the same gene is observed, the read with the most conversions of interest is selected.
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Thanks! My understanding is that when Martin calls for TC,GA (with --conversion TC,GA
argument in dynast count), the TC, GA mutation rates are different from when you call for TC or GA separately (with --conversion TC
or --conversion GA
argument in dynast count). And when calling for TC or GA, the corresponding TC/GA mutation rate is higher than when you don't look for it. Is any special treatment for the mutation you asked for (via ---conversion
) comparing the rest mutations?
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Exactly @Xiaojieqiu! Does that make sense @Lioscro ?
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Take a look at GA conversion and how it it lower when we don't look for the conversion (last slide) vs when we do look for it (the top 2 slides)
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I see what you mean. This is because in the UMI deduplication step, which read is selected depends on the number of conversions (see my previous comment). When you supply --conversion TC,GA
, the read with the most TC+GA conversions is selected; when you supply --conversion GA
, the read with the most GA conversions is selected; and vice-versa when you supply --conversion TC
.
(To be exact, the order of priority is 1) the read that maps to the transcriptome (exon only), 2) the read that has the highest alignment score, 3) read with the highest sum of the provided --conversion
.)
Does that make sense? So it seems that you have many reads per UMI that map to the same gene, do not map to exons only, have (equal) maximum alignment score, but have quite different conversion numbers.
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I see. And so the reason we see changes in other conversions (see blue and yellow circles) is because based on the transcripts that were selected to have the conversion of interest, it changes the background. Is that right?
Would you expect this to affect the accuracy of calling new / old transcripts?
210809_dual_labeling_2.pdf
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Related Issues (12)
- Exception on dynast estimate - cell barcode names HOT 4
- Motivation for using consensus HOT 3
- Consensus.bam lost gene-tag after completion HOT 2
- Recruiting Python Programmers to Develop Predictive Single Cell Spatiotemporal Analysis Toolkit HOT 1
- dynast count error:forward_strand = df_counts['GX'].map(lambda gx: gene_infos[gx]['strand']) == '-' KeyError: '-'
- dynast --list option not suitable HOT 1
- dynast estimate: behavior for genes for which estimation is skipped and --reads argument HOT 3
- dynast count --conversion
- Align: not enough memory for BAM sorting potentially due to BinsN too high HOT 5
- Dynast count BamError: Some paired reads do not have mates HOT 3
- Bug in 'estimate' when p_e is taken from control HOT 4
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