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Detect outliers based on QC metrics (> 3 upper MAD for mito_percent, < 3 lower MAD for sum_umi/sum_gene) using scuttle:isOutlier
Detect doublets using scDblFinder::computeDoubletDensity with 2000 HVGs; drop if doubletScore >= 2.75.
NOTE: Because one sample had a bimodal distribution for mito_percent and 2 for sum_umi/sum_gene, I subset to all other samples to calculate a shared MAD for outlier detection (built in functionality of subset input for 'scuttle::isOutlier').
The text was updated successfully, but these errors were encountered:
QC roadmap
Step 1: Detect and remove empty droplets using
DropletUtils::emptyDrops
barcodeRanks
) and export droplet scoresStep 2: Remove empty droplets and calculate QC metrics per droplet
scuttle:isOutlier
scDblFinder::computeDoubletDensity
with 2000 HVGs; drop if doubletScore >= 2.75.The text was updated successfully, but these errors were encountered: