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The only type of QC that scater cannot do is read-level QC, but this information can easily be incorporated into a scater object when such metrics are available from other alignment or quantification tools.

Why mention this? I don't think it's an important caveat (or at least, not one that should be mentioned at the start before you've talked up scater). In fact, I don't even think about read-level QC, other than removing reads with low MAPQ scores during counting. I think it's fairly obvious that scater's responsibilities start at the read counts, and anything before that is up to the user. I suggest taking out this bit, or moving it to a later point if you really feel it must be said.

The QC metrics computed in scater include, for each cell...

A less clunky way to say it is:

Cell-specific QC metrics include...

These metrics (and others computed) are useful for building up a picture of the complexity of the transcriptome captured for the cell.

Keep it simple; why not just:

These metrics are useful for identifying low-quality cells - for example...

For each gene, QC metrics such as the average expression level and the proportion of cells in which the gene is expressed are computed. The metrics are used by scater to construct QC plots to explore the data and diagnose potential issues.

Briefly mention of what the gene-level QC metrics are for. Suggest:

For each gene, QC metrics such as the average expression level and the proportion of cells in which the gene is expressed are computed. This can be used to identify low-abundance genes or genes with high dropout rates that should be filtered out prior to downstream analyses. All of these metrics are used by scater to construct QC plots to diagnose potential issues with data quality.

davismcc / scaterPaperExtras

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