Description Subsetting by row Filtering by mean Author(s)

Details on how gene selection is performed in almost all scran functions.

For functions accepting some gene-by-cell matrix `x`

, we can choose to perform calculations only on a subset of rows (i.e., genes) with the `subset.row`

argument.
This can be a logical, integer or character vector indicating the rows of `x`

to use.
If a character vector, it must contain the names of the rows in `x`

.
Future support will be added for more esoteric subsetting vectors like the Bioconductor Rle classes.

The output of running a function with `subset.row`

will *always* be the same as the output of subsetting `x`

beforehand and passing it into the function.
However, it is often more efficient to use `subset.row`

as we can avoid constructing an intermediate subsetted matrix.
The same reasoning applies for any `x`

that is a SingleCellExperiment object.

Some functions will have a `min.mean`

argument to filter out low-abundance genes prior to processing.
Depending on the function, the filter may be applied to the average library size-adjusted count computed by `calculateAverage`

, the average log-count,
or some other measure of abundance - see the documentation for each function for details.

Any filtering on `min.mean`

is automatically intersected with a specified `subset.row`

.
For example, only subsetted genes that pass the filter are retained if `subset.row`

is specified alongside `min.mean`

.

Aaron Lun

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