Gene selection {scran} | R Documentation

## Gene selection

### Description

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

### Subsetting by row

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.

### Filtering by mean

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`.

### Author(s)

Aaron Lun

[Package scran version 1.16.0 Index]