quickPerCellQC: Quick cell-level QC
In scuttle: Single-Cell RNA-Seq Analysis Utilities

Description Usage Arguments Details Value Author(s) See Also Examples

A convenient utility that identifies low-quality cells based on frequently used QC metrics.

quickPerCellQC(x, ...)

## S4 method for signature 'ANY'
quickPerCellQC(
  x,
  sum.field = "sum",
  detected.field = "detected",
  sub.fields = NULL,
  ...,
  lib_size = NULL,
  n_features = NULL,
  percent_subsets = NULL
)

## S4 method for signature 'SummarizedExperiment'
quickPerCellQC(
  x,
  ...,
  subsets = NULL,
  assay.type = "counts",
  other.args = list()
)

`x`	A DataFrame containing per-cell QC statistics, as computed by `perCellQCMetrics`. Alternatively, a SummarizedExperiment object that can be used to create such a DataFrame via `perCellQCMetrics`.
`...`	For the generic, further arguments to pass to specific methods. For the ANY method, further arguments to pass to `isOutlier`. For the SummarizedExperiment method, further arguments to pass to the ANY method.
`sum.field`	String specifying the column of `x` containing the library size for each cell.
`detected.field`	String specifying the column of `x` containing the number of detected features per cell.
`sub.fields`	Character vector specifying the column(s) of `x` containing the percentage of counts in subsets of “control features”, usually mitochondrial genes or spike-in transcripts. If set to `TRUE`, this will default to all columns in `x` with names following the patterns `"subsets_._percent"` and `"altexps_._percent"`.
`lib_size, n_features, percent_subsets`	Soft-deprecated equivalents of the arguments above.
`subsets, assay.type`	Arguments to pass to the `perCellQCMetrics` function, exposed here for convenience.
`other.args`	A named list containing other arguments to pass to the `perCellQCMetrics` function.

The ANY method simply calls isOutlier on the various QC metrics in x.

For sum.field, small outliers are detected. These are considered to represent low-quality cells that have not been insufficiently sequenced. Detection is performed on the log-scale to adjust for a heavy right tail and to improve resolution at zero.
For detected.field, small outliers are detected. These are considered to represent low-quality cells with low-complexity libraries. Detection is performed on the log-scale to adjust for a heavy right tail. This is done on the log-scale to adjust for a heavy right tail and to improve resolution at zero.
For each column specified by sub.fields, large outliers are detected. This aims to remove cells with high spike-in or mitochondrial content, usually corresponding to damaged cells. While these distributions often have heavy right tails, the putative low-quality cells are often present in this tail; thus, transformation is not performed to ensure maintain resolution of the filter.

Users can control the outlier detection (e.g., change the number of MADs, specify batches) by passing appropriate arguments to ....

The SummarizedExperiment method calls perCellQCMetrics before running outlier detection. This is simply a convenient wrapper that avoids the need for two separate function calls in routine applications.

A DataFrame with one row per cell and containing columns of logical vectors. Each column specifies a reason for why a cell was considered to be low quality, with the final discard column indicating whether the cell should be discarded.

Aaron Lun

perCellQCMetrics, for calculation of these metrics.

isOutlier, to identify outliers with a MAD-based approach.

example_sce <- mockSCE()
x <- perCellQCMetrics(example_sce, subsets=list(Mito=1:100))

discarded <- quickPerCellQC(x, percent_subsets=c(
    "subsets_Mito_percent", "altexps_Spikes_percent"))
colSums(as.data.frame(discarded))