quickPerCellQC {scater}R Documentation

Quick cell-level QC

Description

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

Usage

quickPerCellQC(
  df,
  lib_size = "sum",
  n_features = "detected",
  percent_subsets = NULL,
  ...
)

Arguments

df

A DataFrame containing per-cell QC statistics, as computed by perCellQCMetrics.

lib_size

String specifying the column of df containing the library size for each cell.

n_features

String specifying the column of df containing the number of detected features per cell.

percent_subsets

String specifying the column(s) of df containing the percentage of counts in subsets of “control features”.

...

Further arguments to pass to isOutlier.

Details

This function simply calls isOutlier on the various QC metrics in df.

Users can change the number of MADs used to define an outlier or specify batches by passing appropriate arguments to ....

Value

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.

Author(s)

Aaron Lun

See Also

perCellQCMetrics, for calculation of these metrics.

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

Examples

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

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


[Package scater version 1.16.2 Index]