quickPerCellQC {scuttle} | R Documentation

## Quick cell-level QC

### Description

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

### Usage

    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(),
      filter = TRUE
    )

### Arguments

`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.
`filter` | Logical scalar indicating whether to filter out low-quality cells from `x`.

### Details

For DataFrame `x`, this function simply calls `perCellQCFilters`. The latter should be directly used in such cases; DataFrame inputs are soft-deprecated here.

For SummarizedExperiment `x`, this function is simply a convenient wrapper around `perCellQCMetrics` and `perCellQCFilters`.

### Value

If `filter=FALSE` or `x` is a DataFrame, a DataFrame is returned 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.

If `filter=TRUE`, `x` is returned with the low-quality cells removed. QC statistics and filtering information for all remaining cells are stored in the `colData`.

### Author(s)

Aaron Lun

### See Also

`perCellQCMetrics`, for calculation of these metrics.

`perCellQCFilters`, to define filter thresholds based on those metrics.

### Examples

    example_sce <- mockSCE()

    filtered_sce <- quickPerCellQC(example_sce, subsets=list(Mito=1:100),
        sub.fields=c("subsets_Mito_percent", "altexps_Spikes_percent"))
    ncol(filtered_sce)

    # Same result as the longer chain of expressions:
    stats <- perCellQCMetrics(example_sce, subsets=list(Mito=1:100))
    discard <- perCellQCFilters(stats,
        sub.fields=c("subsets_Mito_percent", "altexps_Spikes_percent"))
    filtered_sce2 <- example_sce[,!discard$discard]
    ncol(filtered_sce2)

[Package scuttle version 1.4.0 Index]