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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/quickPerCellQC.R
\name{quickPerCellQC}
\alias{quickPerCellQC}
\alias{quickPerCellQC,ANY-method}
\alias{quickPerCellQC,SummarizedExperiment-method}
\title{Quick cell-level QC}
\usage{
quickPerCellQC(x, ...)
\S4method{quickPerCellQC}{ANY}(
x,
sum.field = "sum",
detected.field = "detected",
sub.fields = NULL,
...,
lib_size = NULL,
n_features = NULL,
percent_subsets = NULL
)
\S4method{quickPerCellQC}{SummarizedExperiment}(
x,
...,
subsets = NULL,
assay.type = "counts",
other.args = list()
)
}
\arguments{
\item{x}{A \linkS4class{DataFrame} containing per-cell QC statistics, as computed by \code{\link{perCellQCMetrics}}.
Alternatively, a \linkS4class{SummarizedExperiment} object that can be used to create such a DataFrame via \code{\link{perCellQCMetrics}}.}
\item{...}{For the generic, further arguments to pass to specific methods.
For the ANY method, further arguments to pass to \code{\link{isOutlier}}.
For the SummarizedExperiment method, further arguments to pass to the ANY method.}
\item{sum.field}{String specifying the column of \code{x} containing the library size for each cell.}
\item{detected.field}{String specifying the column of \code{x} containing the number of detected features per cell.}
\item{sub.fields}{Character vector specifying the column(s) of \code{x} containing the percentage of counts in subsets of \dQuote{control features}, usually mitochondrial genes or spike-in transcripts.
If set to \code{TRUE}, this will default to all columns in \code{x} with names following the patterns \code{"subsets_.*_percent"} and \code{"altexps_.*_percent"}.}
\item{lib_size, n_features, percent_subsets}{Soft-deprecated equivalents of the arguments above.}
\item{subsets, assay.type}{Arguments to pass to the \code{\link{perCellQCMetrics}} function, exposed here for convenience.}
\item{other.args}{A named list containing other arguments to pass to the \code{\link{perCellQCMetrics}} function.}
}
\value{
A \linkS4class{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 \code{discard} column indicating whether the cell should be discarded.
}
\description{
A convenient utility that identifies low-quality cells based on frequently used QC metrics.
}
\details{
The ANY method simply calls \code{\link{isOutlier}} on the various QC metrics in \code{x}.
\itemize{
\item For \code{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.
\item For \code{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.
\item For each column specified by \code{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 \code{...}.
The SummarizedExperiment method calls \code{\link{perCellQCMetrics}} before running outlier detection.
This is simply a convenient wrapper that avoids the need for two separate function calls in routine applications.
}
\examples{
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))
}
\seealso{
\code{\link{perCellQCMetrics}}, for calculation of these metrics.
\code{\link{isOutlier}}, to identify outliers with a MAD-based approach.
}
\author{
Aaron Lun
}
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