| quickPerCellQC {scater} | R Documentation |
A convenient utility that identifies low-quality cells based on frequently used QC metrics.
quickPerCellQC( df, lib_size = "sum", n_features = "detected", percent_subsets = NULL, ... )
df |
A DataFrame containing per-cell QC statistics, as computed by |
lib_size |
String specifying the column of |
n_features |
String specifying the column of |
percent_subsets |
String specifying the column(s) of |
... |
Further arguments to pass to |
This function simply calls isOutlier on the various QC metrics in df.
For lib_size, small outliers are detected on the log-scale to remove cells with low library sizes.
For n_features, small outliers are detected on the log-scale to remove cells with few detected features.
For each field in percent_subsets, large outliers are detected on the original scale.
This aims to remove cells with high spike-in or mitochondrial content.
Users can change the number of MADs used to define an outlier or specify batches by passing appropriate arguments to ....
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()
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))