isOutlier {scater} | R Documentation |
Convenience function to determine which values for a metric are outliers based on median-absolute-deviation (MAD).
isOutlier(metric, nmads = 5, type = c("both", "lower", "higher"), log = FALSE, subset = NULL, batch = NULL, min.diff = NA)
metric |
numeric or integer vector of values for a metric |
nmads |
scalar, number of median-absolute-deviations away from median required for a value to be called an outlier |
type |
character scalar, choice indicate whether outliers should be looked for at both tails (default: "both") or only at the lower end ("lower") or the higher end ("higher") |
log |
logical, should the values of the metric be transformed to the log10 scale before computing median-absolute-deviation for outlier detection? |
subset |
logical or integer vector, which subset of values should be
used to calculate the median/MAD? If |
batch |
factor of length equal to |
min.diff |
numeric scalar indicating the minimum difference from the
median to consider as an outlier. The outlier threshold is defined from the
larger of |
a logical vector of the same length as the metric
argument
data("sc_example_counts") data("sc_example_cell_info") example_sce <- SingleCellExperiment( assays = list(counts = sc_example_counts), colData = sc_example_cell_info) example_sce <- calculateQCMetrics(example_sce) ## with a set of feature controls defined example_sce <- calculateQCMetrics(example_sce, feature_controls = list(set1 = 1:40)) isOutlier(example_sce$total_counts, nmads = 3)