isOutlier: Identify if a cell is an outlier based on a metric

In dynverse/scaterlegacy: Single-cell analysis toolkit for gene expression data in R

Description

Convenience function to determine which values for a metric are outliers based on median-absolute-deviation (MAD).

Usage

isOutlier(metric, nmads = 5, type = c("both", "lower", "higher"),
  log = FALSE, subset = NULL, batch = NULL)

Arguments

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 NULL, all values are used. Missing values will trigger a warning and will be automatically ignored.
batch	factor of length equal to metric, specifying the batch to which each observation belongs. A median/MAD is calculated for each batch, and outliers are then identified within each batch.

Value

a logical vector of the same length as the metric argument

Examples

data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data=sc_example_cell_info)
rownames(pd) <- pd$Cell
example_sceset <- newSCESet(countData=sc_example_counts, phenoData=pd)
example_sceset <- calculateQCMetrics(example_sceset)

## with a set of feature controls defined
example_sceset <- calculateQCMetrics(example_sceset, feature_controls = 1:40)
isOutlier(example_sceset$total_counts, nmads = 3)

dynverse/scaterlegacy documentation built on Feb. 17, 2020, 5:07 a.m.