outlierFinder: Identifies outliers in a similarity matrix.

In doppelgangR: Identify likely duplicate samples from genomic or meta-data

Description

By default uses the Fisher z-transform for Pearson correlation (atanh), and identifies outliers as those above the quantile of a skew-t distribution with mean and standard deviation estimated from the z-transformed matrix. The quantile is calculated from the Bonferroni-corrected cumulative probability of the upper tail.

Usage

outlierFinder(
  similarity.mat,
  bonf.prob = 0.05,
  transFun = atanh,
  normal.upper.thresh = NULL,
  tail = "upper"
)

Arguments

similarity.mat	A matrix of similarities - larger values mean more similar.
bonf.prob	Bonferroni-corrected probability. A raw.prob is calculated by dividing this by the number of non-missing values in similarity.mat, and the rejection threshold is qnorm(1-raw.prob, mean, sd) where mean and sd are estimated from the transFun-transformed similarity.mat.
transFun	A function applied to the numeric values of similarity.mat, that should result in normally-distributed values.
normal.upper.thresh	Instead of specifying bonf.prob and transFun, an upper similarity threshold can be set, and values above this will be considered likely duplicates. If specified, this over-rides bonf.prob.
tail	"upper" to look for samples with very high similarity values, "lower" to look for very low values, or "both" to look for both.

Value

Returns either NULL or a dataframe with three columns: sample1, sample2, and similarity.

Author(s)

Levi Waldron, Markus Riester, Marcel Ramos

Examples

library(curatedOvarianData)
data(GSE32063_eset)
cormat <- cor(exprs(GSE32063_eset))
outlierFinder(cormat, bonf.prob = 0.05)

doppelgangR documentation built on Nov. 8, 2020, 6:36 p.m.