detect.outliers {OutSeekR}R Documentation

Detect outliers

Description

Detect outliers in normalized RNA-seq data.

Usage

detect.outliers(
  data,
  num.null = 1000,
  initial.screen.method = c("fdr", "p.value"),
  p.value.threshold = 0.05,
  fdr.threshold = 0.01,
  kmeans.nstart = 1
)

Arguments

data

A matrix or data frame of normalized RNA-seq data, organized with transcripts on rows and samples on columns. Transcript identifiers should be stored as rownames(data).

num.null

The number of transcripts to generate when simulating from null distributions; default is 1000. We recommend using at least 10,000 iterations for publication-level results, with 100,000 or even one million iterations providing more robust estimates.

initial.screen.method

The statistical criterion for initial gene selection; valid options are 'FDR' and 'p-value'.

p.value.threshold

The p-value threshold for the outlier test; default is 0.05. Once the p-value for a sample exceeds p.value.threshold, testing for that transcript ceases, and all remaining samples will have p-values equal to NA.

fdr.threshold

The false discovery rate (FDR)-adjusted p-value threshold for determining the final count of outliers; default is 0.01.

kmeans.nstart

The number of random starts when computing k-means fraction; default is 1. See ?stats::kmeans for further details.

Value

A list consisting of the following entries:

Examples

data(outliers);
outliers.subset <- outliers[1:10,];
results <- detect.outliers(
   data = outliers.subset,
   num.null = 10
   );

[Package OutSeekR version 1.0.0 Index]