runDESeq: runDESeq

In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression

Description

This wrapper runs differential expression analysis via the DESeq package.

Usage

runDESeq(object, group = NULL, condition.a = NULL,
  condition.b = NULL, ngenes = NULL, fitType = c("parametric",
  "local"), method = c("pooled", "pooled-CR", "per-condition", "blind"),
  parallel = TRUE)

Arguments

object	An EMSet object that has undergone filtering and normalisation.
group	Name of the column in the colInfo dataframe where you have defined the conditions you would like to test. eg cluster to compare clusters identified by runCORE.
condition.a	Condition of the group you want to use as the baseline.
condition.b	Conditions of the group you want to compare to the baseline.
ngenes	Perform differential expression analysis using top number of genes. If omitted, this function will run analysis on ALL genes.
fitType	Method used to fit a dispersion-mean relation by DESeq. Options: parametric, local (Default).
method	Method used by DESeq to compute emperical dispersion. Options: pooled, pooled-CR, per-condition (Default), blind.
parallel	Run DESeq through parallelised wrapper (Default: TRUE)

Value

A data frame containing DESeq results

Examples

## Not run: 
library(DESeq)
cluster1_vs_others <- runDESeq(EMSet, group = "cluster", condition.a = "1",
condition.b = c("2", "3"), ngenes = 1500, fitType = "local", method = "per-condition")

## End(Not run)

IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.