noiseq: Differential expression method for technical replicates or no replicates at all

Description

noiseq computes differential expression between two experimental conditions from read count data (e.g. RNA-seq).

Usage

noiseq(input, k = 0.5, norm = c("rpkm","uqua","tmm","n"), 
replicates = c("technical","biological","no"), 
factor=NULL, conditions=NULL, pnr = 0.2, nss = 5, v = 0.02, lc = 0)

Arguments

input

Object of eSet class coming from readData function or other R packages such as DESeq.

factor

A string indicating the name of factor whose levels are the conditions to be compared.

conditions

A vector containing the two conditions to be compared by the differential expression algorithm (needed when the factor contains more than 2 different conditions).

replicates

In this argument, the type of replicates to be used is defined: "technical", "biological" or "no" replicates. By default, "technical" replicates option is chosen.

Counts equal to 0 are replaced by k. By default, k = 0.5.

norm

Normalization method. It can be one of "rpkm" (default), "uqua" (upper quartile), "tmm" (trimmed mean of M) or "n" (no normalization).

Length correction is done by dividing expression by length^lc. By default, lc = 0.

pnr

Percentage of the total reads used to simulated each sample when no replicates are available. By default, pnr = 0.2.

nss

Number of samples to simulate for each condition (nss>= 2). By default, nss = 5.

Variability in the simulated sample total reads. By default, v = 0.02. Sample total reads is computed as a random value from a uniform distribution in the interval [(pnr-v)*sum(counts), (pnr+v)*sum(counts)]

Value

The function returns an object of class Output

References

Bullard J.H., Purdom E., Hansen K.D. and Dudoit S. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinformatics 11(1):94+.

Mortazavi A., Williams B.A., McCue K., Schaeer L. and Wold B. (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods 5(7):621-628.

Robinson M.D. and Oshlack A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11(3):R25+.

Marioni, J.C. and Mason, C.E. and Mane, S.M. and Stephens, M. and Gilad, Y. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18: 1509--1517.

Examples

Run this code

## Load the input object from Marioni's data as returned by readData()
data(myCounts)

## Computing differential expression probability on RPKM-normalized data by NOISeq-real using factor "Tissue"
mynoiseq = noiseq(mydata, k = 0.5, norm = "rpkm", replicates = "technical", factor="Tissue", 
pnr = 0.2, nss = 5, v = 0.02, lc = 1)


## Computing differential expression probability on Upper Quartile normalized data by NOISeq-real 
## using factor "TissueRun" and comparing samples in Run 1 (levels "Kidney_1" and "Liver_1")
mynoiseq.uqua = noiseq(mydata, k = 0.5, norm = "uqua", replicates = "technical", factor="TissueRun", 
conditions = c("Kidney_1","Liver_1"), pnr = 0.2, nss = 5, v = 0.02, lc = 1)