noiseq {NOISeq} | R Documentation

## 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.
`k` | 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).
`lc` | 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.
`v` | 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`

### Author(s)

Sonia Tarazona

### 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.

### See Also

`readData`.

### Examples

    ## 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)

[Package NOISeq version 2.24.0 Index]