meta.oneside.noiseq {metaSeq} | R Documentation

## One-sided NOISeq for meta-analysis

### Description

NOISeq customized for one-sided test in meta-analysis. Parallel computing is also available by snow package.

### Usage

    meta.oneside.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 = 1, studies = NULL, cl = NULL)

### Arguments

`input` | Object of eSet class coming from readData function or other R packages such as DESeq.
`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).
`replicates` | In this argument, the type of replicates to be used is defined. Technical, biological or none. By default, technical replicates option is chosen. Note that "no" is automatically chosen against the study which has no replicate.
`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).
`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)]
`lc` | Length correction is done by dividing expression by length^lc. By default, lc = 1.
`studies` | A vector specifying which column in data are measured in common study. Its length must be equal to the number of column in data.
`cl` | cluster object in snow pacakge.

### Author(s)

Koki Tsuyuzaki, Itoshi Nikaido

### References

Tarazona, S. and Garcia-Alcalde, F. and Dopazo, J. and Ferrer, A. and Conesa, A. (2011) Differential expression in RNA-seq: A matter of depth. Genome Research, 21(12): 2213-2223

### See Also

`noiseq`

### Examples

    data(BreastCancer)
    library("snow")

    # Experimental condition (1: BreastCancer, 0: Normal)
    flag1 <- c(1,1,1,0,0, 1,0, 1,1,1,1,1,1,1,0, 1,1,0)

    # Source of data
    flag2 <- c("A","A","A","A","A", "B","B", "C","C","C","C","C","C","C","C", "D","D","D")

    # readData function for meta-analysis
    cds <- meta.readData(data = BreastCancer, factor = flag1, studies = flag2)

    # oneside NOISeq for meta-analysis
    # cl <- makeCluster(4, "SOCK")
    # result <- meta.oneside.noiseq(cds, k = 0.5, norm = "tmm", replicates = "biological", factor = flag1, conditions = c(1, 0), studies = flag2, cl = cl)
    # stopCluster(cl)

    # Script above is very time-consumming step. Please use this pre-calculated result instead
    data(Result.Meta)
    result <- Result.Meta

    # Fisher's method (without weighting)
    F <- Fisher.test(result)
    str(F)

    # Stouffer's method (with weighting by sample-size)
    S <- Stouffer.test(result)
    str(S)

[Package metaSeq version 1.26.0 Index]