metaSeq (version 1.12.0)

meta.oneside.noiseq: 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.

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)