dEMO: Differential Expression and confidencial estimation for FC

Description Usage Arguments Details Value Author(s) See Also Examples

This function provides a confidencial interval from log2(proportions ratio) or log2(FC) where proportions belong to negative binomial population and from data sets by considering each row as a different experiment, from data sets by considering each row as a different experiment, where each row has nothing to do with the others rows. Overdispersion estimation is carried out with edgeR::estimateTagwiseDisp.default function. dEMO::EMOOD1sci function works row by row, so if you want to calculate dEMO confidence intervals when rows are independent experiments, a loop "for" is recomended, as is showed in the example

1 2	dEMOOD1sci(expr, condition, Fescd, n = NULL, cd, alpha = 0.05, method.adj = "BH")

`expr`	matrix where the first set of columns store the counts for each sample and the second set of columns store the library size for each sample. For instance, if "n1" is the library size from sample 1 and x1 is the counts from sample 1, and j is the number of samples which belong to the same condition that sample 1, the column 1 is x1 and "j+1" is n1, the column 2 is x2 and "j+2" is n2, and so on.
`condition`	Binary vector where 0 means control and 1 treatment
`Fescd`	is the data.frame which stores coefficients calculated by Fesc function from dEMO package
`cd`	it is the common disperssion estimated by `estimateCommonDisp` function from edgeR package
`alpha`	significance level for hypothesis test. Default value is 0.05
`method.adj`	It is the multiple testing correction method. Default value correspond to Benjamini-Hochberg correction. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")

Sometimes, data sets store several rows where each of them does not came from the same experiment, that is, each row has nothing to do with the other rows. Conversely, the rows from RNA-seq data sets are dependent to the other rows into the same column (sample), so other function has to be apply here, such as EMObuODlmTest.

A data.frame is returned, which cointains log2FC, FC_LowBound and FC_UpBound

Enrique Perez_Riesgo

Fesc for use its outcomes as argument of this function and test the differential expression for independents rows.

Other One sample functions: Fesc

#this example is the continuation of example from Fesc function
cd <- estimateCommonDisp(y = expressiondata[, 1:6])
for(z in 1:dim(expressiondata)[1]){
testdEMOODci <- dEMOOD1sci(expr = expressiondata[z, 1:6],
Fescd = Fescoefs, condition = c(0, 0, 0, 1, 1, 1),
n = expressiondata[z, 7:12], cd = cd, alpha = 0.05, method.adj = "none")
}