RBM_F: RBM_F: a R function for microarray and RNA-Seq data analysis...
In ur-ctsi/RBM: RBM: a R package for microarray and RNA-Seq data analysis

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

View source: R/RBM_package.R

Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets for designs with more than two groups.

1	RBM_F(aData, vec_trt, aContrast, repetition, alpha)

`aData`	The input data set with rows and columns denoting features and samples, respectively
`vec_trt`	A vector for group notation such as 1s denote treatment group and 0s denote control group
`aContrast`	A vector for contrast. For example: if we want to compare group 1 with group 0, group 2 with group 1, and group 2 with group 0, then the contrast vector will be ("X1-X0", "X2"-"X1", "X2-X0")
`repetition`	The number of resamplings used in the analysis. You could use 1000 or higher number
`alpha`	The signifiance level

Combine resampling with empirical Bayes approach for Microarrays and RNA-Seq data analysis.

RBM_F produces a named list with the following components:

`ordfit_t`	orignal t statistics
`ordfit_pvalue`	original p-values from lmFit and eBayes
`ordfit_beta0`	estimated mean for the control group
`ordfit_beta1`	estimated mean difference between treatment and control group
`permutation_p`	calculated p-values from permutation method based on resampled test statistics
`bootstrap_p`	calculated p-values from bootstrap method based on resampled test statistics

Dongmei Li and Chin-Yuan Liang

Li D, Le Pape MA, Parikh NI, Chen WX, Dye TD (2013) Assessing Differential Expression in Two-Color Microarrays: A Resampling-Based Empirical Bayes Approach. PLoS ONE 8(11): e80099. doi: 10.1371/journal.pone.0080099

The RBM_T function defined in this package. The limma and marray packages.

normdata_F <- matrix(rnorm(200*9, 0, 2), 200, 9)   
mydesign_new <- c(0, 0, 0, 1, 1, 1, 2, 2, 2)
aContrast <- c("X1-X0", "X2-X1", "X2-X0")
normresult_F <- RBM_F(normdata_F, mydesign_new, aContrast, 100, 0.05) 
     
unifdata_F <- matrix(runif(200*18, 0.15, 0.98), 200, 18) 
mydesign2_new <- c(rep(0, 6), rep(1, 6), rep(2, 6))
aContrast <- c("X1-X0", "X2-X1", "X2-X0")
unifresult_F <- RBM_F(unifdata_F, mydesign2_new, aContrast, 100, 0.05)