RBM_T: RBM_T: a R function for microarray and RNA-Seq data analysis...
In RBM: RBM: a R package for microarray and RNA-Seq data analysis

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

Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression or Identify differntially methylated loci in Two-Color Microarrays and RNA-Seq data sets.

1	RBM_T(aData, vec_trt, 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
`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_T 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_F function defined in this package. The limma and marray packages.

normal_data <- matrix(rnorm(200*6), 200, 6)
mydesign <- c(0,0,0,1,1,1)
norm_result <- RBM_T(normal_data,mydesign,50,0.05)
    
unif_data <- matrix(runif(200*7, 0.10, 0.95), 200, 7)
mydesign2 <- c(0,0,0, 1,1,1,1)
unif_result <- RBM_T(unif_data,mydesign2,100,0.05)