RBM (version 1.4.0)

RBM_T: RBM_T: a R function for microarray and RNA-Seq data analysis for two-group comparisons

Description

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.

Usage

RBM_T(aData, vec_trt, repetition, alpha)

Arguments

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

Value

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

Details

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

References

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

See Also

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

Examples

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)