RBM_T {RBM} | R Documentation |
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.
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)