postprob_DE_thr_fun {BUScorrect} | R Documentation |
To control the false discovery rate at the targeted level, call postprob_DE_thr_fun to obtain the threshold for the posterior probability of being differentially expressed.
postprob_DE_thr_fun(BUSfits, fdr_threshold = 0.1)
BUSfits |
The BUSfits object output by the function |
fdr_threshold |
the false discovery rate level we want to control. |
thre0 |
the posterior probability threshold that controls the false discovery rate. |
Xiangyu Luo
Xiangyu Luo, Yingying Wei. Batch Effects Correction with Unknown Subtypes. Journal of the American Statistical Association. Accepted.
rm(list = ls(all = TRUE)) set.seed(123) #a toy example, there are 6 samples and 20 genes in each batch example_Data <- list() #batch 1 example_Data[[1]] <- rbind(matrix(c(1,1,5,5,10,10, 3,3,7,7,12,12), ncol=6, byrow=TRUE), matrix(c(1,2),nrow=18, ncol=6)) #batch 2 batch2_effect <- c(2,2,2,1,1) example_Data[[2]] <- rbind(matrix(c(1,1,5,5,10,10, 3,3,7,7,12,12), ncol=6, byrow=TRUE), matrix(c(1,2),nrow=18, ncol=6)) + batch2_effect #batch 3 batch3_effect <- c(3,2,1,1,2) example_Data[[3]] <- rbind(matrix(c(1,1,5,5,10,10, 3,3,7,7,12,12), ncol=6, byrow=TRUE), matrix(c(1,2),nrow=18, ncol=6)) + batch3_effect set.seed(123) BUSfits <- BUSgibbs(example_Data, n.subtypes = 3, n.iterations = 100, showIteration = FALSE) #select kappa to estimate intrinsic gene indicators thr0 <- postprob_DE_thr_fun(BUSfits, fdr_threshold=0.1) est_L <- estimate_IG_indicators(BUSfits, postprob_DE_threshold=thr0) #obtain the intrinsic gene indicators intrinsic_gene_indices <- IG_index(est_L)