原文题目：Identifying and removing the cellcycle effect from single-cell RNASequencing data
#####step1
##Normalized Data Matrix
#chooseCRANmirror()
#install.packages('ccRemover')
library(ccRemover)
browseVignettes('ccRemover')
data(t.cell_data)
dim(t.cell_data)
head(t.cell_data[,1:5])
summary(apply(t.cell_data,1, mean))
mean_gene_exp <- rowMeans(t.cell_data)
t_cell_data_cen <- t.cell_data - mean_gene_exp
summary(apply(t_cell_data_cen,1,mean))
#####step2
##The cell-cycle genes
gene_names <- rownames(t_cell_data_cen)
cell_cycle_gene_indices <- gene_indexer(gene_names, species = "mouse",
name_type = "symbols" )
length(cell_cycle_gene_indices)
if_cc <- rep(FALSE,nrow(t_cell_data_cen))
if_cc[cell_cycle_gene_indices] <- TRUE
summary(if_cc)
#####step3
##Putting it Together
dat <- list(x=t_cell_data_cen, if_cc=if_cc)
#####step4
##Applying ccRemover
xhat <- ccRemover(dat, bar=FALSE)
#The final step here is to add the mean values back to the cleaned data matrix:
xhat <- xhat + mean_gene_exp
#####step4+
##Settings
#If you choose to run ccRemover not using the default
#settings the following options are available to you:
xhat_2 <- ccRemover(dat, cutoff = 3, max_it = 4, nboot = 200, ntop = 10, bar=FALSE)
参考：单细胞天地——在单细胞转录组表达矩阵里面去除细胞周期影响
