Last updated: 2018-11-29
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Modified: analysis/crop_workflow_Alan.Rmd
Modified: analysis/crop_workflow_Siwei.Rmd
# 01-Nov-2018
# obtain the gene expression profile of all gRNAs individually
library(edgeR)
library(Rfast)
library(data.table)
library(cellrangerRkit)
#################
# exp_matrix <- exp_matrix_backup
##############
gbm <- load_cellranger_matrix("../NSC_merged_05_07_08_new/") # load the GBM with NEW data
exp_matrix <- gbm[, colSums(exprs(gbm)) > 11000] # get cells above background value
use_genes <-get_nonzero_genes(exp_matrix)
exp_matrix <- exp_matrix[use_genes, ]
exp_matrix <- normalize_barcode_sums_to_median(exp_matrix)
# Select cells that have at least 1 reads in gRNA count
exp_matrix <- exp_matrix[, colMaxs(as.matrix(exprs(exp_matrix[
(length(exp_matrix@featureData@data$id)-75): # should be 75 or 76?
(length(exp_matrix@featureData@data$id)), ])), value = T) > 0] #more than 1 count
# make cell count for cells contain unique RNA
exp_matrix <- exp_matrix[, # Select cells that have one gRNA expression more than 3x of all others
(colMaxs(as.matrix(exprs(exp_matrix[
(length(exp_matrix@featureData@data$id)-75):
(length(exp_matrix@featureData@data$id)), ])), value = T)) >
(colSums(exprs(exp_matrix[(length(exp_matrix@featureData@data$id)-75):
(length(exp_matrix@featureData@data$id)), ]))*3/4)]
# exp_GeneBCMatrix <- exp_matrix # output from the last step
#############
# find the gRNA for each cell by taking the rowMax index of gRNA columns(29847:29922)
# will return a data.frame with entries followed by .xx since colnames should be unique
exp_matrix <- as.matrix(exprs(exp_matrix))
exp_matrix_backup <- exp_matrix # make a backup in case of need
#############
exp_matrix <- exp_matrix_backup
############
# total UMI count: 77,997,203
#scale exp_matrix to 1,000,000 counts -- NO, will significantly decrease the statistic power
# exp_matrix <- exp_matrix*(1000000/77997203)
### Make the gRNA list
# gRNA_list <- as.data.frame(rownames(exp_matrix
# [(nrow(exp_matrix)-76 +
# colMaxs(
# as.matrix(
# exp_matrix[
# ((nrow(exp_matrix)-75):(nrow(exp_matrix)))
# ,]
# ), value = F)
# ),
# ]))
gRNA_list <- as.data.frame(rownames(exp_matrix
[(nrow(exp_matrix)-76 +
colMaxs(
exp_matrix[
((nrow(exp_matrix)-75):(nrow(exp_matrix)))
,]
, value = F)
),
]))
colnames(gRNA_list) <- "gRNA"
gRNA_ASoC_list <- gRNA_list[!duplicated(gRNA_list$gRNA), ]
gRNA_ASoC_list <- gRNA_ASoC_list[order(gRNA_ASoC_list)]
# exp_matrix$cell_type <- as.factor(gRNA_dist$gRNA)
gRNA_ASoC_list <- gRNA_ASoC_list[-c(31,32,33,37,38,39,40,41,51,52,59,60,61,65,66,67)]
#############
# exp_matrix now: rownames=ENSG, colnames=barcode
# exp_matrix <- as.matrix(t(exp_matrix))
# not using
cell_type_index <- as.vector(gRNA_list$gRNA) # use this variable as index, length = 2522
############ Main Program ######
gRNA_ASoC_list_backup <- gRNA_ASoC_list # set a backup, will use the full list after debugging
# gRNA_ASoC_list <- gRNA_ASoC_list[1:2] # list for debugging, 2 elements only
# gRNA_ASoC_list <- gRNA_ASoC_list_backup
for(i in 1:length(gRNA_ASoC_list)) {
print(gRNA_ASoC_list[i])
### make correspondence of Gene_Symbol and Gene_id
# write_out_with_gene_name <- as.data.frame(get_CRISPRi_result(gRNA_ASoC_list[i],
# TPM_filter = F, TPM_threshold = 0.01))
write_out_with_gene_name <- as.data.frame(get_CRISPRi_result(gRNA_ASoC_list[i],
TPM_filter = F, TPM_threshold = 0.01)) # no filter set
write_out_with_gene_name$Geneid <- rownames(write_out_with_gene_name)
write_out_with_gene_name_output <- merge(write_out_with_gene_name, ENSG_coord_gene_gencodev28, by = "Geneid")
write_out_with_gene_name_output <- write_out_with_gene_name_output[order(write_out_with_gene_name_output$PValue), ]
write.table(write_out_with_gene_name_output, append = F,
row.names = F, col.names = T, sep = "\t", quote = F,
file = paste("output/", gRNA_ASoC_list[i], "_gRNA.txt", collapse = "", sep = ""))
}
############
############## custom functions##############
get_CRISPRi_result <- function(gRNA_name, TPM_filter = FALSE, TPM_threshold = 0.01) {
# TPM filter takes only genes with an estimated TPM above 1
# in more than 25% of the considered cells
# TPM_filter <- TRUE # temporary
# TPM_threshold <- 0.01 # temporaray
# if(TPM_filter) {
# exp_matrix <- exp_matrix[
# rowSums(exp_matrix > TPM_threshold)
# > trunc(ncol(exp_matrix/4)), ]
# }
# make two matrix using grepl, separate the target gRNA and control gRNA (EGFP/neg)
control_matrix <- exp_matrix[ , grepl("EGFP", cell_type_index) |
grepl("neg", cell_type_index)]
# gRNA_name <- "VPS45_2_gene" # temporary
gRNA_matrix <- exp_matrix[ , grepl(gRNA_name, cell_type_index)]
# prepare the input matrix for edgeR
## merge the two matrices as one, ensure the gRNA is the first
matrix_combined <- as.matrix(cbind(control_matrix, gRNA_matrix))
# matrix_combined <- matrix_combined[order(cell_type_index), ]
## Trim the tailing gRNA artificial genes, total 75
# matrix_combined_transposed <- t(matrix_combined[, -((ncol(matrix_combined)-75):
# ncol(matrix_combined))])
matrix_combined_transposed <- matrix_combined[1:(nrow(matrix_combined)-76), ]
### Assign rownames as ENSG gene identifiers
rownames(matrix_combined_transposed) <- gsub("\\..*", "", rownames(matrix_combined_transposed))
# rownames(matrix_combined_transposed) <- gsub("\\..*", "",
# colnames(matrix_combined[ , 1:(ncol(matrix_combined)-76)]))
# colnames(matrix_combined_transposed) <- rownames(matrix_combined)
# print(scale(colMeans(matrix_combined_transposed > 0)))
# Run edgeR use edgeRQLFDetRate, nrow(control_matrix) should be 139
group <- factor(c(rep("ctrl", len = ncol(control_matrix)),
rep("gRNA", len = ncol(gRNA_matrix))))
# make DGEList()
main_DGE <- DGEList(counts = matrix_combined_transposed, group = group, remove.zeros = T)
# Use edgeRQLFDetRate flow from now on
main_DGE <- calcNormFactors(main_DGE)
cdr <- scale(colMeans(matrix_combined_transposed > 0)) # DetRate is applied here
design <- model.matrix(~ cdr + group) # cdr (~ cdr + group)
main_DGE <- estimateDisp(main_DGE, design = design)
fit <- glmQLFit(main_DGE, design = design) # fit
qlf <- glmQLFTest(fit) # QLF vs LRT
# tt <- topTags(qlf, n = 100)
exp_table <- qlf$table
exp_table$FDR <- p.adjust(exp_table$PValue, "fdr")
# write.table(group, append = F,
# row.names = F, col.names = T, sep = "\t", quote = F,
# file = paste("output/", gRNA_ASoC_list[i], "_group.txt", collapse = ""))
return(exp_table)
}sessionInfo()R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin11.4.2 (64-bit)
Running under: OS X El Capitan 10.11.6
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.1.1 Rcpp_0.12.17 digest_0.6.12
[4] rprojroot_1.2 R.methodsS3_1.7.1 backports_1.0.5
[7] git2r_0.18.0 magrittr_1.5 evaluate_0.10
[10] stringi_1.1.5 whisker_0.3-2 R.oo_1.22.0
[13] R.utils_2.7.0 rmarkdown_1.10 tools_3.3.2
[16] stringr_1.2.0 yaml_2.1.16 htmltools_0.3.6
[19] knitr_1.20 This reproducible R Markdown analysis was created with workflowr 1.1.1