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SCENIC_Script.R
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library(ggplot2)
library(Seurat)
library(RColorBrewer)
library(ggthemes)
library(parallel)
library(dplyr)
library(tidyverse)
library(reshape2)
library(SCENIC)
library(ggsci)
library(AUCell)
library(pheatmap)
###-------1. basical settings--------------------
org <- "hgnc" # or hgnc, or dmel
dbDir <- "/data4/heshuai/RAW_data/1-SingleCell/3-HCA/3-analysis/8-reanalysis/All_singlet_data/" # RcisTarget databases location
myDatasetTitle <- "AHCA" # choose a name for your analysis
data(defaultDbNames)
# scenicOptions@inputDatasetInfo$cellInfo <- "int/cellInfo.Rds"
dbs <- defaultDbNames[[org]]
scenicOptions <- initializeScenic(org = org, dbDir = dbDir, dbs = dbs, datasetTitle = myDatasetTitle, nCores = 40)
saveRDS(scenicOptions, file = paste0("int/scenicOptions.Rds"))
##----2. input file----------------------------------
## only 250 cells were selected in each cluster
load("subset_cells.RData") ## Seurat object
meta.dat <- [email protected]
tmp.dat <- data.frame()
selected_cells <- c()
for (cluster in unique(meta.dat$seurat_clusters %>% levels)) {
tmp <- meta.dat %>% subset(seurat_clusters %in% cluster)
if(dim(tmp)[1] < 250){
cat("do nothig \n")
selected_cells <- c(selected_cells, row.names(tmp))
tmp.dat <- rbind(tmp.dat, tmp)
} else {
set.seed(1)
tmp <- tmp[sample(1:dim(tmp)[1], replace = FALSE, size = 250), ]
tmp.dat <- rbind(tmp.dat, tmp)
selected_cells <- c(selected_cells, row.names(tmp))
}
}
subset_cells_selected <- subset_cells[, selected_cells]
cellInfo <- data.frame(seuratCluster = Idents(subset_cells_selected))
singleCellMatrix <- subset_cells_selected@assays$RNA@counts %>% as.matrix
#-------3. remove low-frequency genes------###
genesKept <- geneFiltering(singleCellMatrix, scenicOptions = scenicOptions,
minCountsPerGene = 3*.01*ncol(singleCellMatrix),
minSamples = ncol(singleCellMatrix)*.05)
exprMat_filtered <- singleCellMatrix[genesKept, ]
dim(exprMat_filtered)
runCorrelation(exprMat_filtered, scenicOptions)
#exprMat_log <- log2(singleCellMatrix + 1)
#-------4. Run GENIE3-----------#####
exprMat_filtered_log <- log2(exprMat_filtered + 1)
runGenie3(exprMat_filtered_log, scenicOptions)
#############-----------------------------###################
scenicOptions <- readRDS("int/scenicOptions.Rds")
scenicOptions@settings$verbose <- TRUE
scenicOptions@settings$nCores <- 40
scenicOptions@settings$seed <- 123
#--------5. Run SCENIC---------########
runSCENIC_1_coexNetwork2modules(scenicOptions)
runSCENIC_2_createRegulons(scenicOptions) #
runSCENIC_3_scoreCells(scenicOptions, exprMat_filtered_log)
######---------to find the most significantly enriched TF in each cluster--------------######
SUBSET_CELLS <- subset_cells
subset_cells <- subset_cells_selected
regulonAUC <- loadInt(scenicOptions, "aucell_regulonAUC")
regulonAUC <- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)),]
subset_cells@assays$RNA@data <- regulonAUC@assays$data$AUC
genes_modified <- subset_cells %>% row.names %>% gsub(., pattern="_extended*", replacement = "")
row.names(subset_cells@assays$RNA@data) <- genes_modified
# Idents(subset_cells) <- subset_cells$orig.ident
result <- mclapply(levels([email protected]),
FUN = function(x) {FindMarkers(subset_cells, ident.1 = x, ident.2 = NULL, logfc.threshold = 0)},
mc.cores = 36)
RESULT <- result
all_markers <- do.call(rbind, result)
all_markers$gene <- unlist(mapply(rownames, result, SIMPLIFY = F))
all_markers$cluster <- rep(levels([email protected]), times = mapply(dim, result, SIMPLIFY = TRUE)[1,])
subset_cells.markers <- all_markers
row.names(regulonAUC@assays@data$AUC) <- regulonAUC@assays$data$AUC %>% row.names %>% gsub(., pattern = "_extended*", replacement = "")
tissue_specific_gene <- subset_cells.markers %>% TOP_N(10, pct.1 = 0.2, sig.padj = 0.05, fc.threshold = 0.01) %>% `[`("gene") %>% unlist %>% unique()
Cells <- "Epi_HCA"
write.table(subset_cells.markers %>% TOP_N(1000, pct.1 = 0.2, sig.padj = 0.05, fc.threshold = 0.01), paste0(Cells, "_order_of_TFs.txt"), row.names = T, col.names = T, sep = "\t", quote = F)
##---plot with average value---------------####
regulonActivity_byCellType <- sapply(split(rownames(cellInfo), cellInfo$seuratCluster),
function(cells) rowMeans(getAUC(regulonAUC)[, cells]))
# TFs <- grep(row.names(regulonActivity_byCellType), pattern = "extended", value = T, invert = T)
regulonActivity_byCellType_Scaled <- t(scale(t(regulonActivity_byCellType[, ]), center = T, scale=T))
row.names(regulonActivity_byCellType_Scaled) <- regulonActivity_byCellType_Scaled %>% row.names %>% gsub(., pattern="_extended*", replacement = "")
###---preparate the annotation materials--------------
anto <- data.frame(cluster = colnames(regulonActivity_byCellType_Scaled))
row.names(anto) <- colnames(regulonActivity_byCellType_Scaled)
cluster <- c(pal_npg()(10),pal_igv()(9),pal_uchicago("light")(9),pal_futurama()(12), pal_aaas()(10))[-8]
# names(cluster) <- 0:((cellInfo$seuratCluster %>% unique %>% length) - 1)
names(cluster) <- anto$cluster %>% as.character
# cluster <- cluster[1:17]
ancols <- list(cluster = cluster)
# png("Epi_to10_significant_cells.reg.png", height = 15, width = 15, units = "in", res = 400)
pdf("Epi_to10_significant_cells.reg.pdf", height = 15, width = 10)
pheatmap::pheatmap(regulonActivity_byCellType_Scaled[tissue_specific_gene %>% unique, ],
color = colorRampPalette(c("#181495", "blue", "white", "red", "#930107"))(100),
breaks = seq(-3, 3, length.out = 100),
treeheight_row = 10,
treeheight_col = 10,
border_color = "white",
cellwidth = 12,
cellheight = 3,
fontsize_row = 3,
fontsize_col = 6,
cluster_rows = T,
cluster_cols = F,
width = 15,
height = 7.5,
annotation_col = anto,
annotation_colors = ancols,
)
dev.off()
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server release 6.6 (Santiago)
Matrix products: default
BLAS/LAPACK: /data/home/heshuai/Miniconda3/envs/RV_3.6/lib/libopenblasp-r0.3.10.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] pheatmap_1.0.12 AUCell_1.8.0 ggsci_2.9 SCENIC_1.1.3 reshape2_1.4.4 forcats_0.5.0 stringr_1.4.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.1 tibble_3.0.3 tidyverse_1.3.0
[13] dplyr_1.0.1 ggthemes_4.2.0 RColorBrewer_1.1-2 Seurat_3.1.5 ggplot2_3.3.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2 GSEABase_1.48.0 splines_3.6.3 BiocParallel_1.20.1
[9] listenv_0.8.0 GenomeInfoDb_1.22.1 digest_0.6.25 htmltools_0.5.0 fansi_0.4.1 magrittr_1.5 memoise_1.1.0 cluster_2.1.0
[17] ROCR_1.0-11 globals_0.12.5 annotate_1.64.0 modelr_0.1.8 matrixStats_0.56.0 R.utils_2.9.2 colorspace_1.4-1 blob_1.2.1
[25] rvest_0.3.6 rappdirs_0.3.1 ggrepel_0.8.2 haven_2.3.1 crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0 graph_1.64.0
[33] survival_3.2-3 zoo_1.8-8 ape_5.4 glue_1.4.1 gtable_0.3.0 zlibbioc_1.32.0 XVector_0.26.0 leiden_0.3.3
[41] DelayedArray_0.12.3 future.apply_1.6.0 SingleCellExperiment_1.8.0 BiocGenerics_0.32.0 scales_1.1.1 DBI_1.1.0 Rcpp_1.0.5 viridisLite_0.3.0
[49] xtable_1.8-4 reticulate_1.16 bit_4.0.4 rsvd_1.0.3 stats4_3.6.3 tsne_0.1-3 htmlwidgets_1.5.1 httr_1.4.2
[57] ellipsis_0.3.1 ica_1.0-2 pkgconfig_2.0.3 XML_3.98-1.20 R.methodsS3_1.8.0 uwot_0.1.8 dbplyr_1.4.4 tidyselect_1.1.0
[65] rlang_0.4.7 later_1.1.0.1 AnnotationDbi_1.48.0 munsell_0.5.0 cellranger_1.1.0 tools_3.6.3 cli_2.0.2 generics_0.0.2
[73] RSQLite_2.2.0 broom_0.7.0 ggridges_0.5.2 fastmap_1.0.1 bit64_4.0.2 fs_1.4.2 fitdistrplus_1.1-1 RANN_2.6.1
[81] pbapply_1.4-2 future_1.18.0 nlme_3.1-148 mime_0.9 R.oo_1.23.0 xml2_1.3.2 compiler_3.6.3 rstudioapi_0.11
[89] plotly_4.9.2.1 png_0.1-7 reprex_0.3.0 stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18 vctrs_0.3.2 pillar_1.4.6
[97] lifecycle_0.2.0 lmtest_0.9-37 RcppAnnoy_0.0.16 data.table_1.13.0 cowplot_1.0.0 bitops_1.0-6 irlba_2.3.3 httpuv_1.5.4
[105] patchwork_1.0.1 GenomicRanges_1.38.0 R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17 gridExtra_2.3 IRanges_2.20.2 codetools_0.2-16
[113] MASS_7.3-51.6 assertthat_0.2.1 SummarizedExperiment_1.16.1 withr_2.2.0 sctransform_0.2.1 S4Vectors_0.24.4 GenomeInfoDbData_1.2.2 hms_0.5.3
[121] grid_3.6.3 Rtsne_0.15 Biobase_2.46.0 shiny_1.5.0 lubridate_1.7.9