Last updated: 2022-09-02
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Knit directory: GSFA_analysis/
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| Rmd | fadd5df | kevinlkx | 2022-09-02 | added tables of DE guides |
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library(Seurat)
library(data.table)
library(Matrix)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(ComplexHeatmap)
library(kableExtra)
library(WebGestaltR)
library(GSFA)
library(DT)
source("code/plotting_functions.R")Load GSFA result
data_folder <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
fit <- readRDS(paste0(data_folder,
"gsfa_output_detect_01/use_negctrl/All.gibbs_obj_k20.svd_negctrl.seed_14314.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat <- fit$lfsr[, -ncol(fit$lfsr)]
total_effect <- fit$total_effect[, -ncol(fit$total_effect)]
KO_names <- colnames(lfsr_mat)
guides <- KO_names[KO_names!="Nontargeting"]Load the mapping from gene name to ENSEMBL ID for all 6k genes used in GSFA
feature.names <- data.frame(fread(paste0(data_folder, "GSE142078_raw/GSM4219576_Run2_genes.tsv.gz"),
header = FALSE), stringsAsFactors = FALSE)
genes_df <- feature.names[match(rownames(lfsr_mat), feature.names$V1), ]
names(genes_df) <- c("ID", "Name")Check the gene loadings to see if the targeted genes in any of the factors P(F) > 0.95?
F_pm <- gibbs_PM$F_pm
guide_genes_df <- feature.names[match(guides, feature.names$V2), ]
names(guide_genes_df) <- c("ID", "Name")
F_pm_guides <- F_pm[match(guide_genes_df$ID, rownames(F_pm)), ]
rownames(F_pm_guides) <- guides
F_pm_guides_highpip <- ifelse(F_pm_guides > 0.95, 1, 0)
F_pm_guides_highpip[is.na(F_pm_guides_highpip)] <- 0
gsfa_num_highpip_factors <- rowSums(F_pm_guides_highpip)GSFA result
effect_mat <- total_effect
rownames(effect_mat) <- rownames(lfsr_mat)
colnames(effect_mat) <- colnames(lfsr_mat)
gsfa_deg_guides.df <- data.frame()
for(guide in guides){
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(lfsr_mat)){
gsfa_deg_guides.df <- rbind(gsfa_deg_guides.df,
data.frame(guide = guide, lfsr = lfsr_mat[guide_geneID, guide], effect = effect_mat[guide_geneID, guide]))
}else{
gsfa_deg_guides.df <- rbind(gsfa_deg_guides.df,
data.frame(guide = guide, lfsr = NA, effect = NA))
}
}
gsfa_deg_guides.df$sig_DE <- ifelse(gsfa_deg_guides.df$lfsr < 0.05, "DE", "")
gsfa_de_guides <- gsfa_deg_guides.df$guide[which(gsfa_deg_guides.df$lfsr < 0.05)]MAST result
mast_deg_guides.df <- data.frame()
for (guide in guides){
fname <- paste0(data_folder, "processed_data/MAST/dev_top6k_negctrl/gRNA_",
guide, ".dev_res_top6k.vs_negctrl.rds")
mast_df <- readRDS(fname)
mast_df$geneID <- rownames(mast_df)
mast_df <- mast_df %>% dplyr::rename(FDR = fdr, PValue = pval)
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(mast_df)){
mast_deg_guides.df <- rbind(mast_deg_guides.df,
data.frame(guide = guide, fdr = mast_df[guide_geneID, "FDR"], logFC = mast_df[guide_geneID, "logFC"]))
}else{
mast_deg_guides.df <- rbind(mast_deg_guides.df,
data.frame(guide = guide, fdr = NA, logFC = NA))
}
}
mast_deg_guides.df$sig_DE <- ifelse(mast_deg_guides.df$fdr < 0.05, "DE", "")
mast_de_guides <- mast_deg_guides.df$guide[which(mast_deg_guides.df$fdr < 0.05)]DESeq2
deseq_deg_guides.df <- data.frame()
for (guide in guides){
fname <- paste0(data_folder, "processed_data/DESeq2/dev_top6k_negctrl/gRNA_",
guide, ".dev_res_top6k.vs_negctrl.rds")
res <- readRDS(fname)
res <- as.data.frame(res@listData, row.names = res@rownames)
res$geneID <- rownames(res)
deseq_df <- res %>% dplyr::rename(FDR = padj, PValue = pvalue)
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(deseq_df)){
deseq_deg_guides.df <- rbind(deseq_deg_guides.df,
data.frame(guide = guide,
fdr = deseq_df[guide_geneID, "FDR"], logFC = deseq_df[guide_geneID, "log2FoldChange"]))
}else{
deseq_deg_guides.df <- rbind(deseq_deg_guides.df,
data.frame(guide = guide, fdr = NA, logFC = NA))
}
}
deseq_deg_guides.df$sig_DE <- ifelse(deseq_deg_guides.df$fdr < 0.05, "DE", "")
deseq_de_guides <- deseq_deg_guides.df$guide[which(deseq_deg_guides.df$fdr < 0.05)]scMAGeCK
scmageck_res <- readRDS(paste0(data_folder, "scmageck/scmageck_lr.LUHMES.dev_res_top_6k.rds"))
colnames(scmageck_res$fdr)[colnames(scmageck_res$fdr) == "NegCtrl"] <- "Nontargeting"
colnames(scmageck_res$score)[colnames(scmageck_res$score) == "NegCtrl"] <- "Nontargeting"
scmageck_deg_guides.df <- data.frame()
for (guide in guides){
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(scmageck_res$fdr)){
scmageck_deg_guides.df <- rbind(scmageck_deg_guides.df,
data.frame(guide = guide, fdr = scmageck_res$fdr[guide_geneID, guide], score = scmageck_res$score[guide_geneID, guide]))
}else{
scmageck_deg_guides.df <- rbind(scmageck_deg_guides.df,
data.frame(guide = guide, fdr = NA, score = NA))
}
}
scmageck_deg_guides.df$sig_DE <- ifelse(scmageck_deg_guides.df$fdr < 0.05, "DE", "")
scmageck_de_guides <- scmageck_deg_guides.df$guide[which(scmageck_deg_guides.df$fdr < 0.05)]SCEPTRE
sceptre_res <- readRDS("/project2/xinhe/kevinluo/GSFA/sceptre_analysis/LUHMES_cropseq_data/sceptre_output/sceptre.result.rds")
sceptre_count_df <- data.frame(matrix(nrow = length(guides), ncol = 2))
colnames(sceptre_count_df) <- c("target", "num_DEG")
sceptre_deg_guides.df <- data.frame()
for (guide in guides){
curr_sceptre_res <- sceptre_res %>% filter(gRNA_id == guide)
curr_sceptre_res$fdr <- p.adjust(curr_sceptre_res$p_value, method = "fdr")
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% curr_sceptre_res$gene_id){
sceptre_deg_guides.df <- rbind(sceptre_deg_guides.df,
data.frame(guide = guide,
fdr = curr_sceptre_res$fdr[curr_sceptre_res$gene_id == guide_geneID],
z_value = curr_sceptre_res$z_value[curr_sceptre_res$gene_id == guide_geneID]))
}else{
sceptre_deg_guides.df <- rbind(sceptre_deg_guides.df,
data.frame(guide = guide, fdr = NA, z_value = NA))
}
}
sceptre_deg_guides.df$sig_DE <- ifelse(sceptre_deg_guides.df$fdr < 0.05, "DE", "")
sceptre_de_guides <- sceptre_deg_guides.df$guide[which(sceptre_deg_guides.df$fdr < 0.05)]Compare across methods
dge_guides_comparison_df <- data.frame(Perturbation = guides,
num_GSFA_factors = gsfa_num_highpip_factors,
GSFA = gsfa_deg_guides.df$sig_DE,
scMAGeCK = scmageck_deg_guides.df$sig_DE,
DESeq2 = deseq_deg_guides.df$sig_DE,
MAST = mast_deg_guides.df$sig_DE,
SCEPTRE = sceptre_deg_guides.df$sig_DE)
DT::datatable(dge_guides_comparison_df,
rownames = FALSE,
options = list(pageLength = length(guides),
columnDefs = list(list(className = 'dt-center', targets = 0:4))))
LUHMES_de_guides <- data.frame(method = c("GSFA", "scMAGeCK", "DESeq2", "MAST", "SCEPTRE"),
DEGs = c(paste(gsfa_de_guides, collapse = ","),
paste(scmageck_de_guides, collapse = ","),
paste(deseq_de_guides, collapse = ","),
paste(mast_de_guides, collapse = ","),
paste(sceptre_de_guides, collapse = ",")))Load GSFA result
data_folder <- "/project2/xinhe/yifan/Factor_analysis/Stimulated_T_Cells/"
fit <- readRDS(paste0(data_folder,
"gsfa_output_detect_01/all_uncorrected_by_group.use_negctrl/All.gibbs_obj_k20.svd_negctrl.restart.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat1 <- fit$lfsr1[, -ncol(fit$lfsr1)]
lfsr_mat0 <- fit$lfsr0[, -ncol(fit$lfsr0)]
total_effect1 <- fit$total_effect1[, -ncol(fit$total_effect1)]
total_effect0 <- fit$total_effect0[, -ncol(fit$total_effect0)]
KO_names <- colnames(lfsr_mat1)
guides <- KO_names[KO_names!="NonTarget"]Load the mapping from gene name to ENSEMBL ID for all 6k genes used in GSFA.
feature.names <- data.frame(fread(paste0(data_folder, "GSE119450_RAW/D1N/genes.tsv"),
header = FALSE), stringsAsFactors = FALSE)
genes_df <- feature.names[match(rownames(lfsr_mat1), feature.names$V1), ]
names(genes_df) <- c("ID", "Name")Check the gene loadings to see if the targeted genes in any of the factors P(F) > 0.95?
F_pm <- gibbs_PM$F_pm
guide_genes_df <- feature.names[match(guides, feature.names$V2), ]
names(guide_genes_df) <- c("ID", "Name")
F_pm_guides <- F_pm[match(guide_genes_df$ID, rownames(F_pm)), ]
rownames(F_pm_guides) <- guides
F_pm_guides_highpip <- ifelse(F_pm_guides > 0.95, 1, 0)
F_pm_guides_highpip[is.na(F_pm_guides_highpip)] <- 0
gsfa_num_highpip_factors <- rowSums(F_pm_guides_highpip)GSFA result
lfsr_mat <- lfsr_mat1
effect_mat <- total_effect1
rownames(effect_mat) <- rownames(lfsr_mat)
colnames(effect_mat) <- colnames(lfsr_mat)
gsfa_deg_guides.df <- data.frame()
for(guide in guides){
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(lfsr_mat)){
gsfa_deg_guides.df <- rbind(gsfa_deg_guides.df,
data.frame(guide = guide, lfsr = lfsr_mat[guide_geneID, guide], effect = effect_mat[guide_geneID, guide]))
}else{
gsfa_deg_guides.df <- rbind(gsfa_deg_guides.df,
data.frame(guide = guide, lfsr = NA, effect = NA))
}
}
gsfa_deg_guides.df$sig_DE <- ifelse(gsfa_deg_guides.df$lfsr < 0.05, "DE", "")
gsfa_de_guides <- gsfa_deg_guides.df$guide[which(gsfa_deg_guides.df$lfsr < 0.05)]MAST
mast_deg_guides.df <- data.frame()
for (guide in guides){
fname <- paste0(data_folder, "processed_data/MAST/all_by_stim_negctrl/gRNA_",
guide, ".dev_res_top6k.vs_negctrl.rds")
mast_df <- readRDS(fname)
mast_df$geneID <- rownames(mast_df)
mast_df <- mast_df %>% dplyr::rename(FDR = fdr, PValue = pval)
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(mast_df)){
mast_deg_guides.df <- rbind(mast_deg_guides.df,
data.frame(guide = guide, fdr = mast_df[guide_geneID, "FDR"], logFC = mast_df[guide_geneID, "logFC"]))
}else{
mast_deg_guides.df <- rbind(mast_deg_guides.df,
data.frame(guide = guide, fdr = NA, logFC = NA))
}
}
mast_deg_guides.df$sig_DE <- ifelse(mast_deg_guides.df$fdr < 0.05, "DE", "")
mast_de_guides <- mast_deg_guides.df$guide[which(mast_deg_guides.df$fdr < 0.05)]DESeq2
deseq_deg_guides.df <- data.frame()
for (guide in guides){
fname <- paste0(data_folder, "processed_data/DESeq2/all_by_stim_negctrl/gRNA_",
guide, ".dev_res_top6k.vs_negctrl.rds")
res <- readRDS(fname)
res <- as.data.frame(res@listData, row.names = res@rownames)
res$geneID <- rownames(res)
deseq_df <- res %>% dplyr::rename(FDR = padj, PValue = pvalue)
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(deseq_df)){
deseq_deg_guides.df <- rbind(deseq_deg_guides.df,
data.frame(guide = guide, fdr = deseq_df[guide_geneID, "FDR"], logFC = deseq_df[guide_geneID, "log2FoldChange"]))
}else{
deseq_deg_guides.df <- rbind(deseq_deg_guides.df,
data.frame(guide = guide, fdr = NA, logFC = NA))
}
}
deseq_deg_guides.df$sig_DE <- ifelse(deseq_deg_guides.df$fdr < 0.05, "DE", "")
deseq_de_guides <- deseq_deg_guides.df$guide[which(deseq_deg_guides.df$fdr < 0.05)]scMAGeCK
scmageck_res <- readRDS(paste0(data_folder, "scmageck/scmageck_lr.TCells_stim.dev_res_top_6k.rds"))
colnames(scmageck_res$fdr)[colnames(scmageck_res$fdr) == "NegCtrl"] <- "NonTarget"
colnames(scmageck_res$score)[colnames(scmageck_res$score) == "NegCtrl"] <- "NonTarget"
scmageck_deg_guides.df <- data.frame()
for (guide in guides){
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% rownames(scmageck_res$fdr)){
scmageck_deg_guides.df <- rbind(scmageck_deg_guides.df,
data.frame(guide = guide, fdr = scmageck_res$fdr[guide_geneID, guide], score = scmageck_res$score[guide_geneID, guide]))
}else{
scmageck_deg_guides.df <- rbind(scmageck_deg_guides.df,
data.frame(guide = guide, fdr = NA, score = NA))
}
}
scmageck_deg_guides.df$sig_DE <- ifelse(scmageck_deg_guides.df$fdr < 0.05, "DE", "")
scmageck_de_guides <- scmageck_deg_guides.df$guide[which(scmageck_deg_guides.df$fdr < 0.05)]SCEPTRE
sceptre_res <- readRDS("/project2/xinhe/kevinluo/GSFA/sceptre_analysis/TCells_cropseq_data/simulated_data/sceptre_output/sceptre.result.rds")
sceptre_count_df <- data.frame(matrix(nrow = length(guides), ncol = 2))
colnames(sceptre_count_df) <- c("target", "num_DEG")
sceptre_deg_guides.df <- data.frame()
for (guide in guides){
curr_sceptre_res <- sceptre_res %>% filter(gRNA_id == guide)
curr_sceptre_res$fdr <- p.adjust(curr_sceptre_res$p_value, method = "fdr")
guide_geneID <- feature.names[which(feature.names$V2 == guide),]$V1
if(guide_geneID %in% curr_sceptre_res$gene_id){
sceptre_deg_guides.df <- rbind(sceptre_deg_guides.df,
data.frame(guide = guide, fdr = curr_sceptre_res$fdr[curr_sceptre_res$gene_id == guide_geneID],
z_value = curr_sceptre_res$z_value[curr_sceptre_res$gene_id == guide_geneID]))
}else{
sceptre_deg_guides.df <- rbind(sceptre_deg_guides.df,
data.frame(guide = guide, fdr = NA, z_value = NA))
}
}
sceptre_deg_guides.df$sig_DE <- ifelse(sceptre_deg_guides.df$fdr < 0.05, "DE", "")
sceptre_de_guides <- sceptre_deg_guides.df$guide[which(sceptre_deg_guides.df$fdr < 0.05)]Compare across methods
dge_guides_comparison_df <- data.frame(Perturbation = guides,
num_GSFA_factors = gsfa_num_highpip_factors,
GSFA = gsfa_deg_guides.df$sig_DE,
scMAGeCK = scmageck_deg_guides.df$sig_DE,
DESeq2 = deseq_deg_guides.df$sig_DE,
MAST = mast_deg_guides.df$sig_DE,
SCEPTRE = sceptre_deg_guides.df$sig_DE)
DT::datatable(dge_guides_comparison_df,
rownames = FALSE,
options = list(pageLength = length(guides),
columnDefs = list(list(className = 'dt-center', targets = 0:4))))
Tcells_de_guides <- data.frame(method = c("GSFA", "scMAGeCK", "DESeq2", "MAST", "SCEPTRE"),
DEGs = c(paste(gsfa_de_guides, collapse = ","),
paste(scmageck_de_guides, collapse = ","),
paste(deseq_de_guides, collapse = ","),
paste(mast_de_guides, collapse = ","),
paste(sceptre_de_guides, collapse = ",")))de_guides_table <- data.frame(Method = LUHMES_de_guides$method,
LUHMES_DEGs = LUHMES_de_guides$DEGs,
Tcells_DEGs = Tcells_de_guides$DEGs)
DT::datatable(de_guides_table,
rownames = FALSE,
options = list(pageLength = nrow(LUHMES_de_guides),
columnDefs = list(list(className = 'dt-center', targets = 0:4))))
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] lattice_0.20-45 DT_0.21 GSFA_0.2.8
[4] WebGestaltR_0.4.4 kableExtra_1.3.4 ComplexHeatmap_2.6.2
[7] gridExtra_2.3 forcats_0.5.1 stringr_1.4.0
[10] dplyr_1.0.8 purrr_0.3.4 readr_2.1.2
[13] tidyr_1.2.0 tibble_3.1.6 ggplot2_3.3.5
[16] tidyverse_1.3.1 Matrix_1.4-1 data.table_1.14.2
[19] SeuratObject_4.0.4 Seurat_4.1.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.25 R.utils_2.12.0
[4] tidyselect_1.1.2 htmlwidgets_1.5.4 Rtsne_0.15
[7] munsell_0.5.0 codetools_0.2-18 ica_1.0-2
[10] future_1.24.0 miniUI_0.1.1.1 withr_2.5.0
[13] spatstat.random_2.1-0 colorspace_2.0-3 knitr_1.38
[16] rstudioapi_0.13 stats4_4.0.4 ROCR_1.0-11
[19] tensor_1.5 listenv_0.8.0 git2r_0.30.1
[22] polyclip_1.10-0 rprojroot_2.0.2 parallelly_1.32.1
[25] vctrs_0.4.1 generics_0.1.3 xfun_0.30
[28] R6_2.5.1 doParallel_1.0.17 clue_0.3-60
[31] spatstat.utils_2.3-0 assertthat_0.2.1 promises_1.2.0.1
[34] scales_1.2.0 gtable_0.3.0 Cairo_1.6-0
[37] globals_0.16.0 processx_3.5.3 goftest_1.2-3
[40] rlang_1.0.4 systemfonts_1.0.4 GlobalOptions_0.1.2
[43] splines_4.0.4 lazyeval_0.2.2 spatstat.geom_2.3-2
[46] broom_0.8.0 yaml_2.3.5 reshape2_1.4.4
[49] abind_1.4-5 modelr_0.1.8 crosstalk_1.2.0
[52] backports_1.4.1 httpuv_1.6.5 tools_4.0.4
[55] ellipsis_0.3.2 spatstat.core_2.4-0 jquerylib_0.1.4
[58] RColorBrewer_1.1-3 BiocGenerics_0.36.1 ggridges_0.5.3
[61] Rcpp_1.0.9 plyr_1.8.6 ps_1.7.1
[64] rpart_4.1-15 deldir_1.0-6 pbapply_1.5-0
[67] GetoptLong_1.0.5 cowplot_1.1.1 S4Vectors_0.28.1
[70] zoo_1.8-9 haven_2.5.0 ggrepel_0.9.1
[73] cluster_2.1.3 fs_1.5.2 apcluster_1.4.10
[76] magrittr_2.0.3 scattermore_0.7 circlize_0.4.15
[79] lmtest_0.9-40 reprex_2.0.1 RANN_2.6.1
[82] whisker_0.4 fitdistrplus_1.1-8 matrixStats_0.62.0
[85] hms_1.1.1 patchwork_1.1.1 mime_0.12
[88] evaluate_0.16 xtable_1.8-4 readxl_1.4.0
[91] IRanges_2.24.1 shape_1.4.6 compiler_4.0.4
[94] KernSmooth_2.23-20 crayon_1.5.1 R.oo_1.24.0
[97] htmltools_0.5.3 mgcv_1.8-39 later_1.3.0
[100] tzdb_0.3.0 lubridate_1.8.0 DBI_1.1.3
[103] dbplyr_2.1.1 MASS_7.3-58.1 cli_3.3.0
[106] R.methodsS3_1.8.1 parallel_4.0.4 igraph_1.3.4
[109] pkgconfig_2.0.3 getPass_0.2-2 plotly_4.10.0
[112] spatstat.sparse_2.1-0 xml2_1.3.3 foreach_1.5.2
[115] svglite_2.0.0 bslib_0.3.1 rngtools_1.5.2
[118] webshot_0.5.2 rvest_1.0.2 doRNG_1.8.2
[121] callr_3.7.0 digest_0.6.29 sctransform_0.3.3
[124] RcppAnnoy_0.0.19 spatstat.data_2.1-2 rmarkdown_2.13
[127] cellranger_1.1.0 leiden_0.3.9 uwot_0.1.11
[130] shiny_1.7.1 rjson_0.2.21 lifecycle_1.0.1
[133] nlme_3.1-159 jsonlite_1.8.0 viridisLite_0.4.0
[136] fansi_1.0.3 pillar_1.8.0 fastmap_1.1.0
[139] httr_1.4.2 survival_3.3-1 glue_1.6.2
[142] png_0.1-7 iterators_1.0.14 stringi_1.7.6
[145] sass_0.4.1 irlba_2.3.5 future.apply_1.8.1