- LUHMES data
- T cells data
- Session Information
Check the expression levels for DEGs found by GSFA
Kaixuan Luo
Last updated: 2022-08-31
Checks: 7 0
Knit directory: GSFA_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b709053 | kevinlkx | 2022-08-31 | checked the distribution of DEGs by expression bins |
Load necessary packages and data
library(data.table)
library(Matrix)
library(tidyverse)
library(ggplot2)
theme_set(theme_bw() + theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
panel.grid.minor = element_blank())
)
library(gridExtra)
library(ComplexHeatmap)
library(kableExtra)
library(WebGestaltR)
source("/project2/xinhe/yifan/Factor_analysis/analysis_website_for_Kevin/scripts/plotting_functions.R")Set directories
res_dir <- "/project2/xinhe/kevinluo/GSFA/DEGs_by_expression_level"
dir.create(res_dir, recursive = TRUE, showWarnings = FALSE)LUHMES data
Load processed gene expression matrix
data_folder <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
scaled.gene_exp <- readRDS(paste0(data_folder,"processed_data/deviance_residual.merged_top_6k.corrected_4.scaled.rds"))Load GSFA result
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$total_effect)]
KO_names <- colnames(lfsr_mat)
guides <- KO_names[KO_names!="Nontargeting"]if(!all.equal(rownames(lfsr_mat), rownames(scaled.gene_exp))){stop("Gene names not match!")}
mean_gene_exp <- rowMeans(scaled.gene_exp)
exp_breaks <- quantile(mean_gene_exp, probs = seq(0,1,0.2))
gene_exp_bins <- cut(mean_gene_exp, breaks = exp_breaks, labels = 1:5)
gene_exp_bins[is.na(gene_exp_bins)] <- 1
gene_exp_bins.df <- data.frame(geneID = rownames(scaled.gene_exp), mean_exp = mean_gene_exp, exp_bin = gene_exp_bins)
table(gene_exp_bins.df$exp_bin)1 2 3 4 5 1200 1200 1200 1200 1200
lfsr_signif_num_bins <- data.frame()
for(l in 1:5){
curr_genes <- gene_exp_bins.df[gene_exp_bins.df$exp_bin == l, ]$geneID
cat(length(curr_genes), "genes in bin", l, "\n")
curr_lfsr_mat <- lfsr_mat[curr_genes, ]
curr_lfsr_signif_num <- colSums(curr_lfsr_mat < 0.05)
lfsr_signif_num_bins <- rbind(lfsr_signif_num_bins,
c(bin = l, curr_lfsr_signif_num))
}1200 genes in bin 1 1200 genes in bin 2 1200 genes in bin 3 1200 genes in bin 4 1200 genes in bin 5
colnames(lfsr_signif_num_bins) <- c("bin", colnames(lfsr_mat))
lfsr_signif_num_bins.df <- tidyr::gather(lfsr_signif_num_bins[,c("bin", guides)], guide, num_genes, guides, factor_key=TRUE)
lfsr_signif_num_bins.df$bin <- factor(lfsr_signif_num_bins.df$bin, levels = 5:1,
labels = c("highest", "mid-high", "mid", "low-mid", "lowest"))
# pdf(file.path(res_dir, "LUHMES_stimulated_lfsr_signif_num_by_exp_bins.pdf"), width = 14, height = 3)
ggplot(lfsr_signif_num_bins.df, aes(x=guide, y=num_genes, fill=bin)) +
geom_bar(position="stack", stat="identity") +
scale_fill_brewer(palette = "Greens") +
guides(fill=guide_legend(title="Gene expression level")) +
labs(x = "Target genes",
y = "Number of DEGs",
title = "Number of DEGs detected by gene expression level") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
legend.position = "right",
legend.text = element_text(size = 13))
# dev.off()T cells data
Load processed gene expression matrix
data_folder <- "/project2/xinhe/yifan/Factor_analysis/Stimulated_T_Cells/"
scaled.gene_exp <- readRDS(paste0(data_folder,"processed_data/deviance_residual.all_T_cells_merged_top_6k.batch_uncorrected.rds"))Load GSFA result
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$total_effect0)]
KO_names <- colnames(lfsr_mat1)
guides <- KO_names[KO_names!="NonTarget"]lfsr_mat <- lfsr_mat1
if(!all.equal(rownames(lfsr_mat), rownames(scaled.gene_exp))){stop("Gene names not match!")}
mean_gene_exp <- rowMeans(scaled.gene_exp)
exp_breaks <- quantile(mean_gene_exp, probs = seq(0,1,0.2))
gene_exp_bins <- cut(mean_gene_exp, breaks = exp_breaks, labels = 1:5)
gene_exp_bins[is.na(gene_exp_bins)] <- 1
gene_exp_bins.df <- data.frame(geneID = rownames(scaled.gene_exp), mean_exp = mean_gene_exp, exp_bin = gene_exp_bins)
table(gene_exp_bins.df$exp_bin)1 2 3 4 5 1200 1200 1200 1200 1200
lfsr_signif_num_bins <- data.frame()
for(l in 1:5){
curr_genes <- gene_exp_bins.df[gene_exp_bins.df$exp_bin == l, ]$geneID
cat(length(curr_genes), "genes in bin", l, "\n")
curr_lfsr_mat <- lfsr_mat[curr_genes, ]
curr_lfsr_signif_num <- colSums(curr_lfsr_mat < 0.05)
lfsr_signif_num_bins <- rbind(lfsr_signif_num_bins,
c(bin = l, curr_lfsr_signif_num))
}1200 genes in bin 1 1200 genes in bin 2 1200 genes in bin 3 1200 genes in bin 4 1200 genes in bin 5
colnames(lfsr_signif_num_bins) <- c("bin", colnames(lfsr_mat))
lfsr_signif_num_bins.df <- tidyr::gather(lfsr_signif_num_bins[,c("bin", guides)], guide, num_genes, guides, factor_key=TRUE)
lfsr_signif_num_bins.df$bin <- factor(lfsr_signif_num_bins.df$bin, levels = 5:1,
labels = c("highest", "mid-high", "mid", "low-mid", "lowest"))
# pdf(file.path(res_dir, "Tcells_stimulated_lfsr_signif_num_by_exp_bins.pdf"), width = 14, height = 3)
ggplot(lfsr_signif_num_bins.df, aes(x=guide, y=num_genes, fill=bin)) +
geom_bar(position="stack", stat="identity") +
scale_fill_brewer(palette = "Greens") +
guides(fill=guide_legend(title="Gene expression level")) +
labs(x = "Target genes",
y = "Number of DEGs",
title = "Number of DEGs detected by gene expression level") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
legend.position = "right",
legend.text = element_text(size = 13))
# dev.off()Session Information
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 WebGestaltR_0.4.4 kableExtra_1.3.4
[4] ComplexHeatmap_2.6.2 gridExtra_2.3 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
[10] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[13] ggplot2_3.3.5 tidyverse_1.3.1 Matrix_1.4-1
[16] data.table_1.14.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] matrixStats_0.62.0 fs_1.5.2 lubridate_1.8.0
[4] doParallel_1.0.17 webshot_0.5.2 RColorBrewer_1.1-3
[7] httr_1.4.2 rprojroot_2.0.2 doRNG_1.8.2
[10] tools_4.0.4 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.3
[16] BiocGenerics_0.36.1 colorspace_2.0-3 GetoptLong_1.0.5
[19] withr_2.5.0 tidyselect_1.1.2 processx_3.5.3
[22] compiler_4.0.4 git2r_0.30.1 cli_3.3.0
[25] rvest_1.0.2 Cairo_1.6-0 xml2_1.3.3
[28] labeling_0.4.2 sass_0.4.1 scales_1.2.0
[31] callr_3.7.0 apcluster_1.4.10 systemfonts_1.0.4
[34] digest_0.6.29 svglite_2.0.0 rmarkdown_2.13
[37] pkgconfig_2.0.3 htmltools_0.5.3 highr_0.9
[40] dbplyr_2.1.1 fastmap_1.1.0 rlang_1.0.4
[43] GlobalOptions_0.1.2 readxl_1.4.0 rstudioapi_0.13
[46] farver_2.1.1 shape_1.4.6 jquerylib_0.1.4
[49] generics_0.1.3 jsonlite_1.8.0 magrittr_2.0.3
[52] Rcpp_1.0.9 munsell_0.5.0 S4Vectors_0.28.1
[55] fansi_1.0.3 lifecycle_1.0.1 stringi_1.7.6
[58] whisker_0.4 yaml_2.3.5 parallel_4.0.4
[61] promises_1.2.0.1 crayon_1.5.1 haven_2.5.0
[64] pander_0.6.5 circlize_0.4.15 hms_1.1.1
[67] knitr_1.38 ps_1.7.1 pillar_1.8.0
[70] igraph_1.3.4 rjson_0.2.21 rngtools_1.5.2
[73] codetools_0.2-18 stats4_4.0.4 reprex_2.0.1
[76] glue_1.6.2 evaluate_0.16 getPass_0.2-2
[79] modelr_0.1.8 foreach_1.5.2 png_0.1-7
[82] vctrs_0.4.1 tzdb_0.3.0 httpuv_1.6.5
[85] cellranger_1.1.0 gtable_0.3.0 clue_0.3-60
[88] assertthat_0.2.1 xfun_0.30 broom_0.8.0
[91] later_1.3.0 viridisLite_0.4.0 iterators_1.0.14
[94] IRanges_2.24.1 cluster_2.1.3 ellipsis_0.3.2
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 WebGestaltR_0.4.4 kableExtra_1.3.4
[4] ComplexHeatmap_2.6.2 gridExtra_2.3 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
[10] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[13] ggplot2_3.3.5 tidyverse_1.3.1 Matrix_1.4-1
[16] data.table_1.14.2 workflowr_1.7.0
loaded via a namespace (and not attached): [1] matrixStats_0.62.0 fs_1.5.2 lubridate_1.8.0
[4] doParallel_1.0.17 webshot_0.5.2 RColorBrewer_1.1-3 [7] httr_1.4.2 rprojroot_2.0.2 doRNG_1.8.2
[10] tools_4.0.4 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.3
[16] BiocGenerics_0.36.1 colorspace_2.0-3 GetoptLong_1.0.5
[19] withr_2.5.0 tidyselect_1.1.2 processx_3.5.3
[22] compiler_4.0.4 git2r_0.30.1 cli_3.3.0
[25] rvest_1.0.2 Cairo_1.6-0 xml2_1.3.3
[28] labeling_0.4.2 sass_0.4.1 scales_1.2.0
[31] callr_3.7.0 apcluster_1.4.10 systemfonts_1.0.4
[34] digest_0.6.29 svglite_2.0.0 rmarkdown_2.13
[37] pkgconfig_2.0.3 htmltools_0.5.3 highr_0.9
[40] dbplyr_2.1.1 fastmap_1.1.0 rlang_1.0.4
[43] GlobalOptions_0.1.2 readxl_1.4.0 rstudioapi_0.13
[46] farver_2.1.1 shape_1.4.6 jquerylib_0.1.4
[49] generics_0.1.3 jsonlite_1.8.0 magrittr_2.0.3
[52] Rcpp_1.0.9 munsell_0.5.0 S4Vectors_0.28.1
[55] fansi_1.0.3 lifecycle_1.0.1 stringi_1.7.6
[58] whisker_0.4 yaml_2.3.5 parallel_4.0.4
[61] promises_1.2.0.1 crayon_1.5.1 haven_2.5.0
[64] pander_0.6.5 circlize_0.4.15 hms_1.1.1
[67] knitr_1.38 ps_1.7.1 pillar_1.8.0
[70] igraph_1.3.4 rjson_0.2.21 rngtools_1.5.2
[73] codetools_0.2-18 stats4_4.0.4 reprex_2.0.1
[76] glue_1.6.2 evaluate_0.16 getPass_0.2-2
[79] modelr_0.1.8 foreach_1.5.2 png_0.1-7
[82] vctrs_0.4.1 tzdb_0.3.0 httpuv_1.6.5
[85] cellranger_1.1.0 gtable_0.3.0 clue_0.3-60
[88] assertthat_0.2.1 xfun_0.30 broom_0.8.0
[91] later_1.3.0 viridisLite_0.4.0 iterators_1.0.14
[94] IRanges_2.24.1 cluster_2.1.3 ellipsis_0.3.2