Run differential gene expression and gene-sets analysis

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Last updated: 2022-05-10

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Knit directory: diamantopoulou-ctc-dynamics/

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Load libraries, additional functions and data

Setup environment

knitr::opts_chunk$set(results='asis', echo=TRUE, message=FALSE, warning=FALSE, error=FALSE, fig.align = 'center', fig.width = 3.5, fig.asp = 0.618, dpi = 600, dev = c("png", "pdf"), fig.showtext = TRUE)

options(stringsAsFactors = FALSE)

Load packages

library(tidyverse)
library(scater)
library(scran)
library(edgeR)
library(clusterProfiler)
library(GSVA)
library(foreach)

Load shared variables

source("./configuration/rmarkdown/shared_variables.R")

Load custom functions

source('./code/R-functions/dge_wrappers.r')
source('./code/R-functions/gse_omnibus.r')
source('./code/R-functions/gse_report.r')
clean_msigdb_names <- function(x) x %>% gsub('REACTOME_', '', .) %>% gsub('WP_', '', .) %>% gsub('BIOCARTA_', '', .) %>% gsub('KEGG_', '', .) %>% gsub('PID_', '', .) %>% gsub('GOBP_', '', .) %>% gsub('_', ' ', .)

Load MSigDB gene sets

gmt_files_symbols <- list(
  msigdb.c2.cp = './data/resources/MSigDB/v7.4/c2.cp.v7.4.symbols.gmt',
  msigdb.c5.bp = './data/resources/MSigDB/v7.4/c5.go.bp.v7.4.symbols.gmt'
)

gmt_files_entrez <- list(
  msigdb.c2.cp = './data/resources/MSigDB/v7.4/c2.cp.v7.4.entrez.gmt',
  msigdb.c5.bp = './data/resources/MSigDB/v7.4/c5.go.bp.v7.4.entrez.gmt'
)

# combine MSigDB.C2.CP and GO:BP
new_file <- gsub('c2.cp', 'c2.cp.c5.bp', gmt_files_symbols$msigdb.c2.cp)
cat_cmd <- paste('cat', gmt_files_symbols$msigdb.c5.bp,  gmt_files_symbols$msigdb.c2.cp, '>',new_file)
system(cat_cmd)
gmt_files_symbols$msigdb.c2.cp.c5.bp <- new_file

gmt_sets <- lapply(gmt_files_symbols, function(x) read.gmt(x) %>% collect %>% .[['term']] %>% levels)

NSG-CDX-BR16 : all samples

Configuration

use_sce <- readRDS(file = file.path(params$sce_dir, 'sce_br16.rds'))
output_dir <- './data/differential_expression/br16'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run DGE analysis

dge <- edgeR_dge(
  use_sce,
  # Desing configuration for differential expression
  group_var =  'timepoint',
  group_sample = 'resting',
  group_ref = 'active',
  numeric_covar = NULL,
  batch_vars = NULL,
  design_formula = "~ 0 + timepoint",
  coef = 'last',
  # Conversion from SingleCellExperiment to DGEList
  spike_normalization = FALSE,
  assay_to_DGEList = 'counts',
  assay_to_row_filter = "counts",
  use_colData = NULL,
  use_rowData = NULL,
  # Feature filtering parameters
  use_filterByExpr = TRUE,
  min_counts = params$min_counts,
  min_present_prop = params$min_present_prop,
  # EdgeR workflow configuration
  run_calcNormFactors = 'TMM',
  estimateDisp_robust = FALSE,
  estimateDisp_trend.method = "locfit",
  glmQLFit_robust = TRUE,
  glm_approach = "QLF",
  # Output configuration
  adjust_method = 'BH',
  assays_from_SingleCellExperiment = NULL
  )

# Add gene description
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensembl <-  biomaRt::useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
gene_desc <- biomaRt::getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values = dge$results$gene_name, mart =ensembl) %>% 
  dplyr::rename('gene_name' = 'external_gene_name')
use_res <- dge$results %>%  left_join(., gene_desc)
dge$results <- use_res %>% 
  filter(!duplicated(feature)) %>% 
  mutate(rownames = feature) %>% 
  column_to_rownames('rownames')

detach("package:biomaRt", unload=TRUE)

saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))

Run GSEA

dge <- readRDS(file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))
res_gse <- gse_omnibus(
    feature_names = dge$results$gene_name,
    p = dge$results$FDR,
    fc = dge$results$logFC,
    gmt_files = gmt_files_symbols, 

    save_intermediates = file.path(output_dir, 'gse_omnibus'),
    
    run_all_ora = FALSE,
    run_all_gsea = FALSE,
    run_GSEA = TRUE,
    run_gseGO = FALSE,

    args_gse = list(minGSSize = 10, maxGSSize = 500, pvalueCutoff = 1),

    )
saveRDS(res_gse, file = file.path(output_dir, 'gse_gsea.rds'))

Clean data

rm(use_sce)
rm(dge)
rm(res_gse)

NSG-CDX-BR16 : CTC-Cluster and CTC-WBC

Configuration

use_sce <- use_sce[,use_sce$sample_type_g == 'ctc_cluster']
output_dir <- './data/differential_expression/br16-ctc_cluster_and_wbc'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run DGE analysis

dge <- edgeR_dge(
  use_sce,
  # Desing configuration for differential expression
  group_var =  'timepoint',
  group_sample = 'resting',
  group_ref = 'active',
  numeric_covar = NULL,
  batch_vars = NULL,
  design_formula = "~ 0 + timepoint",
  coef = 'last',
  # Conversion from SingleCellExperiment to DGEList
  spike_normalization = FALSE,
  assay_to_DGEList = 'counts',
  assay_to_row_filter = "counts",
  use_colData = NULL,
  use_rowData = NULL,
  # Feature filtering parameters
  use_filterByExpr = TRUE,
  min_counts = params$min_counts,
  min_present_prop = params$min_present_prop,
  # EdgeR workflow configuration
  run_calcNormFactors = 'TMM',
  estimateDisp_robust = FALSE,
  estimateDisp_trend.method = "locfit",
  glmQLFit_robust = TRUE,
  glm_approach = "QLF",
  # Output configuration
  adjust_method = 'BH',
  assays_from_SingleCellExperiment = NULL
  )

# Add gene description
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensembl <-  biomaRt::useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
gene_desc <- biomaRt::getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values = dge$results$gene_name, mart =ensembl) %>% 
  dplyr::rename('gene_name' = 'external_gene_name')
use_res <- dge$results %>%  left_join(., gene_desc)
dge$results <- use_res %>% 
  filter(!duplicated(feature)) %>% 
  mutate(rownames = feature) %>% 
  column_to_rownames('rownames')

detach("package:biomaRt", unload=TRUE)

saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))

Run GSEA

dge <- readRDS(file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))
res_gse <- gse_omnibus(
    feature_names = dge$results$gene_name,
    p = dge$results$FDR,
    fc = dge$results$logFC,
    gmt_files = gmt_files_symbols, 

    save_intermediates = file.path(output_dir, 'gse_omnibus'),
    
    run_all_ora = FALSE,
    run_all_gsea = FALSE,
    run_GSEA = TRUE,
    run_gseGO = FALSE,

    args_gse = list(minGSSize = 10, maxGSSize = 500, pvalueCutoff = 1),

    )
saveRDS(res_gse, file = file.path(output_dir, 'gse_gsea.rds'))

Clean data

rm(use_sce)
rm(dge)
rm(res_gse)

NSG-CDX-BR16 : CTC-Single

Configuration

use_sce <- use_sce[,use_sce$sample_type_g == 'ctc_single']
output_dir <- './data/differential_expression/br16-ctc_single'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run DGE analysis

dge <- edgeR_dge(
  use_sce,
  # Desing configuration for differential expression
  group_var =  'timepoint',
  group_sample = 'resting',
  group_ref = 'active',
  numeric_covar = NULL,
  batch_vars = NULL,
  design_formula = "~ 0 + timepoint",
  coef = 'last',
  # Conversion from SingleCellExperiment to DGEList
  spike_normalization = FALSE,
  assay_to_DGEList = 'counts',
  assay_to_row_filter = "counts",
  use_colData = NULL,
  use_rowData = NULL,
  # Feature filtering parameters
  use_filterByExpr = TRUE,
  min_counts = params$min_counts,
  min_present_prop = params$min_present_prop,
  # EdgeR workflow configuration
  run_calcNormFactors = 'TMM',
  estimateDisp_robust = FALSE,
  estimateDisp_trend.method = "locfit",
  glmQLFit_robust = TRUE,
  glm_approach = "QLF",
  # Output configuration
  adjust_method = 'BH',
  assays_from_SingleCellExperiment = NULL
  )

# Add gene description
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensembl <-  biomaRt::useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
gene_desc <- biomaRt::getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values = dge$results$gene_name, mart =ensembl) %>% 
  dplyr::rename('gene_name' = 'external_gene_name')
use_res <- dge$results %>%  left_join(., gene_desc)
dge$results <- use_res %>% 
  filter(!duplicated(feature)) %>% 
  mutate(rownames = feature) %>% 
  column_to_rownames('rownames')

detach("package:biomaRt", unload=TRUE)

saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))

Run GSEA

dge <- readRDS(file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))
res_gse <- gse_omnibus(
    feature_names = dge$results$gene_name,
    p = dge$results$FDR,
    fc = dge$results$logFC,
    gmt_files = gmt_files_symbols, 

    save_intermediates = file.path(output_dir, 'gse_omnibus'),
    
    run_all_ora = FALSE,
    run_all_gsea = FALSE,
    run_GSEA = TRUE,
    run_gseGO = FALSE,

    args_gse = list(minGSSize = 10, maxGSSize = 500, pvalueCutoff = 1),

    )
saveRDS(res_gse, file = file.path(output_dir, 'gse_gsea.rds'))

Clean data

rm(use_sce)
rm(dge)
rm(res_gse)

NSG-LM2

Configuration

use_sce <- readRDS(file = file.path(params$sce_dir, 'sce_lm2.rds'))
output_dir <- './data/differential_expression/lm2'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run DGE analysis

dge <- edgeR_dge(
  use_sce,
  # Desing configuration for differential expression
  group_var =  'timepoint',
  group_sample = 'resting',
  group_ref = 'active',
  numeric_covar = NULL,
  batch_vars = NULL,
  design_formula = "~ 0 + timepoint",
  coef = 'last',
  # Conversion from SingleCellExperiment to DGEList
  spike_normalization = FALSE,
  assay_to_DGEList = 'counts',
  assay_to_row_filter = "counts",
  use_colData = NULL,
  use_rowData = NULL,
  # Feature filtering parameters
  use_filterByExpr = TRUE,
  min_counts = params$min_counts,
  min_present_prop = params$min_present_prop,
  # EdgeR workflow configuration
  run_calcNormFactors = 'TMM',
  estimateDisp_robust = FALSE,
  estimateDisp_trend.method = "locfit",
  glmQLFit_robust = TRUE,
  glm_approach = "QLF",
  # Output configuration
  adjust_method = 'BH',
  assays_from_SingleCellExperiment = NULL
  )

# Add gene description
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensembl <-  biomaRt::useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
gene_desc <- biomaRt::getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values = dge$results$gene_name, mart =ensembl) %>% 
  dplyr::rename('gene_name' = 'external_gene_name')
use_res <- dge$results %>%  left_join(., gene_desc)
dge$results <- use_res %>% 
  filter(!duplicated(feature)) %>% 
  mutate(rownames = feature) %>% 
  column_to_rownames('rownames')

detach("package:biomaRt", unload=TRUE)

saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))

Clean data

rm(use_sce)
rm(dge)

Patient

Configuration

use_sce <- readRDS(file = file.path(params$sce_dir, 'sce_patient.rds'))
output_dir <- './data/differential_expression/patient'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run DGE analysis

dge <- edgeR_dge(
  use_sce,
  # Desing configuration for differential expression
  group_var =  'timepoint',
  group_sample = 'resting',
  group_ref = 'active',
  numeric_covar = NULL,
  batch_vars = NULL,
  design_formula = "~ 0 + timepoint",
  coef = 'last',
  # Conversion from SingleCellExperiment to DGEList
  spike_normalization = FALSE,
  assay_to_DGEList = 'counts',
  assay_to_row_filter = "counts",
  use_colData = NULL,
  use_rowData = NULL,
  # Feature filtering parameters
  use_filterByExpr = TRUE,
  min_counts = params$min_counts,
  min_present_prop = params$min_present_prop,
  # EdgeR workflow configuration
  run_calcNormFactors = 'TMM',
  estimateDisp_robust = FALSE,
  estimateDisp_trend.method = "locfit",
  glmQLFit_robust = TRUE,
  glm_approach = "QLF",
  # Output configuration
  adjust_method = 'BH',
  assays_from_SingleCellExperiment = NULL
  )

# Add gene description
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensembl <-  biomaRt::useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
gene_desc <- biomaRt::getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values = dge$results$gene_name, mart =ensembl) %>% 
  dplyr::rename('gene_name' = 'external_gene_name')
use_res <- dge$results %>%  left_join(., gene_desc)
dge$results <- use_res %>% 
  filter(!duplicated(feature)) %>% 
  mutate(rownames = feature) %>% 
  column_to_rownames('rownames')

detach("package:biomaRt", unload=TRUE)

saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))

Clean data

rm(use_sce)
rm(dge)

LM2 time kinetics

Configuration

use_sce <- readRDS(file = file.path(params$sce_dir, 'sce_lm2_tk.rds'))
output_dir <- './data/differential_expression/lm2_tk'
if(!file.exists(output_dir))
  dir.create(output_dir, recursive = TRUE)

Run GSVA run with gene-set size between 5 and 700. Original GSEA analysis was performed with 10-500, but with this new treshold we make sure that all the gene sets from BR16 results are included in the analysis, as the effective gene set (expressed genes) might be different in GSVA analysis.

For this analysis we remove samples from timepoint ZT0 (06:00). It only contains one replicate and can bias results. The timepoint will be added for visualization.

use_sce <- use_sce[,!use_sce$timepoint %in% c('0600')]
rownames(use_sce) <- rowData(use_sce)$gene_name
use_gmt_file <- "./data/resources/MSigDB/v7.4/c2.cp.c5.bp.v7.4.symbols.gmt"
gset <- GSEABase::getGmt(use_gmt_file)
gset_db <- foreach(x = gset, .combine = rbind) %do% {c(term_size = length(x@geneIds))} %>% data.frame()
gset_db$term_name <- names(gset)

gsva_res <- gsva(assay(use_sce, 'logcpm'), 
                   method = 'gsva',
                   gset.idx.list = gset, 
                   min.sz = 5, 
                   max.sz = 700, 
                   kcdf = "Gaussian",
                   mx.diff = TRUE, 
                   verbose = FALSE)

saveRDS(gsva_res, file = file.path(output_dir, 'gsva_c2.cp.c5.bp.rds'))

Session information

sessionInfo()

R version 4.1.0 (2021-05-18) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16

Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

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] parallel stats4 stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] foreach_1.5.1 GSVA_1.40.1
[3] clusterProfiler_4.0.5 edgeR_3.34.1
[5] limma_3.48.3 scran_1.20.1
[7] scater_1.20.1 scuttle_1.2.1
[9] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0 [11] Biobase_2.52.0 GenomicRanges_1.44.0
[13] GenomeInfoDb_1.28.4 IRanges_2.26.0
[15] S4Vectors_0.30.2 BiocGenerics_0.38.0
[17] MatrixGenerics_1.4.3 matrixStats_0.61.0
[19] forcats_0.5.1 stringr_1.4.0
[21] dplyr_1.0.7 purrr_0.3.4
[23] readr_2.0.2 tidyr_1.1.4
[25] tibble_3.1.5 ggplot2_3.3.5
[27] tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached): [1] utf8_1.2.2 tidyselect_1.1.1
[3] RSQLite_2.2.8 AnnotationDbi_1.54.1
[5] grid_4.1.0 BiocParallel_1.26.2
[7] scatterpie_0.1.7 munsell_0.5.0
[9] ScaledMatrix_1.0.0 codetools_0.2-18
[11] statmod_1.4.36 withr_2.4.2
[13] colorspace_2.0-2 GOSemSim_2.18.1
[15] knitr_1.36 rstudioapi_0.13
[17] DOSE_3.18.3 git2r_0.28.0
[19] GenomeInfoDbData_1.2.6 polyclip_1.10-0
[21] bit64_4.0.5 farver_2.1.0
[23] rhdf5_2.36.0 rprojroot_2.0.2
[25] downloader_0.4 treeio_1.16.2
[27] vctrs_0.3.8 generics_0.1.1
[29] xfun_0.27 R6_2.5.1
[31] ggbeeswarm_0.6.0 graphlayouts_0.7.1
[33] rsvd_1.0.5 locfit_1.5-9.4
[35] rhdf5filters_1.4.0 bitops_1.0-7
[37] cachem_1.0.6 fgsea_1.18.0
[39] gridGraphics_0.5-1 DelayedArray_0.18.0
[41] assertthat_0.2.1 showtext_0.9-4
[43] promises_1.2.0.1 scales_1.1.1
[45] ggraph_2.0.5 enrichplot_1.12.3
[47] beeswarm_0.4.0 gtable_0.3.0
[49] beachmat_2.8.1 tidygraph_1.2.0
[51] rlang_0.4.12 splines_4.1.0
[53] lazyeval_0.2.2 broom_0.7.10
[55] yaml_2.2.1 reshape2_1.4.4
[57] modelr_0.1.8 backports_1.3.0
[59] httpuv_1.6.3 qvalue_2.24.0
[61] tools_4.1.0 ggplotify_0.1.0
[63] ellipsis_0.3.2 jquerylib_0.1.4
[65] RColorBrewer_1.1-2 Rcpp_1.0.7
[67] plyr_1.8.6 sparseMatrixStats_1.4.2
[69] zlibbioc_1.38.0 RCurl_1.98-1.5
[71] viridis_0.6.2 cowplot_1.1.1
[73] haven_2.4.3 ggrepel_0.9.1
[75] cluster_2.1.2 fs_1.5.0
[77] magrittr_2.0.1 data.table_1.14.2
[79] DO.db_2.9 reprex_2.0.1
[81] whisker_0.4 xtable_1.8-4
[83] hms_1.1.1 patchwork_1.1.1
[85] evaluate_0.14 XML_3.99-0.8
[87] readxl_1.3.1 gridExtra_2.3
[89] compiler_4.1.0 shadowtext_0.0.9
[91] crayon_1.4.2 htmltools_0.5.2
[93] ggfun_0.0.4 later_1.3.0
[95] tzdb_0.2.0 aplot_0.1.1
[97] lubridate_1.8.0 DBI_1.1.1
[99] tweenr_1.0.2 dbplyr_2.1.1
[101] MASS_7.3-54 Matrix_1.3-4
[103] cli_3.1.0 metapod_1.0.0
[105] igraph_1.2.7 pkgconfig_2.0.3
[107] xml2_1.3.2 annotate_1.70.0
[109] ggtree_3.0.4 vipor_0.4.5
[111] bslib_0.3.1 dqrng_0.3.0
[113] XVector_0.32.0 rvest_1.0.2
[115] yulab.utils_0.0.4 digest_0.6.28
[117] graph_1.70.0 showtextdb_3.0
[119] Biostrings_2.60.2 rmarkdown_2.11
[121] cellranger_1.1.0 fastmatch_1.1-3
[123] tidytree_0.3.5 GSEABase_1.54.0
[125] DelayedMatrixStats_1.14.3 lifecycle_1.0.1
[127] nlme_3.1-153 jsonlite_1.7.2
[129] Rhdf5lib_1.14.2 BiocNeighbors_1.10.0
[131] viridisLite_0.4.0 fansi_0.5.0
[133] pillar_1.6.4 lattice_0.20-45
[135] KEGGREST_1.32.0 fastmap_1.1.0
[137] httr_1.4.2 GO.db_3.13.0
[139] glue_1.4.2 iterators_1.0.13
[141] png_0.1-7 bluster_1.2.1
[143] bit_4.0.4 HDF5Array_1.20.0
[145] ggforce_0.3.3 stringi_1.7.5
[147] sass_0.4.0 blob_1.2.2
[149] BiocSingular_1.8.1 memoise_2.0.0
[151] irlba_2.3.3 ape_5.5
[153] sysfonts_0.8.5