Run differential gene expression and gene-sets analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-05-12

Checks: 7 0

Knit directory: diamantopoulou-ctc-dynamics/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220425)

The command set.seed(20220425) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: a747daf

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version a747daf. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/0_differential_expression_gsea_gsva.md
    Untracked:  analysis/about.md
    Untracked:  analysis/br16_dge.md
    Untracked:  analysis/br16_pca.md
    Untracked:  analysis/core_gene_sets.md
    Untracked:  analysis/gsea_across_models.md
    Untracked:  analysis/index.md
    Untracked:  analysis/license.md
    Untracked:  analysis/patients_ctc_counts_distribution.md
    Untracked:  data/differential_expression/
    Untracked:  data/patients/
    Untracked:  data/resources/
    Untracked:  data/sce/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/0_differential_expression_gsea_gsva.Rmd) and HTML (docs/0_differential_expression_gsea_gsva.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	1fc87b5	fcg-bio	2022-05-10	Build site.
html	8fb5513	fcg-bio	2022-05-10	Build site.
html	74b1891	fcg-bio	2022-04-26	Build site.
html	c0865c6	fcg-bio	2022-04-26	Build site.
html	a136590	fcg-bio	2022-04-26	Build site.
html	bfb622b	fcg-bio	2022-04-26	Build site.
html	1006c84	fcg-bio	2022-04-26	Build site.
Rmd	0ded9f5	fcg-bio	2022-04-26	added final code

---

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