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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$msigdb.c2.cp.c5.bp <- new_file
gmt_sets <- lapply(gmt_files_symbols, function(x) read.gmt(x) %>% collect %>% .[['term']] %>% levels)Configuration
use_sce <- readRDS(file = file.path(params$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)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$logFC,
gmt_files = gmt_files_symbols,
save_intermediates = file.path(output_dir, 'gse_omnibus'),
run_all_ora = FALSE,
run_all_gsea = FALSE,
run_GSEA = TRUE,
run_gseGO = FALSE,
args_gse = list(minGSSize = 10, maxGSSize = 500, pvalueCutoff = 1),
)
saveRDS(res_gse, file = file.path(output_dir, 'gse_gsea.rds'))Clean data
rm(use_sce)
rm(dge)
rm(res_gse)Configuration
use_sce <- use_sce[,use_sce$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)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$results <- use_res %>%
filter(!duplicated(feature)) %>%
mutate(rownames = feature) %>%
column_to_rownames('rownames')
detach("package:biomaRt", unload=TRUE)
saveRDS(dge, file = file.path(output_dir, 'dge_edgeR_QLF_robust.rds'))Clean data
rm(use_sce)
rm(dge)Configuration
use_sce <- readRDS(file = file.path(params$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)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'))
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