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source('code/ms00_utils.R')
library("knitr")
Cartoon of study design.
include_graphics("figure/additional_figures/study_cartoon.png", error = FALSE)
UMAP
applied to subset of 100k cells (subset because of memory limits), using parameters min_dist = 1, spread = 2
, otherwise defaults. Clusters are determined by Louvain clustering applied to the conos
graph, followed by post-hoc splitting of two clusters based on biological expectations (COPs and immune cells), and merging of very similar clusters (using SCCAF
).
include_graphics("figure/ms12_markers.Rmd/plot_umap_final_celltypes_sel-1.png", error = FALSE)
UMAP
plot of just oligodendrocyte and OPC celltypes.
include_graphics("figure/ms08_modules.Rmd/plot_umap_oligos-1.png", error = FALSE)
UMAP
for all celltypes annotated with MS / CTR and WM / GM.
include_graphics("figure/ms08_modules.Rmd/plot_umap_ctr_ms-1.png", error = FALSE)
Median oNMF
module score per fine celltype for OPC and oligo modules and cells. Columns are scaled to have max value equal to 1.
include_graphics("figure/ms08_modules.Rmd/plot_scores_by_type_scaled-1.png", error = FALSE)
PAGA
applied to oligodendrocytes and OPCs / COPs across all samples.
include_graphics("figure/ms11_paga.Rmd/plot_paga_olg_wm_gm-1.png", error = FALSE)
include_graphics("figure/ms99_deg_figures_wm.Rmd/plot_de_barplot_gm_wm-1.png", error = FALSE)
Dotplot of Hallmark module results for GM and WM.
include_graphics("figure/ms99_deg_figures_wm.Rmd/plot_gsea_dotplot_gm_wm-1.png", error = FALSE)
Heatmap of logFCs for selected genes significant in excitatory neurons.
include_graphics("figure/ms99_deg_figures_gm.Rmd/plot_heatmap_logfcs-1.png", error = FALSE)
Heatmap of logFCs for interferon genes significant in oligodendroglia.
include_graphics("figure/ms99_deg_figures_wm.Rmd/plot_heatmap_logfcs-1.png", error = FALSE)
Genetic enrichment of differentially expressed genes.
include_graphics("figure/additional_figures/gwas_de_barplots.png", error = FALSE)
Clustering of WM fold change profiles. Restricted to genes where at least one lesion type has FDR < 5%. Clusters split so that average logFC difference between clusters is > log(4); clusters with fewer than 5 genes not shown; clusters ordered in descending order of mean logFC.
include_graphics("figure/ms99_deg_figures_wm.Rmd/plot_fc_cluster_profiles_sel-1.png", error = FALSE)
Expression heatmap of WM genes, ordered by lesion type.
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_overview_expression_narrow-2.png", error = FALSE)
Expression heatmap of WM genes, ordered by hierarchical clustering.
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_overview_expression_narrow-1.png", error = FALSE)
Expression heatmap of GM genes, ordered by lesion type.
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_overview_expression_narrow-1.png", error = FALSE)
Version | Author | Date |
---|---|---|
7f9bc30 | Macnair | 2022-04-05 |
Expression heatmap of GM genes, ordered by hierarchical clustering.
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_overview_expression_narrow-2.png", error = FALSE)
Version | Author | Date |
---|---|---|
7f9bc30 | Macnair | 2022-04-05 |
Proportions of fine celltypes in healthy GM and healthy WM. Neuronal celltypes excluded. Negative binomial model fit to absolute numbers for each celltype, using total number of cells in sample as offset. FDR calculated across all celltypes.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_wm_vs_gm-1.png", error = FALSE)
Median oNMF
module score per fine celltype for astrocyte modules and cells. Columns are scaled to have max value equal to 1.
include_graphics("figure/ms08_modules.Rmd/plot_scores_by_type_scaled-5.png", error = FALSE)
Contribution to variability in celltype abundances explained by lesion + patient in WM.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_lrt_results-1.png", error = FALSE)
Contribution to variability in celltype abundances explained by lesion + patient in GM, including 4 layer PCs.
# include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_lrt_results-2.png", error = FALSE)
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_lrt_results-6.png", error = FALSE)
Differential abundance results for WM.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_bootstraps_lesions_signif-1.png", error = FALSE)
Differential abundance results for GM (with layers factored out).
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_bootstraps_lesions_signif-6.png", error = FALSE)
Validation of GPR17+ cell abundances by IHC staining.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_no_gpr17_cells-1.png", error = FALSE)
Patient stratification via MOFA factors.
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/plot_factors_heatmap_few-1.png", error = FALSE)
Factor 1 top genes
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_factor1-1.png", error = FALSE)
Factor 3 top genes
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_factor3-1.png", error = FALSE)
Factor 5 top genes
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_factor5-1.png", error = FALSE)
WM oligodendroglia proportions barplot
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_sample_splits_bars_oligos-1.png", error = FALSE)
Summary of numbers of nuclei, samples and donors excluded and retained by QC procedure, split by various metadata labels. Distributions of metadata labels split by control and MS samples: age_cat is age at death binned into categories; yrs_w_ms is years with MS, binned into categories, and NA for control samples; pmi_cat is post mortem interval, binned into categories; brain_bank is the sample source; and seq_pool is the batch in which the samples were sequenced.
include_graphics("figure/ms03_SampleQC_summary.Rmd/plot_totals_split_by_meta-1.png", error = FALSE)
Distributions of metadata labels split by control and MS samples. pmi_minutes is post mortem interval in minutes.
include_graphics("figure/ms03_SampleQC_summary.Rmd/plot_ctrl_vs_ms_metadata-1.png", error = FALSE)
Summary of QC metrics of post-QC WM samples. donor is colour for donor ID, with grey values used for donors contributing only one sample; mito pct is the proportion of reads in the sample that are mitochondrial; pct unspliced is the proportion of reads in the sample that are unspliced as opposed to spliced mRNA. Colours in heatmap are the z-scores for each QC metric column, with colours chosen so that red is good and blue is bad (e.g. low library size, or high mitochondrial read percentage).
include_graphics("figure/ms03_SampleQC_summary.Rmd/plot_qc_summary_heatmap-1.png", error = FALSE)
Summary of QC metrics of post-QC GM samples, as for ED1c.
include_graphics("figure/ms03_SampleQC_summary.Rmd/plot_qc_summary_heatmap-2.png", error = FALSE)
QC metric summaries for fine celltypes. Each point is a sample with >= 10 nuclei of that type, showing median QC metric value for those nuclei in that sample (with exception of number of nuclei).
include_graphics("figure/ms13_labelling.Rmd/plot_qc_stats_by_cluster-1.png", error = FALSE)
UMAP
embedding (as in Fig. 1b) annotated with proportion of nuclei in binned region of UMAP
embedding coming from MS as opposed to control samples (left) and WM as opposed to GM samples (right). In both plots, white corresponds to the average proportion across all cells (i.e. 20% of nuclei are from MS samples, and 60% of nuclei are from GM samples).
include_graphics("figure/ms08_modules.Rmd/plot_umap_ctr_ms-1.png", error = FALSE)
Expression of marker genes selected for broad celltypes, and for fine celltypes. CPM indicates counts per million, number of counts of gene divided by total number of pseudobulk counts. Expression calculated across all cells and samples.
include_graphics("figure/ms12_markers.Rmd/plot_dotplot_dheeraj_compact-1.png", error = FALSE)
Comparison of clusters with Seurat clusters.
include_graphics("figure/additional_figures/Conos_celltypes_perc_of_cells_in_Seurat.png", error = FALSE)
include_graphics("figure/ms13_labelling.Rmd/plot_cluster_entropies-1.png", error = FALSE)
Expression of top genes for each oligo-OPC module (gene selected if weight >2%). Expression calculated across all cells and samples.
include_graphics("figure/ms08_modules.Rmd/plot_genes_dotplot-2.png", error = FALSE)
Distribution of model fits for genes for each broad celltype in GM. y-axis shows standard deviation of random (donor) effects for each gene. x-axis shows log2FC of lesion type with smallest p-value for each gene. Horizontal dashed lines show cutoff at SD = log(1.5); vertical dashed lines show cutoff at abs(log2FC) = log(1.5).
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_interesting_gs-1.png", error = FALSE)
Distribution of model fits for genes for each broad celltype in WM. y-axis shows standard deviation of random (donor) effects for each gene. x-axis shows log2FC of lesion type with smallest p-value for each gene. Horizontal dashed lines show cutoff at SD = log(1.5); vertical dashed lines show cutoff at abs(log2FC) = log(1.5).
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_interesting_gs-1.png", error = FALSE)
include_graphics("figure/ms99_deg_figures_gm.Rmd/plot_de_barplot_fine-1.png", error = FALSE)
include_graphics("figure/ms99_deg_figures_wm.Rmd/plot_de_barplot_fine-1.png", error = FALSE)
Cartoon giving intuition of how MOFA+ identifies tissue-level factors.
include_graphics("figure/additional_figures/mofa_cartoon_2022-02-04.png", error = FALSE)
First panel shows variation in expression for each celltype explained by MOFA+ factors in GM; second panel shows extent to which MOFA+ factors can be accounted for by metadata. Variance explained in first panel is per celltype, so the maximum total for each row is 100%. Pseudo-R2 values are calculated by fitting a mixed model to each factor, using model factor_value ~ lesion_type + sex + age_scale + pmi_cat + (1 | donor_id), and the glmmTMB
function in package glmmTMB
. Pseudo-R2 values are determined by Nakagawa’s R2, showing proportion of variance explained using fixed components only, and including a donor effect (see Methods).
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_factor_r2s-1.png", error = FALSE)
As for S5B, for WM.
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/fig_factor_r2s-1.png", error = FALSE)
Distributions of MOFA factors in GM. Colour denotes donor; grey is used where only one sample was obtained from a donor.
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_mofa_factors_lesions-1.png", error = FALSE)
Factor 1 top genes in GM.
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/fig_factor1-1.png", error = FALSE)
Pairwise distributions of MOFA factors in GM. Colour denotes donor; grey is used where only one sample was obtained from a donor.
include_graphics("figure/ms15_mofa_gm_edger_libs.Rmd/plot_factors_pairwise-1.png", error = FALSE)
Pairwise distributions of MOFA factors in WM. Colour denotes donor; grey is used where only one sample was obtained from a donor.
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/plot_factors_pairwise-1.png", error = FALSE)
Upper Proportions of fine celltypes in healthy GM and healthy WM. Neuronal celltypes excluded. Negative binomial model fit to absolute numbers for each celltype, using total number of cells in sample as offset. FDR calculated across all celltypes. Lower Absolute numbers.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_wm_vs_gm_absolute-1.png", error = FALSE)
Version | Author | Date |
---|---|---|
5c9901d | Macnair | 2022-04-05 |
Median oNMF
module score per fine celltype for microglial and immune modules and cells. Columns are scaled to have max value equal to 1.
include_graphics("figure/ms08_modules.Rmd/plot_scores_by_type_scaled-2.png", error = FALSE)
Differential abundance of WM lesion samples against control WM, as calculated by bootstrapped ANCOM-BC. Left Dashed line at 0 corresponds to no difference between control and lesion. Model fitted is count ~ lesion_type + sex + age_scale + pmi_cat (where age_scale is age at death, normalized to have SD = 0.5). Point corresponds to median log2FC effect estimated by ANCOM-BC; coloured range is 80% bootstrapped confidence interval, grey range is 95% CI. Points are filled when the 95% CI excludes zero; otherwise empty. Cell type called as significant when at the 95% CI excludes 0 for at least one lesion. Right Boxplots of absolute numbers cell types in left plot. Boxplots (and outlier dots) show range of total numbers of this cell type observed across samples.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_bootstraps_lesions-1.png", error = FALSE)
Proportions of neuronal compartment per sample, split by layer-specificity of neurons. L1 and L2/L3 neurons account for relatively low proportions of NAGM samples, while L5 and L6 neurons account for high proportions of NAGM samples; vice versa for GML samples, while ctrl GM lies in the middle. This indicates that, on average, the samples are roughly ordered as follows: NAGM is deeper than ctrl GM, which is deeper than GML.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_propns_layers-1.png", error = FALSE)
Principal components of GM neuronal layer centred log ratios (CLRs; see Methods). y-axis shows absolute Spearman correlation between PC loadings and neuronal layer numbers (excluding neuronal clusters without an assigned layer number). x-axis shows the variance explained by each PC (on a log scale). Dashed lines show thresholds at 0.2 Spearman correlation, and 1% variance explained, giving up to 7 PCs that could be relevant to layers.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_layer_var_exp-1.png", error = FALSE)
Differential abundance of GM lesion samples against control WM, as calculated by bootstrapped ANCOM-BC. Left Dashed line at 0 corresponds to no difference between control and lesion. Model fitted is count ~ lesion_type + sex + age_scale + pmi_cat + layer_PC1 + layer_PC2 + layer_PC3 + layer_PC4. Point corresponds to median log2FC effect estimated by ANCOM-BC; coloured range is 80% bootstrapped confidence interval, grey range is 95% CI. Points are filled when the 95% CI excludes zero; otherwise empty. Cell type called as significant when at the 95% CI excludes 0 for at least one lesion. Right Boxplots of absolute numbers cell types in left plot. Boxplots (and outlier dots) show range of total numbers of this cell type observed across samples.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_bootstraps_lesions-6.png", error = FALSE)
Bootstrapped ANCOM-BC results including varying numbers of PCs as covariates. Number of PCs used varies from 0 to 7 (see S4B for rationale for 7). Grey lines show 95% bootstrapped confidence interval, coloured lines show 80% confidence interval; based on 20k bootstraps (large number taken to give reliable estimates of tails; see [ref: Hesterberg 2011]).
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_effect_of_pcs_lesions-1.png", error = FALSE)
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_effect_of_pcs_lesions-2.png", error = FALSE)
Heatmap of MOFA+ factors in WM (left), annotated with median QC metrics per sample (right). Left Rows are samples, annotated by metadata and oligo groupings; donor is colour for donor ID, with grey values used for donors contributing only one sample. Columns are MOFA+ factors, with signs changed to positively correlate with MS status. Right Columns are QC metrics: mito pct is the proportion of reads in the sample that are mitochondrial; pct unspliced is the proportion of reads in the sample that are unspliced as opposed to spliced mRNA; neuronal pct is the percentage of cells in the sample that are neurons. Colours in heatmap are the z-scores for each QC metric column, with colours chosen so that dark purple is ‘bad’ (e.g. low library size, or high mitochondrial read percentage).
include_graphics("figure/ms15_mofa_wm_edger_libs.Rmd/plot_factors_heatmap_qc-1.png", error = FALSE)
First two PCs of CLRs of oligodendroglia proportions. WM oligodendroglial compositional similarity between samples. Points are samples, represented by vector of proportions of 13 oligo + OPC subtypes, then transformed with centred log ratio (CLR), and PCA applied. To reduce noise, an empirical prior on sample proportions of 1k cells is used.
include_graphics("figure/ms09_ancombc_mixed.Rmd/plot_sample_splits_clrs_oligos-6.png", error = FALSE)
devtools::session_info()
- Session info ---------------------------------------------------------------
setting value
version R version 4.1.2 (2021-11-01)
os Red Hat Enterprise Linux 8.2 (Ootpa)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype C
tz Europe/Amsterdam
date 2022-04-06
pandoc 2.5 @ /apps/rocs/pRED/2020.08/cascadelake/software/Pandoc/2.5/bin/ (via rmarkdown)
- Packages -------------------------------------------------------------------
package * version date (UTC) lib source
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BiocManager * 1.30.16 2021-06-15 [3] CRAN (R 4.1.2)
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cachem 1.0.6 2021-08-19 [5] CRAN (R 4.1.2)
callr 3.7.0 2021-04-20 [5] CRAN (R 4.1.2)
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munsell 0.5.0 2018-06-12 [5] CRAN (R 4.1.2)
pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.2)
pkgbuild 1.2.1 2021-11-30 [5] CRAN (R 4.1.2)
pkgconfig 2.0.3 2019-09-22 [5] CRAN (R 4.1.2)
pkgload 1.2.4 2021-11-30 [5] CRAN (R 4.1.2)
prettyunits 1.1.1 2020-01-24 [5] CRAN (R 4.1.2)
processx 3.5.2 2021-04-30 [5] CRAN (R 4.1.2)
promises 1.2.0.1 2021-02-11 [5] CRAN (R 4.1.2)
ps 1.6.0 2021-02-28 [5] CRAN (R 4.1.2)
purrr 0.3.4 2020-04-17 [5] CRAN (R 4.1.2)
R6 2.5.1 2021-08-19 [5] CRAN (R 4.1.2)
RColorBrewer * 1.1-2 2014-12-07 [5] CRAN (R 4.1.2)
Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.1.2)
readxl * 1.3.1 2019-03-13 [5] CRAN (R 4.1.2)
remotes 2.4.2 2021-11-30 [5] CRAN (R 4.1.2)
rlang 1.0.2 2022-03-04 [1] CRAN (R 4.1.2)
rmarkdown 2.13 2022-03-10 [1] CRAN (R 4.1.2)
rprojroot 2.0.2 2020-11-15 [5] CRAN (R 4.1.2)
sass 0.4.0 2021-05-12 [5] CRAN (R 4.1.2)
scales * 1.1.1 2020-05-11 [5] CRAN (R 4.1.2)
sessioninfo 1.2.2 2021-12-06 [5] CRAN (R 4.1.2)
shape 1.4.6 2021-05-19 [3] CRAN (R 4.1.2)
stringi 1.7.6 2021-11-29 [5] CRAN (R 4.1.2)
stringr * 1.4.0 2019-02-10 [5] CRAN (R 4.1.2)
testthat 3.1.1 2021-12-03 [5] CRAN (R 4.1.2)
tibble 3.1.6 2021-11-07 [5] CRAN (R 4.1.2)
tidyselect 1.1.1 2021-04-30 [5] CRAN (R 4.1.2)
usethis 2.1.3 2021-10-27 [5] CRAN (R 4.1.2)
utf8 1.2.2 2021-07-24 [5] CRAN (R 4.1.2)
vctrs 0.3.8 2021-04-29 [5] CRAN (R 4.1.2)
viridis * 0.6.2 2021-10-13 [5] CRAN (R 4.1.2)
viridisLite * 0.4.0 2021-04-13 [5] CRAN (R 4.1.2)
whisker 0.4 2019-08-28 [5] CRAN (R 4.1.2)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
workflowr 1.7.0 2021-12-21 [1] CRAN (R 4.1.2)
xfun 0.30 2022-03-02 [1] CRAN (R 4.1.2)
yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.2)
[1] /gpfs/homefs/global/home/macnairw/R/x86_64-pc-linux-gnu-library/4.1.2-foss
[2] /apps/rocs/2020.08/cascadelake/software/R-Roche-bundle/2021.12-foss-2020a-R-4.1.2
[3] /apps/rocs/2020.08/cascadelake/software/R-bundle-Bioconductor/3.14-foss-2020a-R-4.1.2
[4] /apps/rocs/2020.08/cascadelake/software/ncdf4/1.18-foss-2020a-R-4.1.2
[5] /apps/rocs/2020.08/cascadelake/software/R/4.1.2-foss-2020a/lib64/R/library
------------------------------------------------------------------------------
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.2 (Ootpa)
Matrix products: default
BLAS/LAPACK: /apps/rocs/2020.08/cascadelake/software/OpenBLAS/0.3.9-GCC-9.3.0/lib/libopenblas_skylakexp-r0.3.9.so
locale:
[1] LC_CTYPE=C 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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.37 readxl_1.3.1 forcats_0.5.1
[4] ggplot2_3.3.5 scales_1.1.1 viridis_0.6.2
[7] viridisLite_0.4.0 assertthat_0.2.1 stringr_1.4.0
[10] data.table_1.14.2 magrittr_2.0.2 circlize_0.4.13
[13] RColorBrewer_1.1-2 BiocStyle_2.22.0 colorout_1.2-2
[16] BiocManager_1.30.16
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 prettyunits_1.1.1 ps_1.6.0
[4] rprojroot_2.0.2 digest_0.6.29 utf8_1.2.2
[7] R6_2.5.1 cellranger_1.1.0 evaluate_0.15
[10] highr_0.9 pillar_1.7.0 GlobalOptions_0.1.2
[13] rlang_1.0.2 callr_3.7.0 whisker_0.4
[16] jquerylib_0.1.4 rmarkdown_2.13 desc_1.4.0
[19] devtools_2.4.3 munsell_0.5.0 compiler_4.1.2
[22] httpuv_1.6.3 xfun_0.30 pkgconfig_2.0.3
[25] pkgbuild_1.2.1 shape_1.4.6 htmltools_0.5.2
[28] tidyselect_1.1.1 tibble_3.1.6 gridExtra_2.3
[31] workflowr_1.7.0 codetools_0.2-18 fansi_1.0.3
[34] crayon_1.5.0 dplyr_1.0.7 withr_2.5.0
[37] later_1.3.0 grid_4.1.2 jsonlite_1.8.0
[40] gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.1
[43] git2r_0.29.0 cli_3.2.0 stringi_1.7.6
[46] cachem_1.0.6 remotes_2.4.2 fs_1.5.1
[49] promises_1.2.0.1 testthat_3.1.1 bslib_0.3.1
[52] ellipsis_0.3.2 generics_0.1.1 vctrs_0.3.8
[55] tools_4.1.2 glue_1.6.2 purrr_0.3.4
[58] pkgload_1.2.4 processx_3.5.2 fastmap_1.1.0
[61] yaml_2.3.5 colorspace_2.0-3 sessioninfo_1.2.2
[64] memoise_2.0.1 usethis_2.1.3 sass_0.4.0