Last updated: 2022-01-07
Checks: 4 3
Knit directory: MS_lesions/
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.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish
to commit the R Markdown file and build the HTML.
The global environment had objects present when the code in the R Markdown file was run. These objects 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. Use wflow_publish
or wflow_build
to ensure that the code is always run in an empty environment.
The following objects were defined in the global environment when these results were created:
Name | Class | Size |
---|---|---|
q | function | 1008 bytes |
The command set.seed(20210118)
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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
To ensure reproducibility of the results, delete the cache directory ms11_paga_cache
and re-run the analysis. To have workflowr automatically delete the cache directory prior to building the file, set delete_cache = TRUE
when running wflow_build()
or wflow_publish()
.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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 5a1c0c2. 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: .DS_Store
Ignored: .Rhistory
Ignored: .Rprofile
Ignored: .Rproj.user/
Ignored: ._MS_lesions.sublime-project
Ignored: ._paga_plot_test.png
Ignored: ._test_heatmap.png
Ignored: .log/
Ignored: MS_lesions.sublime-project
Ignored: MS_lesions.sublime-workspace
Ignored: analysis/.__site.yml
Ignored: analysis/fig_muscat_cache/
Ignored: analysis/ms00_manuscript_figures_cache/
Ignored: analysis/ms02_doublet_id_cache/
Ignored: analysis/ms03_SampleQC_cache/
Ignored: analysis/ms04_conos_cache/
Ignored: analysis/ms05_splitting_cache/
Ignored: analysis/ms06_sccaf_cache/
Ignored: analysis/ms07_soup_cache/
Ignored: analysis/ms08_modules_cache/
Ignored: analysis/ms08_modules_pseudobulk_cache/
Ignored: analysis/ms09_ancombc_cache/
Ignored: analysis/ms09_ancombc_clean_1e3_cache/
Ignored: analysis/ms09_ancombc_clean_2e3_cache/
Ignored: analysis/ms09_ancombc_mixed_cache/
Ignored: analysis/ms10_muscat_run01_cache/
Ignored: analysis/ms10_muscat_run02_cache/
Ignored: analysis/ms10_muscat_template_broad_slim_cache/
Ignored: analysis/ms10_muscat_template_fine_slim_cache/
Ignored: analysis/ms11_paga_cache/
Ignored: analysis/ms11_paga_recalc_cache/
Ignored: analysis/ms11_paga_superclean_cache/
Ignored: analysis/ms12_markers_cache/
Ignored: analysis/ms13_labelling_cache/
Ignored: analysis/ms14_lesions_cache/
Ignored: analysis/ms15_mofa_sample_gm_cache/
Ignored: analysis/ms15_mofa_sample_gm_final_meta_cache/
Ignored: analysis/ms15_mofa_sample_gm_superclean_cache/
Ignored: analysis/ms15_mofa_sample_gm_w_layers_final_meta_cache/
Ignored: analysis/ms15_mofa_sample_wm_cache/
Ignored: analysis/ms15_mofa_sample_wm_final_meta_cache/
Ignored: analysis/ms15_mofa_sample_wm_new_meta_cache/
Ignored: analysis/ms15_mofa_sample_wm_superclean_cache/
Ignored: analysis/ms15_patients_cache/
Ignored: analysis/ms15_patients_gm_cache/
Ignored: analysis/ms15_patients_sample_level_cache/
Ignored: analysis/ms15_patients_w_ms_cache/
Ignored: analysis/supp06_sccaf_cache/
Ignored: analysis/supp07_superclean_check_cache/
Ignored: analysis/supp09_ancombc_cache/
Ignored: analysis/supp09_ancombc_mixed_cache/
Ignored: analysis/supp09_ancombc_rowitch_cache/
Ignored: analysis/supp09_ancombc_superclean_cache/
Ignored: analysis/supp10_muscat_cache/
Ignored: analysis/supp10_muscat_ctrl_gm_vs_wm_cache/
Ignored: analysis/supp10_muscat_gm_layers_effects_cache/
Ignored: analysis/supp10_muscat_gsea_cache/
Ignored: analysis/supp10_muscat_heatmaps_cache/
Ignored: analysis/supp10_muscat_olg_pc1_cache/
Ignored: analysis/supp10_muscat_olg_pc2_cache/
Ignored: analysis/supp10_muscat_olg_pc_cache/
Ignored: analysis/supp10_muscat_regression_cache/
Ignored: analysis/supp10_muscat_soup_cache/
Ignored: analysis/supp10_muscat_soup_mito_cache/
Ignored: code/._ms10_muscat_fns_recover.R
Ignored: code/.recovery/
Ignored: code/jobs/._muscat_run09_2021-10-11.slurm
Ignored: code/muscat_plan.txt
Ignored: data/
Ignored: figures/
Ignored: output/
Ignored: tmp/
Untracked files:
Untracked: analysis/ms11_paga_recalc.Rmd
Untracked: analysis/supp09_ancombc_superclean.Rmd
Untracked: code/dev_dheeraj_oligo_dotplots_2021-12-08.R
Untracked: code/dev_magma_de_heatmaps_2021-12-03.R
Untracked: code/dev_plot_dotplot_sel_genes_2021-12-21.R
Untracked: code/dev_plot_fcs_selected_genes_2021-12-09.R
Untracked: code/jobs/muscat_run20_2021-12-07.slurm
Untracked: paga_plot_test.png
Unstaged changes:
Modified: analysis/fig_muscat.Rmd
Modified: analysis/ms11_paga.Rmd
Modified: analysis/ms13_labelling.Rmd
Modified: analysis/ms14_lesions.Rmd
Modified: analysis/ms15_mofa_sample_wm_superclean.Rmd
Modified: analysis/supp07_superclean_check.Rmd
Modified: code/dev_de_w_contamation_2021-10-25.R
Modified: code/dev_edger_on_mofa_20210804.R
Modified: code/fig_muscat.R
Modified: code/ms04_conos.R
Modified: code/ms11_paga.R
Modified: code/ms11_paga_fns.py
Modified: code/ms14_lesions.R
Modified: code/supp07_superclean.R
Modified: code/supp10_muscat.R
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/ms11_paga.Rmd
) and HTML (public/ms11_paga.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 |
---|---|---|---|---|
Rmd | 93fa77e | wmacnair | 2022-01-05 | Add superclean PAGA analysis |
html | 93fa77e | wmacnair | 2022-01-05 | Add superclean PAGA analysis |
Rmd | ff2b8fb | wmacnair | 2021-12-15 | Update PAGA analysis, including colouring with ANCOM |
html | ff2b8fb | wmacnair | 2021-12-15 | Update PAGA analysis, including colouring with ANCOM |
html | 7fb1b95 | wmacnair | 2021-11-25 | Host with GitLab. |
Rmd | 58205c2 | Macnair | 2021-05-21 | Update with random effects and markers |
Rmd | eef8a1c | Macnair | 2021-04-29 | Minor tweaks to allow rerunning on Roche servers |
Rmd | 129c53d | Macnair | 2021-04-16 | Renamed a lot of things to add ms07_soup |
source('code/ms00_utils.R')
source('code/ms09_ancombc_mixed.R')
source('code/ms11_paga.R')
use_condaenv('scanpy', required=TRUE)
source_python('code/ms11_paga_fns.py')
# define inputs
graph_f = 'output/ms04_conos/conos_graph_2021-02-11.txt'
# define run
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
meta_f = "data/metadata/metadata_checked_assumptions_2021-10-08.xlsx"
save_dir = 'output/ms11_paga'
if (!dir.exists(save_dir))
dir.create(save_dir)
date_tag = '2022-01-07'
seed = 20220107
# do in parallel
n_cores = 8
# identifying strange samples
neuro_mad_cut = 2
log_n_mad_cut = 3
# where to save files with subgraphs?
subg_pat = sprintf('%s/subgraph_%s_%s.txt.gz', save_dir, '%s', date_tag)
# define subsets to run
subset_list = list(
all = list(),
healthy_WM = list(lesion_type = c('WM')),
MS_WM = list(lesion_type = c('NAWM', 'AL', 'CAL', 'CIL', 'RL')),
healthy_GM = list(lesion_type = c('GM')),
MS_GM = list(lesion_type = c('NAGM', 'GML'))
)
lesion_list = lapply(lesion_ord, function(l)
list(lesion_type = l)) %>%
setNames(paste0('lesion_', lesion_ord)
)
xy_ref_list = c(rep('WM', 6), rep('GM', 3)) %>%
paste0('healthy_', .) %>% setNames(names(lesion_list))
# what to look at when comparing edges across samples?
sel_broad = c('Oligodendrocytes', 'OPCs / COPs')
assert_that(all(sel_broad %in% broad_ord))
[1] TRUE
# define ancom outputs to use
ancom_dir = 'output/ms09_ancombc'
ancom_stamp = '2021-11-12'
ancom_tags = c(WM = "lesions_WM", GM = "lesions_GM_4pcs")
ancom_fs = sapply(ancom_tags, function(t)
sprintf('%s/ancombc_bootstrap_%s_%s.txt.gz', ancom_dir, t, ancom_stamp)) %>%
setNames(names(ancom_tags))
meta_dt = load_meta_dt_from_xls(meta_f)
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f) %>%
merge(meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = neuro_mad_cut)
# check for any outliers
size_chks = calc_size_outliers(conos_dt, mad_cut = log_n_mad_cut)
message("these samples excluded to outlier sample sizes:")
these samples excluded to outlier sample sizes:
print(size_chks[ size_ok == FALSE ])
matter sample_id N med_log_N mad_log_N size_ok
1: GM EU034 911 8.519391 0.423111 FALSE
2: GM EU043 687 8.519391 0.423111 FALSE
# exclude them from conos
ok_samples = size_chks[ size_ok == TRUE]$sample_id
conos_dt = conos_dt[ (sample_id %in% ok_samples) & (neuro_ok == TRUE) ]
# make list of all samples
samples = unique(conos_dt$sample_id)
sample_list = lapply(samples, function(s) list(sample_id = s)) %>%
setNames(paste0('sample_', samples))
conos_tidy = conos_dt[, .(sample_id, matter, lesion_type, cell_id, type_broad, type_fine)]
# fwrite(conos_tidy, file = sprintf('output/ms11_paga/conos_tidy_%s.txt.gz', date_tag)
ancom_ls = ancom_fs %>% lapply(function(f) fread(f) %>%
.calc_boots_sum(min_effect = 0.1, q_cut = 0.05, signif_cut = 0.8)) %>%
setNames(names(ancom_fs))
# make subgraphs for each specification
set.seed(seed)
calc_subgraphs(conos_tidy, subset_list, graph_f, subg_pat, n_sample = 1e5)
already done!
NULL
calc_subgraphs(conos_tidy, lesion_list, graph_f, subg_pat, n_sample = 1e5)
already done!
NULL
# calc_subgraphs(conos_tidy, sample_list, graph_f, subg_pat, n_sample = 0)
# # run on each sample
# if (!check_all_done(sample_list, save_dir, date_tag)) {
# bpparam = MulticoreParam(workers = n_cores)
# bpstop()
# bpstart()
# ignore_me = bplapply(seq_along(sample_list), function(i) {
# spec_n = names(sample_list)[[i]]
# subg_f = sprintf(subg_pat, spec_n)
# run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_tidy)),
# subg_f, group_var = 'type_fine')
# }, BPPARAM = bpparam)
# bpstop()
# }
# run on each lesion type
if (!check_all_done(lesion_list, save_dir, date_tag)) {
n_lesions = length(lesion_list)
bpparam = MulticoreParam(workers = min(n_lesions, n_cores))
bpstop()
bpstart()
bplapply(seq_along(lesion_list), function(i) {
spec_n = names(lesion_list)[[i]]
subg_f = sprintf(subg_pat, spec_n)
run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_tidy)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}
# run on each subset
if (!check_all_done(subset_list, save_dir, date_tag)) {
bpparam = MulticoreParam(workers = length(subset_list))
bpstop()
bpstart()
bplapply(seq_along(subset_list), function(i) {
# define things
spec_n = names(subset_list)[[i]]
subg_f = sprintf(subg_pat, spec_n)
# run paga
run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_tidy)),
subg_f, group_var = 'type_fine')
}, BPPARAM = bpparam)
bpstop()
}
edges_all = calc_edges_over_samples(sample_list, date_tag,
sel_broad, labels_dt)
edges_all = merge(edges_all, meta_dt, by = 'sample_id')
PAGA
on all celltypesfor (nn in names(subset_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'all',
conos_dt, subset_list[[nn]], labels_dt, xy_ref = nn))})
cat('\n\n')
}
for (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'all',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref = nn))})
cat('\n\n')
}
PAGA
on oligo + OPC compartmentfor (nn in names(subset_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'olg',
conos_dt, subset_list[[nn]], labels_dt, xy_ref = nn))})
cat('\n\n')
}
for (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
suppressWarnings({print(plot_paga_outputs(save_dir, nn, date_tag, 'olg',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref = nn))})
cat('\n\n')
}
ANCOM
coefficientsfor (nn in names(lesion_list)) {
cat('#### ', nn, '\n')
ctrl_sample = xy_ref_list[[nn]] %>% str_match("(lesion|healthy)_(.+)") %>% .[3]
ancom_dt = ancom_ls[[ ctrl_sample ]]
suppressWarnings({print(plot_paga_w_ancom(save_dir, nn, date_tag, 'olg',
conos_dt, lesion_list[[nn]], labels_dt, xy_ref = "all",
ancom_dt, max_fc = 8))})
cat('\n\n')
}
PAGA
edges across samples, within oligo + OPC compartmentcat('### Data-driven')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'data'))
cat('\n')
cat('### Data-driven, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'data'))
cat('\n')
cat('### Data-driven, WM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'WM'],
split = 'data'))
cat('\n')
cat('### Data-driven, GM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'GM'],
split = 'data'))
cat('\n')
cat('### Split by lesion')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'lesion_type'))
cat('\n')
cat('### Split by lesion, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'lesion_type'))
cat('\n')
devtools::session_info()
- Session info ---------------------------------------------------------------
setting value
version R version 4.0.5 (2021-03-31)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype C
tz Europe/Zurich
date 2022-01-07
- Packages -------------------------------------------------------------------
package * version date lib source
ade4 1.7-18 2021-09-16 [1] CRAN (R 4.0.5)
ANCOMBC * 1.0.5 2021-03-09 [1] Bioconductor
ape 5.5 2021-04-25 [1] CRAN (R 4.0.3)
assertthat * 0.2.1 2019-03-21 [2] CRAN (R 4.0.0)
beeswarm 0.4.0 2021-06-01 [1] CRAN (R 4.0.3)
Biobase 2.50.0 2020-10-27 [1] Bioconductor
BiocGenerics 0.36.1 2021-04-16 [1] Bioconductor
BiocManager 1.30.16 2021-06-15 [1] CRAN (R 4.0.3)
BiocParallel * 1.24.1 2020-11-06 [1] Bioconductor
BiocStyle * 2.18.1 2020-11-24 [1] Bioconductor
biomformat 1.18.0 2020-10-27 [1] Bioconductor
Biostrings 2.58.0 2020-10-27 [1] Bioconductor
bslib 0.3.1 2021-10-06 [2] CRAN (R 4.0.5)
cachem 1.0.6 2021-08-19 [1] CRAN (R 4.0.5)
Cairo 1.5-12.2 2020-07-07 [2] CRAN (R 4.0.2)
callr 3.7.0 2021-04-20 [2] CRAN (R 4.0.3)
cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.0.0)
circlize * 0.4.13 2021-06-09 [1] CRAN (R 4.0.3)
cli 3.0.1 2021-07-17 [1] CRAN (R 4.0.3)
clue 0.3-60 2021-10-11 [1] CRAN (R 4.0.5)
cluster 2.1.2 2021-04-17 [2] CRAN (R 4.0.3)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.0.3)
colorout * 1.2-2 2021-04-15 [1] Github (jalvesaq/colorout@79931fd)
colorspace 2.0-2 2021-06-24 [1] CRAN (R 4.0.3)
ComplexHeatmap * 2.6.2 2020-11-12 [1] Bioconductor
crayon 1.4.1 2021-02-08 [2] CRAN (R 4.0.3)
data.table * 1.14.2 2021-09-27 [2] CRAN (R 4.0.5)
DBI 1.1.1 2021-01-15 [2] CRAN (R 4.0.3)
desc 1.4.0 2021-09-28 [1] CRAN (R 4.0.5)
devtools 2.4.2 2021-06-07 [1] CRAN (R 4.0.3)
digest 0.6.28 2021-09-23 [2] CRAN (R 4.0.5)
dplyr 1.0.7 2021-06-18 [2] CRAN (R 4.0.3)
ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.0.3)
evaluate 0.14 2019-05-28 [2] CRAN (R 4.0.0)
fansi 0.5.0 2021-05-25 [2] CRAN (R 4.0.3)
farver 2.1.0 2021-02-28 [2] CRAN (R 4.0.3)
fastmap 1.1.0 2021-01-25 [2] CRAN (R 4.0.3)
forcats * 0.5.1 2021-01-27 [2] CRAN (R 4.0.3)
foreach 1.5.1 2020-10-15 [2] CRAN (R 4.0.3)
fs 1.5.0 2020-07-31 [2] CRAN (R 4.0.2)
generics 0.1.1 2021-10-25 [2] CRAN (R 4.0.5)
GetoptLong 1.0.5 2020-12-15 [1] CRAN (R 4.0.3)
ggbeeswarm * 0.6.0 2017-08-07 [1] CRAN (R 4.0.3)
ggplot2 * 3.3.5 2021-06-25 [1] CRAN (R 4.0.3)
ggrepel * 0.9.1 2021-01-15 [2] CRAN (R 4.0.3)
git2r 0.28.0 2021-01-10 [1] CRAN (R 4.0.3)
GlobalOptions 0.1.2 2020-06-10 [1] CRAN (R 4.0.3)
glue 1.4.2 2020-08-27 [2] CRAN (R 4.0.3)
gridExtra 2.3 2017-09-09 [2] CRAN (R 4.0.0)
gtable 0.3.0 2019-03-25 [2] CRAN (R 4.0.0)
here 1.0.1 2020-12-13 [2] CRAN (R 4.0.5)
highr 0.9 2021-04-16 [2] CRAN (R 4.0.3)
htmltools 0.5.2 2021-08-25 [2] CRAN (R 4.0.5)
httpuv 1.6.3 2021-09-09 [2] CRAN (R 4.0.5)
ica * 1.0-2 2018-05-24 [2] CRAN (R 4.0.0)
igraph 1.2.7 2021-10-15 [2] CRAN (R 4.0.5)
IRanges 2.24.1 2020-12-12 [1] Bioconductor
iterators 1.0.13 2020-10-15 [2] CRAN (R 4.0.3)
janitor 2.1.0 2021-01-05 [1] CRAN (R 4.0.3)
jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.0.3)
jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.0.3)
knitr 1.36 2021-09-29 [1] CRAN (R 4.0.5)
labeling 0.4.2 2020-10-20 [2] CRAN (R 4.0.3)
later 1.3.0 2021-08-18 [2] CRAN (R 4.0.5)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.0.5)
lifecycle 1.0.1 2021-09-24 [2] CRAN (R 4.0.5)
lubridate 1.8.0 2021-10-07 [2] CRAN (R 4.0.5)
magrittr * 2.0.1 2020-11-17 [1] CRAN (R 4.0.3)
MASS * 7.3-54 2021-05-03 [2] CRAN (R 4.0.3)
Matrix 1.3-4 2021-06-01 [2] CRAN (R 4.0.3)
matrixStats 0.61.0 2021-09-17 [1] CRAN (R 4.0.5)
memoise 2.0.0 2021-01-26 [1] CRAN (R 4.0.3)
mgcv 1.8-38 2021-10-06 [1] CRAN (R 4.0.5)
microbiome 1.12.0 2020-10-27 [1] Bioconductor
multtest 2.46.0 2020-10-27 [1] Bioconductor
munsell 0.5.0 2018-06-12 [2] CRAN (R 4.0.0)
nlme 3.1-153 2021-09-07 [2] CRAN (R 4.0.5)
nloptr 1.2.2.2 2020-07-02 [1] CRAN (R 4.0.3)
patchwork * 1.1.1 2020-12-17 [2] CRAN (R 4.0.3)
permute 0.9-5 2019-03-12 [1] CRAN (R 4.0.3)
phyloseq * 1.34.0 2020-10-27 [1] Bioconductor
pillar 1.6.4 2021-10-18 [1] CRAN (R 4.0.5)
pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.0.3)
pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.0.0)
pkgload 1.2.3 2021-10-13 [2] CRAN (R 4.0.5)
plyr 1.8.6 2020-03-03 [2] CRAN (R 4.0.0)
png 0.1-7 2013-12-03 [2] CRAN (R 4.0.0)
prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.0.0)
processx 3.5.2 2021-04-30 [2] CRAN (R 4.0.3)
promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.0.3)
ps 1.6.0 2021-02-28 [2] CRAN (R 4.0.3)
purrr * 0.3.4 2020-04-17 [2] CRAN (R 4.0.0)
R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.0.3)
R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.0.3)
R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.0.5)
R6 2.5.1 2021-08-19 [2] CRAN (R 4.0.5)
rappdirs 0.3.3 2021-01-31 [2] CRAN (R 4.0.3)
rbibutils 2.2.4 2021-10-11 [1] CRAN (R 4.0.5)
RColorBrewer * 1.1-2 2014-12-07 [2] CRAN (R 4.0.0)
Rcpp 1.0.7 2021-07-07 [1] CRAN (R 4.0.3)
Rdpack 2.1.2 2021-06-01 [1] CRAN (R 4.0.3)
readxl * 1.3.1 2019-03-13 [2] CRAN (R 4.0.0)
remotes 2.4.1 2021-09-29 [1] CRAN (R 4.0.5)
reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.0.0)
reticulate * 1.22 2021-09-17 [2] CRAN (R 4.0.5)
rhdf5 2.34.0 2020-10-27 [1] Bioconductor
rhdf5filters 1.2.1 2021-05-03 [1] Bioconductor
Rhdf5lib 1.12.1 2021-01-26 [1] Bioconductor
rjson 0.2.20 2018-06-08 [1] CRAN (R 4.0.3)
rlang 0.4.12 2021-10-18 [2] CRAN (R 4.0.5)
rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.0.5)
rprojroot 2.0.2 2020-11-15 [2] CRAN (R 4.0.3)
Rtsne 0.15 2018-11-10 [2] CRAN (R 4.0.0)
S4Vectors 0.28.1 2020-12-09 [1] Bioconductor
sass 0.4.0 2021-05-12 [2] CRAN (R 4.0.3)
scales * 1.1.1 2020-05-11 [2] CRAN (R 4.0.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.3)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.0.1)
snakecase 0.11.0 2019-05-25 [1] CRAN (R 4.0.3)
stringi 1.7.4 2021-08-25 [1] CRAN (R 4.0.5)
stringr * 1.4.0 2019-02-10 [2] CRAN (R 4.0.0)
survival 3.2-13 2021-08-24 [2] CRAN (R 4.0.5)
testthat 3.1.0 2021-10-04 [2] CRAN (R 4.0.5)
tibble 3.1.5 2021-09-30 [1] CRAN (R 4.0.5)
tidyr 1.1.4 2021-09-27 [2] CRAN (R 4.0.5)
tidyselect 1.1.1 2021-04-30 [2] CRAN (R 4.0.3)
usethis 2.1.2 2021-10-25 [1] CRAN (R 4.0.5)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.0.3)
vctrs 0.3.8 2021-04-29 [2] CRAN (R 4.0.3)
vegan 2.5-7 2020-11-28 [1] CRAN (R 4.0.3)
vipor 0.4.5 2017-03-22 [1] CRAN (R 4.0.3)
viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.0.5)
viridisLite * 0.4.0 2021-04-13 [1] CRAN (R 4.0.1)
whisker 0.4 2019-08-28 [1] CRAN (R 4.0.3)
withr 2.4.2 2021-04-18 [2] CRAN (R 4.0.3)
workflowr * 1.6.2 2020-04-30 [1] CRAN (R 4.0.3)
xfun 0.27 2021-10-18 [1] CRAN (R 4.0.5)
XVector 0.30.0 2020-10-27 [1] Bioconductor
yaml 2.2.1 2020-02-01 [2] CRAN (R 4.0.3)
zlibbioc 1.36.0 2020-10-27 [1] Bioconductor
[1] /pstore/home/macnairw/lib/conda_r3.12
[2] /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/R/library
sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/libopenblasp-r0.3.12.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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] BiocParallel_1.24.1 ComplexHeatmap_2.6.2 ggbeeswarm_0.6.0
[4] ggrepel_0.9.1 reticulate_1.22 MASS_7.3-54
[7] phyloseq_1.34.0 ANCOMBC_1.0.5 ica_1.0-2
[10] purrr_0.3.4 patchwork_1.1.1 readxl_1.3.1
[13] forcats_0.5.1 ggplot2_3.3.5 scales_1.1.1
[16] viridis_0.6.2 viridisLite_0.4.0 assertthat_0.2.1
[19] stringr_1.4.0 data.table_1.14.2 magrittr_2.0.1
[22] circlize_0.4.13 RColorBrewer_1.1-2 BiocStyle_2.18.1
[25] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] plyr_1.8.6 igraph_1.2.7 splines_4.0.5
[4] usethis_2.1.2 digest_0.6.28 foreach_1.5.1
[7] htmltools_0.5.2 fansi_0.5.0 memoise_2.0.0
[10] cluster_2.1.2 remotes_2.4.1 Biostrings_2.58.0
[13] matrixStats_0.61.0 R.utils_2.11.0 prettyunits_1.1.1
[16] colorspace_2.0-2 rappdirs_0.3.3 rbibutils_2.2.4
[19] xfun_0.27 dplyr_1.0.7 callr_3.7.0
[22] crayon_1.4.1 jsonlite_1.7.2 survival_3.2-13
[25] iterators_1.0.13 ape_5.5 glue_1.4.2
[28] gtable_0.3.0 zlibbioc_1.36.0 XVector_0.30.0
[31] GetoptLong_1.0.5 pkgbuild_1.2.0 Rhdf5lib_1.12.1
[34] shape_1.4.6 BiocGenerics_0.36.1 DBI_1.1.1
[37] Rcpp_1.0.7 clue_0.3-60 stats4_4.0.5
[40] ellipsis_0.3.2 pkgconfig_2.0.3 R.methodsS3_1.8.1
[43] farver_2.1.0 sass_0.4.0 utf8_1.2.2
[46] janitor_2.1.0 here_1.0.1 tidyselect_1.1.1
[49] labeling_0.4.2 rlang_0.4.12 reshape2_1.4.4
[52] later_1.3.0 munsell_0.5.0 cellranger_1.1.0
[55] tools_4.0.5 cachem_1.0.6 cli_3.0.1
[58] generics_0.1.1 ade4_1.7-18 devtools_2.4.2
[61] evaluate_0.14 biomformat_1.18.0 fastmap_1.1.0
[64] yaml_2.2.1 processx_3.5.2 knitr_1.36
[67] fs_1.5.0 nlme_3.1-153 whisker_0.4
[70] R.oo_1.24.0 compiler_4.0.5 beeswarm_0.4.0
[73] png_0.1-7 testthat_3.1.0 tibble_3.1.5
[76] bslib_0.3.1 stringi_1.7.4 ps_1.6.0
[79] highr_0.9 desc_1.4.0 lattice_0.20-45
[82] Matrix_1.3-4 nloptr_1.2.2.2 vegan_2.5-7
[85] microbiome_1.12.0 permute_0.9-5 multtest_2.46.0
[88] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[91] rhdf5filters_1.2.1 BiocManager_1.30.16 Rdpack_2.1.2
[94] jquerylib_0.1.4 GlobalOptions_0.1.2 httpuv_1.6.3
[97] R6_2.5.1 promises_1.2.0.1 gridExtra_2.3
[100] vipor_0.4.5 IRanges_2.24.1 sessioninfo_1.1.1
[103] codetools_0.2-18 pkgload_1.2.3 rhdf5_2.34.0
[106] rprojroot_2.0.2 rjson_0.2.20 withr_2.4.2
[109] S4Vectors_0.28.1 mgcv_1.8-38 parallel_4.0.5
[112] tidyr_1.1.4 rmarkdown_2.11 snakecase_0.11.0
[115] Cairo_1.5-12.2 Rtsne_0.15 git2r_0.28.0
[118] Biobase_2.50.0 lubridate_1.8.0