ANCOM-BC clean - cells with >= 1000 features only
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandJune 04, 2021
Last updated: 2021-06-04
Checks: 4 3
Knit directory: MS_lesions/
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Notes
I’ve done a range of runs of ANCOM-BC with slightly different parameters, to see which works best. As usual, there’s not so much difference between them (which is good). Here’s a quick description of the runs:
- lesions_WM_mad: Testing lesions vs healthy, WM only, samples with unusually high neuronal propn excluded.
- lesions_WM_big_mad: Testing lesions vs healthy, WM only, samples with unusually high neuronal propn excluded, celltypes with very few cells excluded.
- lesions_GM_mad: Testing lesions vs healthy, GM only, samples with unusually low neuronal propn excluded (actually only excludes one sample).
- lesions_GM_big_mad: Testing lesions vs healthy, GM only, samples with unusually low neuronal propn excluded, celltypes with very few cells excluded.
- lesions_WM_no_neuro: Testing lesions vs healthy, WM only, samples with unusually high neuronal propn excluded, restricted to non-neuronal celltypes only.
- lesions_GM_neuro: Testing lesions vs healthy, GM only, samples with unusually low neuronal propn excluded, restricted to ONLY neuronal celltypes.
- lesions_WM_all: Testing lesions vs healthy, WM only, all samples included.
- lesions_GM_all: Testing lesions vs healthy, GM only, all samples included.
- GM_vs_WM: Testing healthy GM vs healthy WM. This doesn’t work so well with
ANCOM-BC, as it looks for a number of unchanging celltypes to use as a reference, and in this comparison it’s only OPCs that could plausibly not change between them. - lesions_WM_oligos: Testing lesions vs healthy, WM only, oligos + OPCs only, samples with unusually high neuronal propn excluded.
- lesions_GM_oligos: Testing lesions vs healthy, GM only, oligos + OPCs only, samples with unusually high neuronal propn excluded.
- GM_vs_WM_oligos: Testing healthy GM vs healthy WM, WM only, oligos + OPCs only.
The same samples + celltypes are used in running scCODA, but in scCODA you have to manually specify a reference celltype that you assume doesn’t change. For this, I’ve used one broad celltype for each run, as follows:
- lesions_WM_mad: Excitatory neurons
- lesions_WM_big_mad: Excitatory neurons
- lesions_GM_mad: Excitatory neurons
- lesions_GM_big_mad: Excitatory neurons
- lesions_WM_no_neuro: Astrocytes
- lesions_GM_neuro: Excitatory neurons
- lesions_WM_all: Excitatory neurons
- lesions_GM_all: Excitatory neurons
- GM_vs_WM: OPCs / COPs
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms04_conos.R')
source('code/ms09_ancombc.R')
use_condaenv('sccoda', required=TRUE)
source_python('code/ms09_scCODA_fns.py')Inputs
# 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_updated_20201127.txt'
byhand_f = paste0('data/byhand_markers/Copy of Copy of ',
'Marker_selection_for_validation_MS_snucseq_30102020 ',
'Ediinburgh.xlsx - final markers for Cartana panel.csv')
qc_dir = 'output/ms03_SampleQC'
qc_f = file.path(qc_dir, 'ms_qc_dt.txt')Outputs
# where to save?
save_dir = 'output/ms09_ancombc'
date_tag = '2021-06-03'
if (!dir.exists(save_dir))
dir.create(save_dir)
# strange samples
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'source', 'patient_id',
'sex', 'age_norm', 'pmi_cat')
mad_cut = 2
feat_cut = 1e3
# define models
subset_list = list(
lesions_WM_mad = list(
subset_spec = list(matter = 'WM', neuro_ok = TRUE)
),
lesions_WM_big_mad = list(
subset_spec = list(matter = 'WM', neuro_ok = TRUE),
size_spec = list(min_count = 10, min_prop = 0.1)),
lesions_GM_mad = list(
subset_spec = list(matter = 'GM', neuro_ok = TRUE)
),
lesions_GM_big_mad = list(
subset_spec = list(matter = 'GM', neuro_ok = TRUE),
size_spec = list(min_count = 10, min_prop = 0.1)),
lesions_WM_no_neuro = list(
subset_spec = list(matter = 'WM', neuro_ok = TRUE),
type_spec = setdiff(broad_ord,
c('Excitatory neurons', 'Inhibitory neurons'))
),
lesions_GM_neuro = list(
subset_spec = list(matter = 'GM', neuro_ok = TRUE),
type_spec = c('Excitatory neurons', 'Inhibitory neurons')
),
lesions_WM_all = list(
subset_spec = list(matter = 'WM')
),
lesions_GM_all = list(
subset_spec = list(matter = 'GM')
),
GM_vs_WM = list(
subset_spec = list(lesion_type = c('GM', 'WM'), neuro_ok = TRUE)
),
lesions_WM_oligos = list(
subset_spec = list(matter = 'WM', neuro_ok = TRUE),
type_spec = c('Oligodendrocytes', 'OPCs / COPs')
),
lesions_GM_oligos = list(
subset_spec = list(matter = 'GM', neuro_ok = TRUE),
type_spec = c('Oligodendrocytes', 'OPCs / COPs')
),
GM_vs_WM_oligos = list(
subset_spec = list(lesion_type = c('GM', 'WM'), neuro_ok = TRUE),
type_spec = c('Oligodendrocytes', 'OPCs / COPs')
)
)
model_names = names(subset_list)
formulae = list(
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ matter + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ lesion_type + sex + age_norm',
'~ matter + sex + age_norm'
) %>% setNames(model_names)
names_list = list(
'Lesion types (WM, low neurons)',
'Lesion types (WM, low neurons, large celltypes)',
'Lesion types (GM, high neurons)',
'Lesion types (GM, high neurons, large celltypes)',
'Lesion types (WM, neurons excluded)',
'Lesion types (GM, neurons only)',
'Lesion types (WM, all)',
'Lesion types (GM, all)',
'GM vs WM',
'Lesion types (WM, oligos + opcs)',
'Lesion types (GM, oligos + opcs)',
'GM vs WM (oligos + opcs)'
) %>% setNames(model_names)
whatplot_list = list(
'lesion_type',
'lesion_type',
'lesion_type',
'lesion_type',
'lesion_type',
'lesion_type',
'lesion_type',
'lesion_type',
'matter',
'lesion_type',
'lesion_type',
'matter'
) %>% setNames(model_names)
ref_type_list = list(
c(`Excitatory neurons` = 'excit-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`Astrocytes` = 'astro-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`Excitatory neurons` = 'excit-ref'),
c(`OPCs / COPs` = 'opc_cop-ref'),
c(`OPCs / COPs` = 'opc_cop-ref'),
c(`OPCs / COPs` = 'opc_cop-ref'),
c(`OPCs / COPs` = 'opc_cop-ref')
) %>% setNames(model_names)
p_cut = 0.05
# define filenames
ancom_pat = sprintf('%s/ancom_obj_clean_1e3_%s_%s.rds', save_dir, date_tag, '%s')
# define sccoda setup
n_results = 20000L
n_burnin = 5000L
coda_pat = sprintf('%s/sccoda_obj_clean_1e3_%s_%s.p', save_dir, date_tag, '%s')Load inputs
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f)meta_dt = load_meta_dt(meta_f)Processing / calculations
conos_dt = merge(conos_dt, meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = mad_cut)qc_dt = qc_f %>% fread %>%
.[, .(cell_id, log_feats = log10(all_feats),
log_counts = log10(all_counts))] %>%
.[, feat_ok := log_feats >= log10(feat_cut)]
feat_ok_ids = qc_dt[feat_ok == TRUE]$cell_id
conos_dt = conos_dt[cell_id %in% feat_ok_ids]props_dt = calc_props_dt(conos_dt, sample_vars)counts_wide = calc_counts_wide(props_dt, sample_vars)pca_list = calc_pca(props_dt, by_what = 'sample')
pca_wm = calc_pca(props_dt[matter == 'WM' & neuro_ok == TRUE], by_what = 'sample')
pca_gm = calc_pca(props_dt[matter == 'GM' & neuro_ok == TRUE], by_what = 'sample')# do fitting
n_models = length(model_names)
bpparam = MulticoreParam(workers = n_models)
ancom_list = bplapply(seq.int(n_models),
function(i) {
nn = model_names[[i]]
if (!is.null(subset_list[[nn]]$type_spec)) {
conos_tmp = conos_dt[type_broad %in% subset_list[[nn]]$type_spec]
} else {
conos_tmp = copy(conos_dt)
}
props_tmp = calc_props_dt(conos_tmp, sample_vars)
wide_tmp = calc_counts_wide(props_tmp, sample_vars)
fit_ancombc_fn(ancom_pat, nn, wide_tmp, subset_list[[nn]]$subset_spec,
subset_list[[nn]]$size_spec, formulae[[nn]], sample_vars, seed = i)
}, BPPARAM = bpparam) %>% setNames(model_names)
bpstop()# do fitting
coda_list = lapply(seq.int(n_models),
function(i) {
nn = model_names[[i]]
if (!is.null(subset_list[[nn]]$type_spec)) {
conos_tmp = conos_dt[type_broad %in% subset_list[[nn]]$type_spec]
} else {
conos_tmp = copy(conos_dt)
}
# fit coda
coda_obj = run_sccoda(coda_pat, nn,
conos_tmp, sample_vars, ref_type_list[[nn]],
subset_list[[nn]]$subset_spec, subset_list[[nn]]$size_spec,
formulae[[nn]], num_results = n_results, num_burnin = n_burnin)
}) %>% setNames(model_names)Analysis
Check for outliers
Proportion of neurons by sample
(plot_neuro_prop(props_dt, mad_cut))
PCA of celltype proportions
for (what in c('all', 'WM', 'GM')) {
cat('#### ', what, '\n')
if (what == 'all') {
print(plot_pca(pca_list))
} else if (what == 'WM') {
print(plot_pca(pca_wm))
} else if (what == 'GM') {
print(plot_pca(pca_gm))
}
cat('\n\n')
}all
WM
GM
Cluster samples by celltype proportions
for (what in c('all', 'WM', 'GM')) {
cat('### ', what, '\n')
if (what == 'all') {
draw(plot_clr_heatmap(props_dt), row_dend_width = unit(1, "in"))
} else if (what == 'WM') {
draw(plot_clr_heatmap(props_dt[matter == 'WM' & neuro_ok == TRUE]),
row_dend_width = unit(1, "in"))
} else if (what == 'GM') {
draw(plot_clr_heatmap(props_dt[matter == 'GM' & neuro_ok == TRUE]),
row_dend_width = unit(1, "in"))
}
cat('\n\n')
}all
WM
GM
Barplot of celltype proportions split by sample
types_list = list(
oligo_opc = c('OPCs / COPs', 'Oligodendrocytes'),
neurons = c('Excitatory neurons', 'Inhibitory neurons'),
micro_immune = c('Microglia', 'Immune'),
astro_opc = c('OPCs / COPs', 'Astrocytes'),
endo_peri = c('Endothelial cells', 'Pericytes')
)
for (t in names(types_list)) {
for (m in c('WM', 'GM')) {
cat('### ', t, ', ', m, '\n')
types = types_list[[t]]
print(plot_sample_splits(conos_dt[matter == m], types = types))
cat('\n\n')
}
}oligo_opc , WM
oligo_opc , GM
neurons , WM
neurons , GM
micro_immune , WM
micro_immune , GM
astro_opc , WM
astro_opc , GM
endo_peri , WM
endo_peri , GM
CLR plots of celltype proportions
These plots show the broad variability between sample compositions, viewed at the log scale. A couple of notes:
- The aspect ratio of the plot reflects how much variance each principal component accounts for.
- The arrows show the directions in the plot corresponding to strongest positive change in celltype proportion.
- Patients with more than one sample are labelled by colour; patients with only one are labelled by text on the plot.
for (t in names(types_list)) {
for (m in c('WM', 'GM')) {
cat('### ', t, ', ', m, '\n')
types = types_list[[t]]
input_dt = conos_dt[(matter == m) & (type_broad %in% types)]
suppressWarnings(print(plot_sample_clrs(input_dt)))
cat('\n\n')
}
}oligo_opc , WM
oligo_opc , GM
neurons , WM
neurons , GM
micro_immune , WM
micro_immune , GM
astro_opc , WM
astro_opc , GM
endo_peri , WM
endo_peri , GM
CLR plots of celltype proportions, neuron proportion outliers excluded
for (t in names(types_list)) {
for (m in c('WM', 'GM')) {
cat('### ', t, ', ', m, '\n')
types = types_list[[t]]
input_dt = conos_dt[(matter == m) & (neuro_ok == TRUE) &
type_broad %in% types]
suppressWarnings(print(plot_sample_clrs(input_dt)))
cat('\n\n')
}
}oligo_opc , WM
oligo_opc , GM
neurons , WM
neurons , GM
micro_immune , WM
micro_immune , GM
astro_opc , WM
astro_opc , GM
endo_peri , WM
endo_peri , GM
ANCOM CIs (signif only)
for (nn in model_names) {
cat('### ', nn, '\n')
print(plot_ancombc_ci(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = whatplot_list[[nn]], reported_only = TRUE))
cat('\n\n')
}lesions_WM_mad
lesions_WM_big_mad
lesions_GM_mad
lesions_GM_big_mad
lesions_WM_no_neuro
lesions_GM_neuro
lesions_WM_all
lesions_GM_all
GM_vs_WM
lesions_WM_oligos
lesions_GM_oligos
GM_vs_WM_oligos
scCODA CIs (signif only)
for (nn in model_names) {
# extract this model
cat('### ', nn, '\n')
print(plot_sccoda_ci(coda_list[[nn]], reported_only = TRUE))
cat('\n\n')
}lesions_WM_mad
lesions_WM_big_mad
lesions_GM_mad
lesions_GM_big_mad
lesions_WM_no_neuro
lesions_GM_neuro
lesions_WM_all
lesions_GM_all
GM_vs_WM
lesions_WM_oligos
lesions_GM_oligos
GM_vs_WM_oligos
ANCOM-BC vs scCODA
for (nn in model_names) {
# extract this model
cat('### ', nn, '{.tabset}\n')
print(plot_ancom_vs_sccoda(ancom_list[[nn]], coda_list[[nn]], labels_dt))
cat('\n\n')
}lesions_WM_mad
lesions_WM_big_mad
lesions_GM_mad
lesions_GM_big_mad
lesions_WM_no_neuro
lesions_GM_neuro
lesions_WM_all
lesions_GM_all
GM_vs_WM
lesions_WM_oligos
lesions_GM_oligos
GM_vs_WM_oligos
ANCOM CIs (all)
for (nn in model_names) {
# extract this model
cat('### ', nn, '\n')
print(plot_ancombc_ci(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = whatplot_list[[nn]], reported_only = FALSE))
cat('\n\n')
}lesions_WM_mad
lesions_WM_big_mad
lesions_GM_mad
lesions_GM_big_mad
lesions_WM_no_neuro
lesions_GM_neuro
lesions_WM_all
lesions_GM_all
GM_vs_WM
lesions_WM_oligos
lesions_GM_oligos
GM_vs_WM_oligos
Raw counts across conditions
for (nn in model_names) {
# extract this model
cat('### ', nn, '\n')
print(plot_raw_counts(conos_dt, subset_list[[nn]]$type_spec,
subset_list[[nn]]$subset_spec, subset_list[[nn]]$size_spec,
what = 'boxplot', ancom_list[[nn]]))
cat('\n\n')
}lesions_WM_mad
lesions_WM_big_mad
lesions_GM_mad
lesions_GM_big_mad
lesions_WM_no_neuro
lesions_GM_neuro
lesions_WM_all
lesions_GM_all
GM_vs_WM
lesions_WM_oligos
lesions_GM_oligos
GM_vs_WM_oligos
Outputs
devtools::session_info()Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geom- Session info ---------------------------------------------------------------
setting value
version R version 4.0.3 (2020-10-10)
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 2021-06-04
- Packages -------------------------------------------------------------------
package * version date lib
abind 1.4-5 2016-07-21 [2]
ade4 1.7-16 2020-10-28 [1]
ANCOMBC * 1.0.5 2021-03-09 [1]
ape 5.5 2021-04-25 [1]
assertthat * 0.2.1 2019-03-21 [2]
beachmat 2.6.4 2020-12-20 [1]
beeswarm 0.3.1 2021-03-07 [1]
Biobase * 2.50.0 2020-10-27 [1]
BiocGenerics * 0.36.1 2021-04-16 [1]
BiocManager 1.30.15 2021-05-11 [1]
BiocNeighbors 1.8.2 2020-12-07 [1]
BiocParallel * 1.24.1 2020-11-06 [1]
BiocSingular 1.6.0 2020-10-27 [1]
BiocStyle * 2.18.1 2020-11-24 [1]
biomformat 1.18.0 2020-10-27 [1]
Biostrings 2.58.0 2020-10-27 [1]
bitops 1.0-7 2021-04-24 [2]
bluster 1.0.0 2020-10-27 [1]
bslib 0.2.5 2021-05-12 [2]
cachem 1.0.5 2021-05-15 [2]
Cairo 1.5-12.2 2020-07-07 [2]
callr 3.7.0 2021-04-20 [2]
circlize * 0.4.12 2021-01-08 [1]
cli 2.5.0 2021-04-26 [2]
clue 0.3-59 2021-04-16 [1]
cluster 2.1.2 2021-04-17 [2]
codetools 0.2-18 2020-11-04 [2]
colorout * 1.2-2 2021-04-15 [1]
colorspace 2.0-1 2021-05-04 [2]
ComplexHeatmap * 2.6.2 2020-11-12 [1]
conos * 1.4.1 2021-05-15 [1]
cowplot 1.1.1 2020-12-30 [2]
crayon 1.4.1 2021-02-08 [2]
data.table * 1.14.0 2021-02-21 [2]
DBI 1.1.1 2021-01-15 [2]
DelayedArray 0.16.3 2021-03-24 [1]
DelayedMatrixStats 1.12.3 2021-02-03 [1]
deldir 0.2-10 2021-02-16 [2]
desc 1.3.0 2021-03-05 [2]
devtools 2.4.1 2021-05-05 [1]
digest 0.6.27 2020-10-24 [2]
dplyr 1.0.6 2021-05-05 [2]
dqrng 0.3.0 2021-05-01 [2]
edgeR 3.32.1 2021-01-14 [1]
ellipsis 0.3.2 2021-04-29 [2]
evaluate 0.14 2019-05-28 [2]
fansi 0.4.2 2021-01-15 [2]
farver 2.1.0 2021-02-28 [2]
fastmap 1.1.0 2021-01-25 [2]
fitdistrplus 1.1-3 2020-12-05 [2]
forcats * 0.5.1 2021-01-27 [2]
foreach 1.5.1 2020-10-15 [2]
fs 1.5.0 2020-07-31 [2]
future 1.21.0 2020-12-10 [2]
future.apply 1.7.0 2021-01-04 [2]
generics 0.1.0 2020-10-31 [2]
GenomeInfoDb * 1.26.7 2021-04-08 [1]
GenomeInfoDbData 1.2.4 2021-04-15 [1]
GenomicRanges * 1.42.0 2020-10-27 [1]
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ggbeeswarm 0.6.0 2017-08-07 [1]
ggplot.multistats * 1.0.0 2019-10-28 [1]
ggplot2 * 3.3.3 2020-12-30 [2]
ggrepel * 0.9.1 2021-01-15 [2]
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git2r 0.28.0 2021-01-10 [1]
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globals 0.14.0 2020-11-22 [2]
glue 1.4.2 2020-08-27 [2]
goftest 1.2-2 2019-12-02 [2]
gridExtra 2.3 2017-09-09 [2]
grr 0.9.5 2016-08-26 [1]
gtable 0.3.0 2019-03-25 [2]
hexbin 1.28.2 2021-01-08 [2]
highr 0.9 2021-04-16 [2]
hms 1.1.0 2021-05-17 [2]
htmltools 0.5.1.1 2021-01-22 [2]
htmlwidgets 1.5.3 2020-12-10 [2]
httpuv 1.6.1 2021-05-07 [2]
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jquerylib 0.1.4 2021-04-26 [2]
jsonlite 1.7.2 2020-12-09 [2]
KernSmooth 2.23-20 2021-05-03 [2]
knitr 1.33 2021-04-24 [1]
labeling 0.4.2 2020-10-20 [2]
later 1.2.0 2021-04-23 [2]
lattice 0.20-44 2021-05-02 [2]
lazyeval 0.2.2 2019-03-15 [2]
leiden 0.3.7 2021-01-26 [2]
leidenAlg 0.1.1 2021-03-03 [1]
lifecycle 1.0.0 2021-02-15 [2]
limma 3.46.0 2020-10-27 [1]
listenv 0.8.0 2019-12-05 [2]
lmtest 0.9-38 2020-09-09 [2]
locfit 1.5-9.4 2020-03-25 [1]
lubridate 1.7.10 2021-02-26 [2]
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MatrixGenerics * 1.2.1 2021-01-30 [1]
matrixStats * 0.58.0 2021-01-29 [2]
memoise 2.0.0 2021-01-26 [1]
mgcv 1.8-35 2021-04-18 [2]
microbiome 1.12.0 2020-10-27 [1]
mime 0.10 2021-02-13 [2]
miniUI 0.1.1.1 2018-05-18 [2]
multtest 2.46.0 2020-10-27 [1]
munsell 0.5.0 2018-06-12 [2]
nlme 3.1-152 2021-02-04 [2]
nloptr 1.2.2.2 2020-07-02 [1]
parallelly 1.25.0 2021-04-30 [2]
patchwork * 1.1.1 2020-12-17 [2]
pbapply 1.4-3 2020-08-18 [2]
permute 0.9-5 2019-03-12 [1]
phyloseq * 1.34.0 2020-10-27 [1]
pillar 1.6.1 2021-05-16 [2]
pkgbuild 1.2.0 2020-12-15 [1]
pkgconfig 2.0.3 2019-09-22 [2]
pkgload 1.2.1 2021-04-06 [2]
plotly 4.9.3 2021-01-10 [2]
plyr 1.8.6 2020-03-03 [2]
png 0.1-7 2013-12-03 [2]
polyclip 1.10-0 2019-03-14 [2]
prettyunits 1.1.1 2020-01-24 [2]
processx 3.5.2 2021-04-30 [2]
progress 1.2.2 2019-05-16 [2]
promises 1.2.0.1 2021-02-11 [2]
ps 1.6.0 2021-02-28 [2]
purrr * 0.3.4 2020-04-17 [2]
R.methodsS3 1.8.1 2020-08-26 [1]
R.oo 1.24.0 2020-08-26 [1]
R.utils 2.10.1 2020-08-26 [1]
R6 2.5.0 2020-10-28 [2]
RANN 2.6.1 2019-01-08 [2]
rappdirs 0.3.3 2021-01-31 [2]
rbibutils 2.1.1 2021-04-28 [1]
RColorBrewer * 1.1-2 2014-12-07 [2]
Rcpp 1.0.6 2021-01-15 [2]
RcppAnnoy 0.0.18 2020-12-15 [2]
RCurl 1.98-1.3 2021-03-16 [1]
Rdpack 2.1.1 2021-02-23 [1]
registry 0.5-1 2019-03-05 [1]
remotes 2.3.0 2021-04-01 [1]
reshape2 1.4.4 2020-04-09 [2]
reticulate * 1.20 2021-05-03 [2]
rhdf5 2.34.0 2020-10-27 [1]
rhdf5filters 1.2.1 2021-05-03 [1]
Rhdf5lib 1.12.1 2021-01-26 [1]
rjson 0.2.20 2018-06-08 [1]
rlang 0.4.11 2021-04-30 [2]
rmarkdown 2.8 2021-05-07 [2]
ROCR 1.0-11 2020-05-02 [2]
rpart 4.1-15 2019-04-12 [2]
rprojroot 2.0.2 2020-11-15 [2]
rsvd 1.0.5 2021-04-16 [1]
Rtsne 0.15 2018-11-10 [2]
S4Vectors * 0.28.1 2020-12-09 [1]
sass 0.4.0 2021-05-12 [2]
scales * 1.1.1 2020-05-11 [2]
scater * 1.18.6 2021-02-26 [1]
scattermore 0.7 2020-11-24 [2]
sccore 0.1.3 2021-05-05 [1]
scran * 1.18.7 2021-04-16 [1]
sctransform 0.3.2 2020-12-16 [2]
scuttle 1.0.4 2020-12-17 [1]
seriation * 1.2-9 2020-10-01 [1]
sessioninfo 1.1.1 2018-11-05 [1]
Seurat * 4.0.1 2021-03-18 [2]
SeuratObject * 4.0.1 2021-05-08 [2]
shape 1.4.5 2020-09-13 [2]
shiny 1.6.0 2021-01-25 [2]
SingleCellExperiment * 1.12.0 2020-10-27 [1]
snakecase 0.11.0 2019-05-25 [1]
sparseMatrixStats 1.2.1 2021-02-02 [1]
spatstat.core 2.1-2 2021-04-18 [2]
spatstat.data 2.1-0 2021-03-21 [2]
spatstat.geom 2.1-0 2021-04-15 [2]
spatstat.sparse 2.0-0 2021-03-16 [2]
spatstat.utils 2.1-0 2021-03-15 [2]
statmod 1.4.36 2021-05-10 [1]
stringi 1.6.2 2021-05-17 [2]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-11 2021-04-26 [2]
tensor 1.5 2012-05-05 [2]
testthat 3.0.2 2021-02-14 [2]
tibble 3.1.2 2021-05-16 [2]
tidyr 1.1.3 2021-03-03 [2]
tidyselect 1.1.1 2021-04-30 [2]
TSP 1.1-10 2020-04-17 [1]
usethis 2.0.1 2021-02-10 [1]
utf8 1.2.1 2021-03-12 [2]
uwot * 0.1.10 2020-12-15 [2]
vctrs 0.3.8 2021-04-29 [2]
vegan 2.5-7 2020-11-28 [1]
vipor 0.4.5 2017-03-22 [1]
viridis * 0.6.1 2021-05-11 [1]
viridisLite * 0.4.0 2021-04-13 [2]
whisker 0.4 2019-08-28 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
xfun 0.23 2021-05-15 [1]
xtable 1.8-4 2019-04-21 [2]
XVector 0.30.0 2020-10-27 [1]
yaml 2.2.1 2020-02-01 [2]
zlibbioc 1.36.0 2020-10-27 [1]
zoo 1.8-9 2021-03-09 [2]
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[1] /pstore/home/macnairw/lib/conda_r3.12
[2] /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/R/library
sessionInfo()R version 4.0.3 (2020-10-10)
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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggrepel_0.9.1 reticulate_1.20
[3] MASS_7.3-54 phyloseq_1.34.0
[5] ANCOMBC_1.0.5 purrr_0.3.4
[7] scran_1.18.7 uwot_0.1.10
[9] scater_1.18.6 SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 Biobase_2.50.0
[13] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[15] IRanges_2.24.1 S4Vectors_0.28.1
[17] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[19] matrixStats_0.58.0 BiocParallel_1.24.1
[21] ggplot.multistats_1.0.0 patchwork_1.1.1
[23] seriation_1.2-9 ComplexHeatmap_2.6.2
[25] SeuratObject_4.0.1 Seurat_4.0.1
[27] conos_1.4.1 igraph_1.2.6
[29] Matrix_1.3-3 forcats_0.5.1
[31] ggplot2_3.3.3 scales_1.1.1
[33] viridis_0.6.1 viridisLite_0.4.0
[35] assertthat_0.2.1 stringr_1.4.0
[37] data.table_1.14.0 magrittr_2.0.1
[39] circlize_0.4.12 RColorBrewer_1.1-2
[41] BiocStyle_2.18.1 colorout_1.2-2
[43] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 scattermore_0.7
[3] R.methodsS3_1.8.1 tidyr_1.1.3
[5] knitr_1.33 R.utils_2.10.1
[7] irlba_2.3.3 DelayedArray_0.16.3
[9] rpart_4.1-15 RCurl_1.98-1.3
[11] generics_0.1.0 callr_3.7.0
[13] cowplot_1.1.1 microbiome_1.12.0
[15] usethis_2.0.1 RANN_2.6.1
[17] future_1.21.0 lubridate_1.7.10
[19] spatstat.data_2.1-0 httpuv_1.6.1
[21] xfun_0.23 hms_1.1.0
[23] jquerylib_0.1.4 evaluate_0.14
[25] promises_1.2.0.1 TSP_1.1-10
[27] fansi_0.4.2 progress_1.2.2
[29] DBI_1.1.1 htmlwidgets_1.5.3
[31] spatstat.geom_2.1-0 ellipsis_0.3.2
[33] dplyr_1.0.6 permute_0.9-5
[35] deldir_0.2-10 sparseMatrixStats_1.2.1
[37] vctrs_0.3.8 remotes_2.3.0
[39] Cairo_1.5-12.2 ROCR_1.0-11
[41] abind_1.4-5 cachem_1.0.5
[43] withr_2.4.2 grr_0.9.5
[45] sctransform_0.3.2 vegan_2.5-7
[47] prettyunits_1.1.1 goftest_1.2-2
[49] cluster_2.1.2 ape_5.5
[51] lazyeval_0.2.2 crayon_1.4.1
[53] labeling_0.4.2 edgeR_3.32.1
[55] pkgconfig_2.0.3 pkgload_1.2.1
[57] nlme_3.1-152 vipor_0.4.5
[59] devtools_2.4.1 rlang_0.4.11
[61] globals_0.14.0 lifecycle_1.0.0
[63] miniUI_0.1.1.1 registry_0.5-1
[65] rsvd_1.0.5 rprojroot_2.0.2
[67] polyclip_1.10-0 lmtest_0.9-38
[69] Rhdf5lib_1.12.1 zoo_1.8-9
[71] Matrix.utils_0.9.8 beeswarm_0.3.1
[73] processx_3.5.2 whisker_0.4
[75] ggridges_0.5.3 GlobalOptions_0.1.2
[77] png_0.1-7 rjson_0.2.20
[79] bitops_1.0-7 R.oo_1.24.0
[81] KernSmooth_2.23-20 rhdf5filters_1.2.1
[83] Biostrings_2.58.0 DelayedMatrixStats_1.12.3
[85] shape_1.4.5 parallelly_1.25.0
[87] sccore_0.1.3 beachmat_2.6.4
[89] memoise_2.0.0 plyr_1.8.6
[91] hexbin_1.28.2 ica_1.0-2
[93] zlibbioc_1.36.0 compiler_4.0.3
[95] dqrng_0.3.0 clue_0.3-59
[97] fitdistrplus_1.1-3 cli_2.5.0
[99] snakecase_0.11.0 ade4_1.7-16
[101] XVector_0.30.0 listenv_0.8.0
[103] ps_1.6.0 pbapply_1.4-3
[105] mgcv_1.8-35 tidyselect_1.1.1
[107] stringi_1.6.2 highr_0.9
[109] yaml_2.2.1 BiocSingular_1.6.0
[111] locfit_1.5-9.4 sass_0.4.0
[113] tools_4.0.3 future.apply_1.7.0
[115] rstudioapi_0.13 bluster_1.0.0
[117] foreach_1.5.1 git2r_0.28.0
[119] janitor_2.1.0 gridExtra_2.3
[121] farver_2.1.0 Rtsne_0.15
[123] digest_0.6.27 BiocManager_1.30.15
[125] shiny_1.6.0 Rcpp_1.0.6
[127] scuttle_1.0.4 later_1.2.0
[129] RcppAnnoy_0.0.18 httr_1.4.2
[131] Rdpack_2.1.1 colorspace_2.0-1
[133] fs_1.5.0 tensor_1.5
[135] splines_4.0.3 statmod_1.4.36
[137] spatstat.utils_2.1-0 multtest_2.46.0
[139] sessioninfo_1.1.1 plotly_4.9.3
[141] xtable_1.8-4 jsonlite_1.7.2
[143] nloptr_1.2.2.2 leidenAlg_0.1.1
[145] testthat_3.0.2 R6_2.5.0
[147] pillar_1.6.1 htmltools_0.5.1.1
[149] mime_0.10 glue_1.4.2
[151] fastmap_1.1.0 BiocNeighbors_1.8.2
[153] codetools_0.2-18 pkgbuild_1.2.0
[155] utf8_1.2.1 lattice_0.20-44
[157] bslib_0.2.5 spatstat.sparse_2.0-0
[159] tibble_3.1.2 ggbeeswarm_0.6.0
[161] leiden_0.3.7 survival_3.2-11
[163] limma_3.46.0 rmarkdown_2.8
[165] desc_1.3.0 biomformat_1.18.0
[167] munsell_0.5.0 GetoptLong_1.0.5
[169] rhdf5_2.34.0 GenomeInfoDbData_1.2.4
[171] iterators_1.0.13 reshape2_1.4.4
[173] gtable_0.3.0 rbibutils_2.1.1
[175] spatstat.core_2.1-2