Differential abundance testing with ANCOM-BC
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandOctober 01, 2021
Last updated: 2021-10-01
Checks: 5 2
Knit directory: MS_lesions/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 224491b | Macnair | 2021-09-03 | Update ancom analysis with figures for paper |
| html | 224491b | Macnair | 2021-09-03 | Update ancom analysis with figures for paper |
| Rmd | 841b6a5 | Macnair | 2021-08-11 | Add CLR plots of all glial cells to ANCOM analysis |
| html | 841b6a5 | Macnair | 2021-08-11 | Add CLR plots of all glial cells to ANCOM analysis |
| Rmd | f20d2da | Macnair | 2021-07-22 | Add GM vs WM glial only to ANCOM-BC |
| html | f20d2da | Macnair | 2021-07-22 | Add GM vs WM glial only to ANCOM-BC |
| Rmd | 9d1ab6c | Macnair | 2021-06-03 | Updated ACNOM-BC with type_fine |
| html | 9d1ab6c | Macnair | 2021-06-03 | Updated ACNOM-BC with type_fine |
| html | 71bf87c | Macnair | 2021-05-26 | Tweaked ANCOM propns plots |
| Rmd | c9c8c9a | Macnair | 2021-05-26 | Updated ms09_ancombc with proportions of whole sample |
| html | c9c8c9a | Macnair | 2021-05-26 | Updated ms09_ancombc with proportions of whole sample |
| Rmd | 58205c2 | Macnair | 2021-05-21 | Update with random effects and markers |
| html | 1600da1 | Macnair | 2021-05-12 | Tweaked ANCOM CLR plots |
| html | 9852840 | Macnair | 2021-05-12 | Adding docs to repo for first time - massive! |
| Rmd | 129c53d | Macnair | 2021-04-16 | Renamed a lot of things to add ms07_soup |
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.
- ctrl_GM_vs_WM_glial: Testing healthy GM vs healthy WM, WM only, glial cells only.
- MS_GM_vs_WM_glial: Testing sick GM vs healthy WM, WM only, glial cells 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
- ctrl_GM_vs_WM: OPCs / COPs
- MS_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_checked_assumptions_20210928.xlsx"
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')Outputs
# where to save?
save_dir = 'output/ms09_ancombc'
date_tag = '20211001'
if (!dir.exists(save_dir))
dir.create(save_dir)
# strange samples
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'sample_source', 'subject_id',
'sex', 'age_scale', 'pmi_cat')
mad_cut = 2
# 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'),
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')
),
ctrl_GM_vs_WM_glial = list(
subset_spec = list(lesion_type = c('GM', 'WM'), neuro_ok = TRUE),
type_spec = c('Oligodendrocytes', 'OPCs / COPs', 'Astrocytes', 'Microglia')
),
MS_GM_vs_WM_glial = list(
subset_spec = list(lesion_type = c('GML', 'NAGM', 'NAWM', 'AL', 'CAL', 'CIL', 'RL'),
neuro_ok = TRUE),
type_spec = c('Oligodendrocytes', 'OPCs / COPs', 'Astrocytes', 'Microglia')
)
)
model_names = names(subset_list)
formulae = list(
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ matter + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ lesion_type + sex + age_scale',
'~ matter + sex + age_scale',
'~ matter + sex + age_scale',
'~ matter + sex + age_scale'
) %>% 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)',
'Healthy GM vs WM (neurons excluded)',
'MS GM vs WM (neurons excluded)'
) %>% 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',
'matter',
'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'),
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_%s_%s.rds', save_dir, date_tag, '%s')
# define sccoda setup
n_results = 20000L
n_burnin = 5000L
coda_pat = sprintf('%s/sccoda_obj_%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)
meta_dt = load_meta_dt_from_xls(meta_f, outlier_samples = NULL)Processing / calculations
conos_dt = merge(conos_dt, meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = mad_cut)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)
bpstop()
bpstart()
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
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')
}
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
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
astro_opc , WM
astro_opc , GM
endo_peri , WM
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
astro_opc , WM
astro_opc , GM
endo_peri , WM
CLR plots of celltype proportions, glial only
for (m in c('WM', 'GM')) {
cat('### ', m, '\n')
input_dt = conos_dt[(matter == m) &
# (neuro_ok == TRUE) &
!(type_broad %in% c('Excitatory neurons', 'Inhibitory neurons'))]
suppressWarnings(print(plot_sample_clrs(input_dt)))
cat('\n\n')
}
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_no_neuro
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')
}
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_no_neuro
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_no_neuro
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_no_neuro
Figures
Standard ANCOM-BC output plots for GM vs WM
nn = 'ctrl_GM_vs_WM_glial'
(plot_ancombc_ci(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = whatplot_list[[nn]], reported_only = FALSE, add_plot = 'prop'))
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
Tweaked ANCOM-BC output plots for GM vs WM for manuscript:
nn = 'ctrl_GM_vs_WM_glial'
(plot_ancombc_props(ancom_list[[nn]], counts_wide, names_list[[nn]],
whatplot = whatplot_list[[nn]]))
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
GPR17 validation plot
set.seed(20210830)
prop_fine_f = 'output/ms07_soup/pb_prop_fine_2021-06-01.rds'
sel_g = "GPR17"
(plot_raw_counts_one_celltype(prop_fine_f, conos_dt, sel_g))
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
Patient variability line-plot
nn = 'lesions_WM_no_neuro'
suppressMessages({g = plot_patient_variability_by_sample(conos_dt, subset_list[[nn]])})
print(g)geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
nn = 'lesions_WM_no_neuro'
(plot_patient_variability_by_patient(conos_dt, subset_list[[nn]]))geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
geom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
Heatmaps of CLRs
cat("### WM\n")WM
wm_types = setdiff(broad_ord, c('Excitatory neurons', 'Inhibitory neurons'))
props_wm = calc_props_dt(conos_dt[ type_broad %in% wm_types ], sample_vars) %>%
.[matter == "WM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_wm, split_rows = FALSE),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')cat("### GM\n")GM
gm_types = setdiff(broad_ord, c('Immune'))
props_gm = calc_props_dt(conos_dt[ type_broad %in% gm_types ], sample_vars) %>%
.[matter == "GM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_gm, split_rows = FALSE),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')Heatmaps of proportions
cat("### WM\n")WM
wm_types = setdiff(broad_ord, c('Excitatory neurons', 'Inhibitory neurons'))
props_wm = calc_props_dt(conos_dt[ type_broad %in% wm_types ], sample_vars) %>%
.[matter == "WM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_wm, split_rows = FALSE, what = 'prop'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')cat("### GM\n")GM
gm_types = setdiff(broad_ord, c('Immune'))
props_gm = calc_props_dt(conos_dt[ type_broad %in% gm_types ], sample_vars) %>%
.[matter == "GM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_gm, split_rows = FALSE, what = 'prop'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')Heatmaps of log proportions
cat("### WM\n")WM
wm_types = setdiff(broad_ord, c('Excitatory neurons', 'Inhibitory neurons'))
props_wm = calc_props_dt(conos_dt[ type_broad %in% wm_types ], sample_vars) %>%
.[matter == "WM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_wm, split_rows = FALSE, what = 'log_p'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')cat("### GM\n")GM
gm_types = setdiff(broad_ord, c('Immune'))
props_gm = calc_props_dt(conos_dt[ type_broad %in% gm_types ], sample_vars) %>%
.[matter == "GM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_gm, split_rows = FALSE, what = 'log_p'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')Heatmaps of log proportions (oligodendroglia only)
cat("### WM\n")WM
wm_types = c('Oligodendrocytes', 'OPCs / COPs')
props_wm = calc_props_dt(conos_dt[ type_broad %in% wm_types ], sample_vars) %>%
.[matter == "WM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_wm, split_rows = FALSE, what = 'log_p'),
row_dend_width = unit(1, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')cat("### GM\n")GM
gm_types = c('Oligodendrocytes', 'OPCs / COPs')
props_gm = calc_props_dt(conos_dt[ type_broad %in% gm_types ], sample_vars) %>%
.[matter == "GM" & neuro_ok == TRUE]
draw(plot_clr_heatmap(props_gm, split_rows = FALSE, what = 'log_p'),
row_dend_width = unit(1, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
cat('\n\n')Barplots of samples
t = 'oligo_opc'
types = c('OPCs / COPs', 'Oligodendrocytes')
m = "WM"
(plot_sample_splits(conos_dt[matter == m], types = types, show_broad = FALSE))
| Version | Author | Date |
|---|---|---|
| 224491b | Macnair | 2021-09-03 |
Outputs
devtools::session_info()Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geom- Session info ---------------------------------------------------------------
setting value
version R version 4.0.5 (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 2021-10-01
- Packages -------------------------------------------------------------------
! package * version date lib
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ade4 1.7-17 2021-06-17 [1]
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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
R -- Package was removed from disk.
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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggbeeswarm_0.6.0 ggrepel_0.9.1
[3] reticulate_1.21 MASS_7.3-54
[5] phyloseq_1.34.0 ANCOMBC_1.0.5
[7] purrr_0.3.4 scran_1.18.7
[9] uwot_0.1.10 scater_1.18.6
[11] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[13] Biobase_2.50.0 GenomicRanges_1.42.0
[15] GenomeInfoDb_1.26.7 IRanges_2.24.1
[17] S4Vectors_0.28.1 BiocGenerics_0.36.1
[19] MatrixGenerics_1.2.1 matrixStats_0.60.1
[21] BiocParallel_1.24.1 ggplot.multistats_1.0.0
[23] patchwork_1.1.1 seriation_1.3.0
[25] ComplexHeatmap_2.6.2 SeuratObject_4.0.2
[27] Seurat_4.0.4 conos_1.4.3
[29] igraph_1.2.6 Matrix_1.3-4
[31] readxl_1.3.1 forcats_0.5.1
[33] ggplot2_3.3.5 scales_1.1.1
[35] viridis_0.6.1 viridisLite_0.4.0
[37] assertthat_0.2.1 stringr_1.4.0
[39] data.table_1.14.0 magrittr_2.0.1
[41] circlize_0.4.13 RColorBrewer_1.1-2
[43] BiocStyle_2.18.1 colorout_1.2-2
[45] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2
[3] ps_1.6.0 foreach_1.5.1
[5] lmtest_0.9-38 rprojroot_2.0.2
[7] crayon_1.4.1 spatstat.core_2.3-0
[9] rbibutils_2.2.3 rhdf5filters_1.2.1
[11] Matrix.utils_0.9.8 backports_1.2.1
[13] nlme_3.1-153 rlang_0.4.11
[15] XVector_0.30.0 ROCR_1.0-11
[17] microbiome_1.12.0 irlba_2.3.3
[19] callr_3.7.0 nloptr_1.2.2.2
[21] limma_3.46.0 rjson_0.2.20
[23] bit64_4.0.5 glue_1.4.2
[25] sctransform_0.3.2 processx_3.5.2
[27] pbkrtest_0.5.1 vipor_0.4.5
[29] spatstat.sparse_2.0-0 AnnotationDbi_1.52.0
[31] muscat_1.5.1 spatstat.geom_2.2-2
[33] tidyselect_1.1.1 usethis_2.0.1
[35] fitdistrplus_1.1-5 variancePartition_1.20.0
[37] XML_3.99-0.7 tidyr_1.1.3
[39] zoo_1.8-9 xtable_1.8-4
[41] evaluate_0.14 cli_3.0.1
[43] Rdpack_2.1.2 scuttle_1.0.4
[45] zlibbioc_1.36.0 miniUI_0.1.1.1
[47] whisker_0.4 bslib_0.3.0
[49] rpart_4.1-15 betareg_3.1-4
[51] shiny_1.6.0 BiocSingular_1.6.0
[53] xfun_0.26 clue_0.3-59
[55] pkgbuild_1.2.0 multtest_2.46.0
[57] cluster_2.1.2 caTools_1.18.2
[59] TSP_1.1-10 biomformat_1.18.0
[61] tibble_3.1.4 ape_5.5
[63] listenv_0.8.0 Biostrings_2.58.0
[65] png_0.1-7 permute_0.9-5
[67] future_1.22.1 withr_2.4.2
[69] bitops_1.0-7 plyr_1.8.6
[71] cellranger_1.1.0 dqrng_0.3.0
[73] pillar_1.6.2 gplots_3.1.1
[75] GlobalOptions_0.1.2 cachem_1.0.6
[77] fs_1.5.0 flexmix_2.3-17
[79] GetoptLong_1.0.5 DelayedMatrixStats_1.12.3
[81] vctrs_0.3.8 ellipsis_0.3.2
[83] generics_0.1.0 devtools_2.4.2
[85] tools_4.0.5 beeswarm_0.4.0
[87] munsell_0.5.0 DelayedArray_0.16.3
[89] pkgload_1.2.2 fastmap_1.1.0
[91] compiler_4.0.5 abind_1.4-5
[93] httpuv_1.6.3 sessioninfo_1.1.1
[95] plotly_4.9.4.1 GenomeInfoDbData_1.2.4
[97] gridExtra_2.3 glmmTMB_1.1.2.2
[99] edgeR_3.32.1 lattice_0.20-44
[101] deldir_0.2-10 utf8_1.2.2
[103] later_1.3.0 dplyr_1.0.7
[105] jsonlite_1.7.2 pbapply_1.4-3
[107] sparseMatrixStats_1.2.1 genefilter_1.72.1
[109] lazyeval_0.2.2 promises_1.2.0.1
[111] doParallel_1.0.16 R.utils_2.10.1
[113] goftest_1.2-2 spatstat.utils_2.2-0
[115] rmarkdown_2.11 sandwich_3.0-1
[117] cowplot_1.1.1 blme_1.0-5
[119] statmod_1.4.36 Rtsne_0.15
[121] survival_3.2-13 numDeriv_2016.8-1.1
[123] yaml_2.2.1 htmltools_0.5.2
[125] memoise_2.0.0 modeltools_0.2-23
[127] locfit_1.5-9.4 here_1.0.1
[129] digest_0.6.27 mime_0.11
[131] rappdirs_0.3.3 registry_0.5-1
[133] RSQLite_2.2.8 future.apply_1.8.1
[135] remotes_2.4.0 blob_1.2.2
[137] vegan_2.5-7 R.oo_1.24.0
[139] splines_4.0.5 Formula_1.2-4
[141] labeling_0.4.2 Rhdf5lib_1.12.1
[143] Cairo_1.5-12.2 RCurl_1.98-1.4
[145] broom_0.7.9 hms_1.1.0
[147] rhdf5_2.34.0 colorspace_2.0-2
[149] BiocManager_1.30.16 shape_1.4.6
[151] nnet_7.3-16 sass_0.4.0
[153] Rcpp_1.0.7 RANN_2.6.1
[155] fansi_0.5.0 parallelly_1.28.1
[157] R6_2.5.1 ggridges_0.5.3
[159] lifecycle_1.0.0 bluster_1.0.0
[161] minqa_1.2.4 testthat_3.0.4
[163] leiden_0.3.8 jquerylib_0.1.4
[165] snakecase_0.11.0 desc_1.3.0
[167] RcppAnnoy_0.0.19 iterators_1.0.13
[169] TMB_1.7.21 htmlwidgets_1.5.4
[171] beachmat_2.6.4 polyclip_1.10-0
[173] mgcv_1.8-36 globals_0.14.0
[175] leidenAlg_0.1.1 codetools_0.2-18
[177] lubridate_1.7.10 gtools_3.9.2
[179] prettyunits_1.1.1 R.methodsS3_1.8.1
[181] gtable_0.3.0 DBI_1.1.1
[183] git2r_0.28.0 tensor_1.5
[185] httr_1.4.2 highr_0.9
[187] KernSmooth_2.23-20 stringi_1.7.4
[189] progress_1.2.2 reshape2_1.4.4
[191] farver_2.1.0 annotate_1.68.0
[193] hexbin_1.28.2 magick_2.7.2
[195] colorRamps_2.3 sccore_0.1.3
[197] boot_1.3-28 grr_0.9.5
[199] BiocNeighbors_1.8.2 lme4_1.1-27.1
[201] ade4_1.7-17 geneplotter_1.68.0
[203] scattermore_0.7 DESeq2_1.30.1
[205] bit_4.0.4 spatstat.data_2.1-0
[207] janitor_2.1.0 pkgconfig_2.0.3
[209] lmerTest_3.1-3 knitr_1.34