ANCOM-BC bootstrapped to handle random effects
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandNovember 18, 2021
Last updated: 2021-11-18
Checks: 5 2
Knit directory: MS_lesions/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | afe32c6 | Macnair | 2021-11-16 | Final version of ANCOM-BC analysis |
| html | afe32c6 | Macnair | 2021-11-16 | Final version of ANCOM-BC analysis |
| Rmd | 8d1ece6 | Macnair | 2021-10-28 | Update ms09_ancombc_mixed with paired analysis |
| html | 8d1ece6 | Macnair | 2021-10-28 | Update ms09_ancombc_mixed with paired analysis |
| Rmd | a73a2fa | Macnair | 2021-10-23 | Update ANCOM bootstrapping analysis |
| html | a73a2fa | Macnair | 2021-10-23 | Update ANCOM bootstrapping analysis |
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')
source('code/ms09_ancombc_mixed.R')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_2021-10-08.xlsx"
# define pseudobulk data
soup_dir = 'output/ms07_soup'
pb_broad_f = file.path(soup_dir, 'pb_sum_broad_2021-10-11.rds')
pb_fine_f = file.path(soup_dir, 'pb_sum_fine_2021-10-11.rds')
pb_f_ls = c(broad = pb_broad_f, fine = pb_fine_f)Outputs
# where to save?
save_dir = 'output/ms09_ancombc'
date_tag = '2021-11-12'
if (!dir.exists(save_dir))
dir.create(save_dir)
# sample variables
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'sample_source', 'subject_id',
'sex', 'age_scale', 'pmi_cat', 'pmi_cat2')
# identifying strange samples
neuro_mad_cut = 2
log_n_mad_cut = 3
# define how to select PCs
cut_var_exp = 0.01
cut_layer_cor = 0.2
# define WM data
wm_spec = list(
name = 'lesions_WM',
subset = list(matter = 'WM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = '^(Ex_|Inh_|Neuro_oligo)',
formula = '~ lesion_type + sex + age_scale + pmi_cat',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat'),
ranef_var = 'subject_id'
)
gm_spec = list(
name = 'lesions_GM',
subset = list(matter = 'GM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = NULL,
formula = '~ lesion_type + sex + age_scale + pmi_cat2',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
ranef_var = 'subject_id'
)
# define multiple different ways to do subspaces
gm_pc_spec = list(
name_str = 'lesions_GM_',
subset = list(matter = 'GM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = NULL,
formula_pat = '~ lesion_type + %s + sex + age_scale + pmi_cat2',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
ranef_var = 'subject_id',
broad_sel = c("Excitatory neurons", "Inhibitory neurons"),
lesion_ctrl = "GM",
n_pcs = NA
)
# define multiple different ways to do subspaces
nagm_pc_spec = list(
name_str = 'lesions_NAGM_',
subset = list(matter = 'GM', neuro_ok = TRUE),
size = list(min_count = 10, min_prop = 0.1),
exc_regex = NULL,
formula_pat = '~ lesion_type + %s + sex + age_scale + pmi_cat2',
fixef_test = 'lesion_type',
fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
ranef_var = 'subject_id',
broad_sel = c("Excitatory neurons", "Inhibitory neurons"),
lesion_ctrl = "GM",
n_pcs = NA
)
# gather things
spec_list = list(wm_spec, gm_spec)
names(spec_list) = sapply(spec_list, function(l) l$name)
# bootstrapping parameters
n_boots = 2e4
n_cores = 24
# define files for saving outputs
lrt_pat = file.path(save_dir, 'abundance_nb_lrt_model_%s_%s.rds')
clr_pat = file.path(save_dir, 'clr_clustering_%s_%s.txt')
ancom_pat = file.path(save_dir, 'ancombc_standard_%s_%s.rds')
boots_pat = file.path(save_dir, 'ancombc_bootstrap_%s_%s.txt.gz')
# define files for saving outputs
pb_pcs_ls = c(
broad = sprintf('%s/pb_gm_w_pcs_sum_broad_%s.rds', soup_dir, date_tag),
fine = sprintf('%s/pb_gm_w_pcs_sum_fine_%s.rds', soup_dir, date_tag)
)Load inputs
meta_dt = load_meta_dt_from_xls(meta_f)
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
conos_dt = load_labelled_dt(labelled_f, labels_f) %>%
merge(meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = neuro_mad_cut)# check for any outliers
size_chks = calc_size_outliers(conos_dt, mad_cut = log_n_mad_cut)
message("these samples excluded to outlier sample sizes:")these samples excluded to outlier sample sizes:print(size_chks[ size_ok == FALSE ]) matter sample_id N med_log_N mad_log_N size_ok
1: GM EU034 911 8.519391 0.423111 FALSE
2: GM EU043 687 8.519391 0.423111 FALSE# exclude them from conos
ok_samples = size_chks[ size_ok == TRUE]$sample_id
conos_dt = conos_dt[ (sample_id %in% ok_samples) & (neuro_ok == TRUE) ]props_dt = calc_props_dt(conos_dt, sample_vars)
wide_dt = calc_counts_wide(props_dt, sample_vars)# get neuronal proportions for all samples
props_neu = conos_dt %>%
.[ (type_broad %in% gm_pc_spec$broad_sel) ] %>%
calc_props_dt(sample_vars)
# calc PCAs
ctrl_pcs_dt = props_neu %>%
.[ lesion_type == gm_pc_spec$lesion_ctrl ] %>%
calc_ctrl_pcs_dt(layers_dt)# apply pcs
all_pcs_dt = apply_ctrl_pcs(props_neu[ matter == "GM" ], ctrl_pcs_dt,
cut_var_exp, cut_layer_cor)
wide_neu = merge(all_pcs_dt, wide_dt, 'sample_id')Processing / calculations
# negative-binomial model on unadjusted counts, WM
nb_wm_f = sprintf(lrt_pat, wm_spec$name, date_tag)
if (file.exists(nb_wm_f)) {
nb_wm_ls = readRDS(nb_wm_f)
} else {
nb_wm_ls = calc_celltype_mixed_models(wide_dt, sample_vars,
wm_spec$subset, wm_spec$size, wm_spec$exc_regex, wm_spec$inc_regex,
wm_spec$fixef_test, wm_spec$fixef_covar, wm_spec$ranef_var,
n_cores = n_cores, offset_var = NULL)
saveRDS(nb_wm_ls, file = nb_wm_f)
}
# negative-binomial model on unadjusted counts, GM
nb_gm_f = sprintf(lrt_pat, gm_spec$name, date_tag)
if (file.exists(nb_gm_f)) {
nb_gm_ls = readRDS(nb_gm_f)
} else {
nb_gm_ls = calc_celltype_mixed_models(wide_dt, sample_vars,
gm_spec$subset, gm_spec$size, gm_spec$exc_regex, gm_spec$inc_regex,
gm_spec$fixef_test, gm_spec$fixef_covar, gm_spec$ranef_var,
n_cores = n_cores, offset_var = NULL)
saveRDS(nb_gm_ls, file = nb_gm_f)
}# make spec
pc_vars = str_subset(names(wide_neu), "ctrl_PC")
for (n_pcs in seq_along(pc_vars)) {
# which PCs?
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = n_pcs)
# negative-binomial model on unadjusted counts, GM, w layers
nb_layers_f = sprintf(lrt_pat, layer_spec$name, date_tag)
if (!file.exists(nb_layers_f)) {
nb_layers_ls = calc_celltype_mixed_models(wide_neu, c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex, layer_spec$inc_regex,
layer_spec$fixef_test, layer_spec$fixef_covar, layer_spec$ranef_var,
n_cores = n_cores, offset_var = NULL)
saveRDS(nb_layers_ls, file = nb_layers_f)
}
}
# load them
nb_pcs_ls = lapply(seq_along(pc_vars), function(n_pcs) {
# make file
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = n_pcs)
nb_gm_ls = sprintf(lrt_pat, layer_spec$name, date_tag) %>%
readRDS
return(nb_gm_ls)
}) %>% setNames(paste0(gm_pc_spec$name_str, seq_along(pc_vars), 'pcs'))
# make list of models
lrt_ls = list(WM = nb_wm_ls, GM = nb_gm_ls) %>%
c(nb_pcs_ls)# loop through specified models
for (nn in names(spec_list)) {
# make file
ancom_f = sprintf(ancom_pat, spec_list[[nn]]$name, date_tag)
# if necessary, run thing
if (!file.exists(ancom_f)) {
message('running standard ANCOM-BC for ', nn)
# define things we need
spec = spec_list[[nn]]
# do standard ANCOM, save results
ancom_obj = calc_ancom_standard(wide_dt, sample_vars,
spec$subset, spec$size, spec$exc_regex, spec$inc_regex, spec$ref_type,
spec$fixef_test, spec$fixef_covar)
saveRDS(ancom_obj, file = ancom_f)
}
}# loop through specified models
for (nn in names(spec_list)) {
# make file
boots_f = sprintf(boots_pat, spec_list[[nn]]$name, date_tag)
# if necessary, run thing
if (!file.exists(boots_f)) {
message('running bootstrapped ANCOM-BC for ', nn)
# define things we need
spec = spec_list[[nn]]
# do bootstrapping, save resulst
boots_dt = calc_ancom_bootstrap(wide_dt, sample_vars,
spec$subset, spec$size, spec$exc_regex, spec$inc_regex, spec$ref_type,
spec$fixef_test, spec$fixef_covar, spec$ranef_var,
seed = 1, n_boots, n_cores)
fwrite(boots_dt, file = boots_f)
}
}# set up this run
for (n_pcs in seq_along(pc_vars)) {
# which PCs?
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = n_pcs)
# make file
ancom_f = sprintf(ancom_pat, layer_spec$name, date_tag)
# if necessary, run thing
if (file.exists(ancom_f)) {
message('standard ANCOM-BC for ', layer_spec$name, ' already done')
} else {
# do bootstrapping, save results
message('running standard ANCOM-BC for ', layer_spec$name)
ancom_neu = calc_ancom_standard(wide_neu, c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar)
saveRDS(ancom_neu, file = ancom_f)
}
# do bootstrapping
boots_f = sprintf(boots_pat, layer_spec$name, date_tag)
# if necessary, run thing
if (file.exists(boots_f)) {
message('bootstrapped ANCOM-BC for ', layer_spec$name, ' already done')
} else {
# do bootstrapping, save resulst
message('running bootstrapped ANCOM-BC for ', layer_spec$name)
t_start = Sys.time()
boots_neu = calc_ancom_bootstrap(wide_neu, c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar, layer_spec$ranef_var,
seed = 1, n_boots, n_cores)
t_stop = Sys.time()
fwrite(boots_neu, file = boots_f)
# report how long it took
t_elapsed = difftime(t_stop, t_start, units = 'mins') %>% unclass
message(sprintf(
paste0(' (bootstrapping %d boots with %d cores took %.1f minutes;',
' %.1f boots / min / core)'),
n_boots, n_cores, t_elapsed, n_boots / t_elapsed / n_cores))
}
}standard ANCOM-BC for lesions_GM_1pcs already donebootstrapped ANCOM-BC for lesions_GM_1pcs already donestandard ANCOM-BC for lesions_GM_2pcs already donebootstrapped ANCOM-BC for lesions_GM_2pcs already donestandard ANCOM-BC for lesions_GM_3pcs already donebootstrapped ANCOM-BC for lesions_GM_3pcs already donestandard ANCOM-BC for lesions_GM_4pcs already donebootstrapped ANCOM-BC for lesions_GM_4pcs already donestandard ANCOM-BC for lesions_GM_5pcs already donebootstrapped ANCOM-BC for lesions_GM_5pcs already donestandard ANCOM-BC for lesions_GM_6pcs already donebootstrapped ANCOM-BC for lesions_GM_6pcs already donestandard ANCOM-BC for lesions_GM_7pcs already donebootstrapped ANCOM-BC for lesions_GM_7pcs already done# set up this run
nagm_pcs = 4
layer_spec = make_layer_pc_spec(nagm_pc_spec, pc_vars, n_pcs = nagm_pcs)
wide_nagm = wide_neu %>% copy %>%
.[, lesion_type := lesion_type %>% fct_relevel("NAGM") %>% fct_drop ]
# make file
ancom_f = sprintf(ancom_pat, layer_spec$name, date_tag)
# if necessary, run thing
source("code/ms09_ancombc_mixed.R")
if (file.exists(ancom_f)) {
message('standard ANCOM-BC for ', layer_spec$name, ' already done')
ancom_nagm = ancom_f %>% readRDS
} else {
# do bootstrapping, save results
message('running standard ANCOM-BC for ', layer_spec$name)
ancom_nagm = calc_ancom_standard(wide_nagm, c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar)
saveRDS(ancom_nagm, file = ancom_f)
}standard ANCOM-BC for lesions_NAGM_4pcs already done# do bootstrapping
boots_f = sprintf(boots_pat, layer_spec$name, date_tag)
# if necessary, run thing
if (file.exists(boots_f)) {
message('bootstrapped ANCOM-BC for ', layer_spec$name, ' already done')
boots_nagm = boots_f %>% fread
} else {
# do bootstrapping, save resulst
message('running bootstrapped ANCOM-BC for ', layer_spec$name)
t_start = Sys.time()
boots_nagm = calc_ancom_bootstrap(wide_nagm, c(sample_vars, pc_vars),
layer_spec$subset, layer_spec$size, layer_spec$exc_regex,
layer_spec$inc_regex, layer_spec$ref_type,
layer_spec$fixef_test, layer_spec$fixef_covar, layer_spec$ranef_var,
seed = 1, n_boots, n_cores)
t_stop = Sys.time()
fwrite(boots_nagm, file = boots_f)
# report how long it took
t_elapsed = difftime(t_stop, t_start, units = 'mins') %>% unclass
message(sprintf(
paste0(' (bootstrapping %d boots with %d cores took %.1f minutes;',
' %.1f boots / min / core)'),
n_boots, n_cores, t_elapsed, n_boots / t_elapsed / n_cores))
}bootstrapped ANCOM-BC for lesions_NAGM_4pcs already doneancom_ls = lapply(names(spec_list), function(nn) {
# make file
ancom_obj = sprintf(ancom_pat, spec_list[[nn]]$name, date_tag) %>%
readRDS
return(ancom_obj)
}) %>% setNames(names(spec_list))boots_ls = lapply(names(spec_list), function(nn) {
# make file
boots_dt = sprintf(boots_pat, spec_list[[nn]]$name, date_tag) %>% fread
return(boots_dt)
}) %>% setNames(names(spec_list))# load std
ancom_pcs_ls = lapply(seq_along(pc_vars), function(n_pcs) {
# make file
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = n_pcs)
ancom_obj = sprintf(ancom_pat, layer_spec$name, date_tag) %>%
readRDS
return(ancom_obj)
}) %>% setNames(paste0(gm_pc_spec$name_str, seq_along(pc_vars), 'pcs'))
# load boots
boots_pcs_ls = lapply(seq_along(pc_vars), function(n_pcs) {
# make file
layer_spec = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = n_pcs)
boots_dt = sprintf(boots_pat, layer_spec$name, date_tag) %>% fread
return(boots_dt)
}) %>% setNames(paste0(gm_pc_spec$name_str, seq_along(pc_vars), 'pcs'))ancom_ls = c(ancom_ls, ancom_pcs_ls, list(lesions_NAGM_4pcs = ancom_nagm))
boots_ls = c(boots_ls, boots_pcs_ls, list(lesions_NAGM_4pcs = boots_nagm))pcs_coefs_dt = calc_pcs_coefs_dt(pc_vars, boots_ls, labels_dt) Analysis
Ctrl GM vs WM
for (m in c('nbinom', 'poisson', 'beta')) {
cat('### ', m, '\n')
print(plot_wm_vs_gm(conos_dt, model = m))
cat('\n\n')
}nbinom
poisson
Contribution of lesion type and donor to variability in celltype counts
for (nn in names(lrt_ls)) {
cat('### ', nn, '\n')
print(plot_lrt_results(lrt_ls[[nn]]$anova_dt, labels_dt))
cat('\n\n')
}
Compositional grouping heatmaps
Compositional groupings of samples (CLR)
# plot CLR heatmaps
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" ]
hm_wm = plot_clr_heatmap(props_wm, cluster_rows = TRUE, n_clusters = 5, what = 'clr')
hm_wm = draw(hm_wm, row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
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" ]
hm_gm = plot_clr_heatmap(props_gm, cluster_rows = TRUE, n_clusters = 5, what = 'clr')
hm_gm = draw(hm_gm, row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
cat('\n\n')# save clusters
save_heatmap_clusters(hm_wm@ht_list[[1]], sprintf(clr_pat, "WM", date_tag))
save_heatmap_clusters(hm_gm@ht_list[[1]], sprintf(clr_pat, "GM", date_tag))Compositional groupings of samples (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, cluster_rows = FALSE, what = 'log_p'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
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, cluster_rows = FALSE, what = 'log_p'),
row_dend_width = unit(0.5, "in"), merge_legend = TRUE)
cat('\n\n')Compositional groupings of samples (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, cluster_rows = FALSE, what = 'log_p'),
row_dend_width = unit(1, "in"), merge_legend = TRUE)
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, cluster_rows = FALSE, what = 'log_p'),
row_dend_width = unit(1, "in"), merge_legend = TRUE)
cat('\n\n')Compositional groupings of samples (GM, CLR, neurons only)
gm_types = c('Excitatory neurons', 'Inhibitory neurons')
props_gm = calc_props_dt(conos_dt[ type_broad %in% gm_types ], sample_vars) %>%
.[ matter == "GM" ] %>%
merge(wide_neu[, .(sample_id, ctrl_PC01, ctrl_PC02, ctrl_PC03, ctrl_PC04)],
by = 'sample_id')
draw(plot_clr_heatmap(props_gm, cluster_rows = FALSE, what = 'clr'),
row_dend_width = unit(1, "in"), merge_legend = TRUE)
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
GM layers
Proportions with layers
(plot_propns_layers(props_dt[ matter == "GM" & str_detect(type_broad, 'neuron') ]))Warning: Transformation introduced infinite values in continuous y-axis
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
PCA of proportions, neurons only, all samples
(plot_pca_results(wide_neu, ctrl_pcs_dt, pc_vars, what = "proj"))
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
Patients over PC1
(plot_patients_over_pc(wide_neu, pc_vars))
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
PC variance explained and layer correlations
(plot_pca_loadings(ctrl_pcs_dt, cut_var_exp = cut_var_exp,
cut_layer_cor = cut_layer_cor))
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
ANCOM-BC results
ANCOM-BC standard results
for (nn in names(ancom_ls)) {
cat('#### ', nn, '\n')
print(plot_ancombc_ci(ancom_ls[[nn]], q_cut = 0.05))
cat('\n\n')
}
lesions_GM_3pcs
lesions_NAGM_4pcs
ANCOM-BC standard results, lesions only
for (nn in names(ancom_ls)) {
cat('#### ', nn, '\n')
print(plot_ancombc_ci(ancom_ls[[nn]],
coef_filter = "lesion_type", q_cut = 0.05))
cat('\n\n')
}
lesions_NAGM_4pcs
ANCOM-BC bootstrap results
for (nn in names(boots_ls)) {
cat('#### ', nn, '\n')
print(plot_boots_dt(boots_ls[[nn]], min_effect = 0.2))
cat('\n\n')
}
lesions_NAGM_4pcs
ANCOM-BC bootstrap results, lesions only
for (nn in names(boots_ls)) {
cat('#### ', nn, '\n')
print(plot_boots_dt(boots_ls[[nn]],
coef_filter = "lesion_type", min_effect = 0.2))
cat('\n\n')
}
lesions_NAGM_4pcs
Bootstrap vs standard
for (nn in names(boots_ls)) {
cat('#### ', nn, '\n')
print(plot_boots_vs_standard(boots_ls[[nn]], ancom_ls[[nn]], labels_dt,
q_cut = 0.05, min_effect = 0.2))
cat('\n\n')
}
lesions_NAGM_4pcs
Effect of including PCs, neurons only
for (sel_coef in unique(pcs_coefs_dt$coef)) {
cat('#### ', sel_coef, '\n')
print(plot_effect_of_pcs(sel_coef,
pcs_coefs_dt[ type_broad %in% gm_pc_spec$broad_sel ] ))
cat('\n\n')
}
Effect of including PCs, other celltypes
for (sel_coef in unique(pcs_coefs_dt$coef)) {
cat('#### ', sel_coef, '\n')
print(plot_effect_of_pcs(sel_coef,
pcs_coefs_dt[ !(type_broad %in% gm_pc_spec$broad_sel) ]))
cat('\n\n')
}
Effect of including PCs, lesions only
sel_coefs = c("NAGM", "GML")
cat('#### ', 'neurons only', '\n')neurons only
g = plot_effect_of_pcs(sel_coefs,
pcs_coefs_dt[ type_broad %in% gm_pc_spec$broad_sel ] ) +
labs(title = "Neurons only")
print(g)
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
cat('\n\n')cat('#### ', 'other celltypes', '\n')other celltypes
g = plot_effect_of_pcs(sel_coefs,
pcs_coefs_dt[ !(type_broad %in% gm_pc_spec$broad_sel) ] ) +
labs(title = "Non-neurons only")
print(g)
| Version | Author | Date |
|---|---|---|
| afe32c6 | Macnair | 2021-11-16 |
cat('\n\n')Outputs
if ( !all(file.exists(pb_pcs_ls)) ) {
for (nn in names(pb_f_ls)) {
# which files?
pb_f = pb_f_ls[[ nn ]]
pb_pcs_f = pb_pcs_ls[[ nn ]]
# load full pseudobulk, restrict to just GM
pb_all = pb_f %>% readRDS
pb_pcs = pb_all[, all_pcs_dt$sample_id]
for (v in str_subset(names(all_pcs_dt), "ctrl_PC"))
colData(pb_pcs)[[v]] = all_pcs_dt[[v]] %>% scale %>% `/`(2)
# save
saveRDS(pb_pcs, file = pb_pcs_f)
}
}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 2021-11-18
- Packages -------------------------------------------------------------------
! package * version date lib
abind 1.4-5 2016-07-21 [2]
ade4 1.7-18 2021-09-16 [1]
ANCOMBC * 1.0.5 2021-03-09 [1]
annotate 1.68.0 2020-10-27 [1]
AnnotationDbi 1.52.0 2020-10-27 [1]
ape 5.5 2021-04-25 [1]
assertthat * 0.2.1 2019-03-21 [2]
backports 1.2.1 2020-12-09 [2]
beachmat 2.6.4 2020-12-20 [1]
beeswarm 0.4.0 2021-06-01 [1]
betareg 3.1-4 2021-02-09 [1]
Biobase * 2.50.0 2020-10-27 [1]
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bit 4.0.4 2020-08-04 [2]
bit64 4.0.5 2020-08-30 [2]
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boot 1.3-28 2021-05-03 [2]
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bslib 0.3.1 2021-10-06 [2]
cachem 1.0.6 2021-08-19 [1]
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callr 3.7.0 2021-04-20 [2]
caTools 1.18.2 2021-03-28 [2]
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cluster 2.1.2 2021-04-17 [2]
codetools 0.2-18 2020-11-04 [2]
colorout * 1.2-2 2021-04-15 [1]
colorRamps 2.3 2012-10-29 [1]
colorspace 2.0-2 2021-06-24 [1]
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source
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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_1.22 MASS_7.3-54
[5] phyloseq_1.34.0 ANCOMBC_1.0.5
[7] ica_1.0-2 purrr_0.3.4
[9] nnls_1.4 muscat_1.5.1
[11] DropletUtils_1.10.3 edgeR_3.32.1
[13] limma_3.46.0 googlesheets_0.3.0
[15] scran_1.18.7 uwot_0.1.10
[17] scater_1.18.6 SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0
[21] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[23] IRanges_2.24.1 S4Vectors_0.28.1
[25] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[27] matrixStats_0.61.0 BiocParallel_1.24.1
[29] ggplot.multistats_1.0.0 patchwork_1.1.1
[31] seriation_1.3.1 ComplexHeatmap_2.6.2
[33] SeuratObject_4.0.2 Seurat_4.0.5
[35] conos_1.4.3 igraph_1.2.7
[37] Matrix_1.3-4 readxl_1.3.1
[39] forcats_0.5.1 ggplot2_3.3.5
[41] scales_1.1.1 viridis_0.6.2
[43] viridisLite_0.4.0 assertthat_0.2.1
[45] stringr_1.4.0 data.table_1.14.2
[47] magrittr_2.0.1 circlize_0.4.13
[49] RColorBrewer_1.1-2 BiocStyle_2.18.1
[51] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ps_1.6.0
[3] foreach_1.5.1 lmtest_0.9-38
[5] rprojroot_2.0.2 crayon_1.4.1
[7] spatstat.core_2.3-0 rbibutils_2.2.4
[9] rhdf5filters_1.2.1 Matrix.utils_0.9.8
[11] nlme_3.1-153 backports_1.2.1
[13] rlang_0.4.12 XVector_0.30.0
[15] ROCR_1.0-11 microbiome_1.12.0
[17] irlba_2.3.3 callr_3.7.0
[19] nloptr_1.2.2.2 rjson_0.2.20
[21] bit64_4.0.5 glue_1.4.2
[23] sctransform_0.3.2 processx_3.5.2
[25] pbkrtest_0.5.1 vipor_0.4.5
[27] spatstat.sparse_2.0-0 AnnotationDbi_1.52.0
[29] spatstat.geom_2.3-0 tidyselect_1.1.1
[31] usethis_2.1.2 fitdistrplus_1.1-6
[33] variancePartition_1.20.0 XML_3.99-0.8
[35] tidyr_1.1.4 zoo_1.8-9
[37] xtable_1.8-4 evaluate_0.14
[39] cli_3.0.1 Rdpack_2.1.2
[41] scuttle_1.0.4 zlibbioc_1.36.0
[43] miniUI_0.1.1.1 whisker_0.4
[45] bslib_0.3.1 rpart_4.1-15
[47] betareg_3.1-4 shiny_1.7.1
[49] BiocSingular_1.6.0 xfun_0.27
[51] clue_0.3-60 pkgbuild_1.2.0
[53] multtest_2.46.0 cluster_2.1.2
[55] caTools_1.18.2 TSP_1.1-11
[57] biomformat_1.18.0 tibble_3.1.5
[59] ape_5.5 listenv_0.8.0
[61] Biostrings_2.58.0 png_0.1-7
[63] permute_0.9-5 future_1.22.1
[65] withr_2.4.2 bitops_1.0-7
[67] plyr_1.8.6 cellranger_1.1.0
[69] dqrng_0.3.0 pillar_1.6.4
[71] gplots_3.1.1 GlobalOptions_0.1.2
[73] cachem_1.0.6 fs_1.5.0
[75] flexmix_2.3-17 GetoptLong_1.0.5
[77] DelayedMatrixStats_1.12.3 vctrs_0.3.8
[79] ellipsis_0.3.2 generics_0.1.1
[81] devtools_2.4.2 tools_4.0.5
[83] beeswarm_0.4.0 munsell_0.5.0
[85] DelayedArray_0.16.3 pkgload_1.2.3
[87] fastmap_1.1.0 compiler_4.0.5
[89] abind_1.4-5 httpuv_1.6.3
[91] sessioninfo_1.1.1 plotly_4.10.0
[93] GenomeInfoDbData_1.2.4 gridExtra_2.3
[95] glmmTMB_1.1.2.3 lattice_0.20-45
[97] deldir_1.0-6 utf8_1.2.2
[99] later_1.3.0 dplyr_1.0.7
[101] jsonlite_1.7.2 pbapply_1.5-0
[103] sparseMatrixStats_1.2.1 genefilter_1.72.1
[105] lazyeval_0.2.2 promises_1.2.0.1
[107] doParallel_1.0.16 R.utils_2.11.0
[109] goftest_1.2-3 spatstat.utils_2.2-0
[111] sandwich_3.0-1 rmarkdown_2.11
[113] cowplot_1.1.1 blme_1.0-5
[115] statmod_1.4.36 Rtsne_0.15
[117] HDF5Array_1.18.1 survival_3.2-13
[119] numDeriv_2016.8-1.1 yaml_2.2.1
[121] htmltools_0.5.2 memoise_2.0.0
[123] modeltools_0.2-23 locfit_1.5-9.4
[125] digest_0.6.28 mime_0.12
[127] registry_0.5-1 RSQLite_2.2.8
[129] future.apply_1.8.1 remotes_2.4.1
[131] blob_1.2.2 vegan_2.5-7
[133] R.oo_1.24.0 Formula_1.2-4
[135] splines_4.0.5 Rhdf5lib_1.12.1
[137] Cairo_1.5-12.2 RCurl_1.98-1.5
[139] broom_0.7.9 hms_1.1.1
[141] rhdf5_2.34.0 colorspace_2.0-2
[143] BiocManager_1.30.16 shape_1.4.6
[145] nnet_7.3-16 sass_0.4.0
[147] Rcpp_1.0.7 RANN_2.6.1
[149] fansi_0.5.0 parallelly_1.28.1
[151] R6_2.5.1 ggridges_0.5.3
[153] lifecycle_1.0.1 bluster_1.0.0
[155] minqa_1.2.4 testthat_3.1.0
[157] leiden_0.3.8 jquerylib_0.1.4
[159] snakecase_0.11.0 desc_1.4.0
[161] RcppAnnoy_0.0.19 iterators_1.0.13
[163] TMB_1.7.22 htmlwidgets_1.5.4
[165] beachmat_2.6.4 polyclip_1.10-0
[167] mgcv_1.8-38 globals_0.14.0
[169] leidenAlg_0.1.1 codetools_0.2-18
[171] lubridate_1.8.0 gtools_3.9.2
[173] prettyunits_1.1.1 R.methodsS3_1.8.1
[175] gtable_0.3.0 DBI_1.1.1
[177] git2r_0.28.0 tensor_1.5
[179] httr_1.4.2 highr_0.9
[181] KernSmooth_2.23-20 stringi_1.7.4
[183] progress_1.2.2 reshape2_1.4.4
[185] farver_2.1.0 annotate_1.68.0
[187] hexbin_1.28.2 magick_2.7.3
[189] colorRamps_2.3 sccore_1.0.0
[191] boot_1.3-28 grr_0.9.5
[193] BiocNeighbors_1.8.2 lme4_1.1-27.1
[195] ade4_1.7-18 geneplotter_1.68.0
[197] scattermore_0.7 DESeq2_1.30.1
[199] bit_4.0.4 spatstat.data_2.1-0
[201] janitor_2.1.0 pkgconfig_2.0.3
[203] lmerTest_3.1-3 knitr_1.36