MOFA+ analysis - GM
Will Macnair
Neurogenomics, Neuroscience and Rare Diseases, RocheMarch 06, 2022
Last updated: 2022-03-06
Checks: 4 3
Knit directory: MS_lesions/
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- plot_age_and_duration
- plot_cytokines
- plot_factors_over_mds
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Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms09_ancombc.R')
source('code/ms10_muscat_runs.R')
source('code/ms15_mofa.R')Inputs
# specify what goes into muscat run
meta_f = "data/metadata/metadata_checked_assumptions_2021-10-08.xlsx"
olg_grps_f = 'data/metadata/oligo_groupings.txt'
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
pb_f = file.path(soup_dir, 'pb_gm_w_pcs_sum_broad_2021-11-12.rds')
pb_fine_f = file.path(soup_dir, 'pb_gm_w_pcs_sum_fine_2021-11-12.rds')
soup_f = 'data/ambient/ambient.100UMI.txt'
# get summary QC metrics for each sample
qc_dir = "output/ms03_SampleQC"
qc_f = file.path(qc_dir, "ms_qc_dt.txt")
# define run to load
run_tag = 'run23'
time_stamp = '2021-11-15'
# define files
model_dir = file.path('output/ms10_muscat', run_tag)
muscat_f = '%s/muscat_res_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
anova_f = '%s/muscat_goodness_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
params_f = '%s/muscat_params_%s_%s.rds' %>%
sprintf(model_dir, run_tag, time_stamp)
ranef_dt_f = sprintf('%s/muscat_ranef_dt_%s_%s.txt.gz',
model_dir, run_tag, time_stamp)
mds_sep_f = sprintf('%s/mds_sep_dt_%s_%s.txt.gz',
model_dir, run_tag, time_stamp)Outputs
# where to save
save_dir = 'output/ms15_mofa'
date_tag = '2021-03-04'
if (!dir.exists(save_dir))
dir.create(save_dir)
# file for summary of QC metrics
qc_stats_f = sprintf('%s/qc_stats_by_sample_%s.txt', save_dir, date_tag)
# parameters for gene selection
min_sd = log(1.5)
min_fc = log(1.5)
max_p = 0.01
n_factors = 5
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Excitatory neurons", "Inhibitory neurons",
"Endothelial cells", "Pericytes")
fgsea_cut = 0.1
sel_ps = c('go_bp', 'go_cc', 'go_mf', 'hallmark', 'kegg')
log_p_mad = 2
n_paths = 50
n_cores = 8
# parameters for plotting
min_var = 5
w_cut = 0.2
# checking if metadata can explain factors
formula_str = '~ lesion_type + sex + age_scale + pmi_cat2'
random_var = 'subject_orig'
# output files
mofa_f = sprintf('%s/mofa_%s_%s.hdf5', save_dir, run_tag, date_tag)
fgsea_pat = sprintf('%s/mofa_fgsea_%s_%s_%s.txt',
save_dir, run_tag, '%s', date_tag)
interesting_f = sprintf('%s/mofa_interesting_genes_%s_%s.xlsx',
save_dir, run_tag, date_tag)
# what to use to illustrate random effects concept?
example_cl = "Astrocytes"
example_gs = c("HGF_ENSG00000019991", "OXTR_ENSG00000180914")Load inputs
# load parameters
params = params_f %>% readRDS
# load pseudobulk object
pb = readRDS(params$pb_f) %>% .subset_pb(params$subset_spec) %>%
subset_pb_celltypes(sel_cl) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observed# check for any massive outliers
outliers_dt = calc_log_prop_outliers(pb, mad_cut = log_p_mad)the following samples have half or more of celltypes with very extreme
(2 > MADs) log proportions:
EU005, EU044ok_samples = outliers_dt[ props_ok == TRUE ]$sample_id
pb = pb[ , ok_samples ]
# load other useful things
labels_dt = .load_labels_dt(labels_f, params$cluster_var)
magma_dt = .load_magma_dt(magma_f, pb)
tfs_dt = .load_tfs_dt(tfs_f, pb)
lof_dt = .load_lof_dt(lof_f, pb)
# load annotations
annots_dt = .get_cols_dt(pb) %>%
.[, sample := sample_id ] %>% .[, group := 'single_group'] %>%
.[, .(sample, sample_id, diagnosis, lesion_type, subject_id, subject_orig,
sample_source, age = age_at_death, age_at_death, age_scale,
years_w_ms, sex, pmi_cat, pmi_cat2, smoker )]
# annots_dt = add_oligo_groups(annots_dt, olg_grps_f)
# get random effects
ranef_dt = .load_ranef_dt(ranef_dt_f, labels_dt, pb)
# get results
res_dt = muscat_f %>% fread %>%
.load_muscat_results(labels_dt, params) %>%
.[, .(cluster_id, gene_id, symbol, var_type, coef, test_var,
logCPM, mean_soup, padj = p_adj.soup, logFC)] %>%
.[ !is.na(padj) ]
# get anova results
anova_dt = .load_anova_dt(anova_f, res_dt) %>%
.[ is.na(full), full := 1 ]
# get MDS outputs
mds_sep_dt = mds_sep_f %>% fread
if (params$cluster_var == 'type_broad')
mds_sep_dt[, cluster_id := factor(cluster_id, levels = broad_ord)]# get random effects
sd_dt = ranef_dt %>% calc_ranef_melt %>% calc_sd_dt
filter_dt = calc_filter_dt(res_dt, sd_dt, pb, anova_dt,
max_p = max_p, min_sd = min_sd, min_fc = min_fc)
filtered_dt = filter_dt[ ( (ms_signif == 'signif') & (ms_effect == 'big') ) |
( (pt_signif == 'signif') & (pt_variab == 'variable')) ] %>%
.[ cluster_id %in% sel_cl ] %>%
.[, is_ms := ifelse(ms_effect == "big" & ms_signif == "signif", "ms", "not") ] %>%
.[, is_pt := ifelse(pt_signif == "signif" & pt_variab == "variable", "pt", "not") ]
# check what we've got
filtered_dt[, .N, by = .(cluster_id, is_ms, is_pt)] %>%
.[, total := sum( N ), by = cluster_id ] %>%
dcast.data.table(cluster_id + total ~ is_ms + is_pt, fill = 0, value.var = "N") cluster_id total ms_not ms_pt not_pt
1: OPCs / COPs 187 34 4 149
2: Oligodendrocytes 531 254 22 255
3: Astrocytes 721 176 18 527
4: Microglia 385 71 21 293
5: Excitatory neurons 952 530 19 403
6: Inhibitory neurons 597 400 4 193
7: Endothelial cells 582 117 62 403
8: Pericytes 298 135 47 116n_cells_dt = calc_n_cells_dt(pb_fine_f, annots_dt, sel_cl)soup_dt = get_soup_logcpms(soup_f, pb)qc_stats = calc_qc_stats_by_sample(qc_stats_f, qc_dir, qc_f,
meta_f, labels_f, labelled_f)Processing / calculations
message("which genes have strong layer associations? (FDR < 1%)")which genes have strong layer associations? (FDR < 1%)layer_fits = calc_layer_fits(pb, filtered_dt, sel_cl, params)
layer_fits[ fdr < 0.01 ] %>% .[ order(fdr) ] %>%
.[, .(celltype = view, pc = coef, symbol, coef = estimate %>% round(2),
log10_p = fdr %>% log10 %>% round(2)) ] %>% print celltype pc symbol coef log10_p
1: inhib ctrl_PC01 GCNT2 0.65 -5.88
2: excit ctrl_PC01 JAG1 0.79 -5.37
3: excit ctrl_PC01 ITGA4 0.76 -5.11
4: excit ctrl_PC01 CHRM2 -0.70 -4.58
5: excit ctrl_PC01 ITGA11 -0.90 -4.25
6: excit ctrl_PC01 NPFFR2 -0.97 -4.25
7: excit ctrl_PC01 GPRIN3 -0.75 -4.22
8: excit ctrl_PC01 RASGRF2 0.55 -4.22
9: excit ctrl_PC01 CDH9 0.57 -3.85
10: excit ctrl_PC01 PTGIS -0.58 -3.64
11: excit ctrl_PC01 NXPH2 -0.88 -3.59
12: excit ctrl_PC01 AC068286.1 0.49 -3.50
13: excit ctrl_PC01 LTK 0.69 -3.16
14: inhib ctrl_PC01 CD36 0.66 -3.11
15: excit ctrl_PC01 IGFBP4 0.63 -3.11
16: excit ctrl_PC01 QRFPR -0.57 -3.11
17: excit ctrl_PC01 RAB7B 0.67 -3.11
18: inhib ctrl_PC01 TMEM196 0.52 -3.11
19: excit ctrl_PC01 C10orf67 0.40 -2.88
20: excit ctrl_PC01 PDZD2 0.55 -2.85
21: excit ctrl_PC01 GALNTL6 0.56 -2.68
22: excit ctrl_PC01 STEAP3 0.42 -2.66
23: excit ctrl_PC01 TBL1X -0.51 -2.66
24: excit ctrl_PC01 TRMT9B -0.50 -2.66
25: excit ctrl_PC01 PRLR -0.55 -2.63
26: inhib ctrl_PC01 RGS12 0.42 -2.63
27: excit ctrl_PC01 CBLN2 0.58 -2.63
28: inhib ctrl_PC01 LINC02408 0.60 -2.61
29: inhib ctrl_PC01 TRIM36 0.47 -2.61
30: excit ctrl_PC01 ERG -0.60 -2.57
31: excit ctrl_PC01 AC008415.1 -0.82 -2.55
32: excit ctrl_PC01 AC010266.2 0.48 -2.55
33: inhib ctrl_PC01 SCN5A 0.49 -2.55
34: excit ctrl_PC02 LINC02232 -0.74 -2.52
35: excit ctrl_PC01 AC016687.2 -0.87 -2.50
36: excit ctrl_PC01 CLMP -0.71 -2.50
37: inhib ctrl_PC01 GNG12 0.57 -2.50
38: excit ctrl_PC01 KANK2 -0.30 -2.50
39: inhib ctrl_PC01 NR2F2 0.59 -2.50
40: excit ctrl_PC01 SOWAHA 0.49 -2.50
41: excit ctrl_PC01 DIAPH2 0.32 -2.49
42: inhib ctrl_PC01 NR2E1 0.57 -2.49
43: inhib ctrl_PC01 RXRG 0.84 -2.47
44: excit ctrl_PC01 TEK 0.63 -2.42
45: excit ctrl_PC01 U95743.1 0.51 -2.41
46: inhib ctrl_PC01 INPP4B 0.41 -2.38
47: inhib ctrl_PC01 SYT10 0.66 -2.38
48: excit ctrl_PC02 JAG1 0.55 -2.36
49: excit ctrl_PC01 LINC00390 0.52 -2.31
50: excit ctrl_PC01 NHSL2 0.56 -2.31
51: excit ctrl_PC01 ROBO3 -0.48 -2.31
52: excit ctrl_PC01 EPHA6 0.53 -2.28
53: excit ctrl_PC02 LYPD6B -0.69 -2.28
54: excit ctrl_PC01 AP001999.1 0.66 -2.28
55: excit ctrl_PC01 ZNF608 0.47 -2.28
56: excit ctrl_PC01 MAN1A1 0.44 -2.27
57: inhib ctrl_PC01 CCDC71L 0.43 -2.26
58: excit ctrl_PC01 ART3 0.45 -2.22
59: inhib ctrl_PC01 PTGFR 0.46 -2.19
60: inhib ctrl_PC01 POU3F4 0.42 -2.18
61: inhib ctrl_PC01 CALB2 0.65 -2.16
62: excit ctrl_PC01 CERKL 0.44 -2.16
63: excit ctrl_PC01 GPR83 0.67 -2.16
64: excit ctrl_PC03 LRIG3 -0.65 -2.16
65: inhib ctrl_PC01 PROX1 0.49 -2.16
66: excit ctrl_PC01 RET -0.56 -2.16
67: inhib ctrl_PC01 SYT17 0.38 -2.05
68: excit ctrl_PC01 WTIP 0.40 -2.03
69: excit ctrl_PC03 CXCL13 -0.52 -2.02
celltype pc symbol coef log10_pmofa_obj = make_mofa_obj_samples_regress_layers(pb, filtered_dt,
sel_cl, params)Creating MOFA object from a data.frame...# set up
data_opts = get_default_data_options(mofa_obj)
model_opts = get_default_model_options(mofa_obj)
train_opts = get_default_training_options(mofa_obj)
# specify how many factors
model_opts$num_factors = n_factors
# train mofa
mofa_obj = prepare_mofa(
object = mofa_obj,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts
)Checking data options...Checking training options...Checking model options...model = run_mofa(mofa_obj, mofa_f)Warning: Output file output/ms15_mofa/mofa_run23_2021-03-04.hdf5 already exists, it will be replacedConnecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)...
Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'Warning in .quality_control(object, verbose = verbose): Factor(s) 2 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.# update metadata
model = add_metadata(model, annots_dt)
# put weights and scores in MS order
model = put_model_in_ms_order(model)var_exp_dt = get_variance_explained(model, as.data.frame = TRUE) %>%
as.data.table %>%
.[, .(
view = r2_per_factor.view %>% factor(levels = broad_short),
factor = r2_per_factor.factor,
var_exp = r2_per_factor.value
)]
to_plot_dt = var_exp_dt[ var_exp > min_var ] %>% .[order(factor, -var_exp)]# get weights, define expected files
ws_dt = extract_weights(model, sd_dt)
fgsea_fs = sapply(sel_ps, function(p) sprintf(fgsea_pat, p))
# if necessary, run FGSEA
if (all(file.exists(fgsea_fs))) {
gsea_list = lapply(fgsea_fs, fread)
} else {
# do fgsea for these
bpparam = MulticoreParam(workers = n_cores,
progressbar = TRUE, tasks = 50)
bpstart()
gsea_list = calc_mofa_fgsea(paths_list[ sel_ps ], ws_dt, fgsea_pat, fgsea_cut, bpparam)
bpstop()
}
# restrict to interesting ones
gsea_main = gsea_list %>% map( ~.x[ main_path == TRUE ]) %>% rbindlist
# show what we found
gsea_main[, .(factor, view, path_set, pathway = pathway %>% tolower %>%
str_extract("(?<=(hallmark|gobp|gocc|gomf|kegg)_).+"),
log10_p = log10(padj) %>% round(2), NES = round(NES, 2))] %>%
.[ order(factor, NES) ] %>% print factor view path_set pathway log10_p
1: Factor1 excit kegg parkinsons_disease -5.75
2: Factor1 endo go_bp response_to_type_i_interferon -3.44
3: Factor1 inhib go_cc mitochondrion -3.75
4: Factor1 endo go_bp defense_response_to_virus -3.44
5: Factor1 endo hallmark interferon_alpha_response -4.19
---
528: Factor5 micro go_bp homeostasis_of_number_of_cells -1.02
529: Factor5 micro go_bp intrinsic_apoptotic_signaling_pathway -1.10
530: Factor5 micro go_bp regulation_of_myeloid_cell_differentiation -1.10
531: Factor5 micro go_bp chemokine_production -1.10
532: Factor5 micro go_bp myeloid_cell_differentiation -1.10
NES
1: -2.65
2: -2.63
3: -2.57
4: -2.54
5: -2.41
---
528: 2.01
529: 2.02
530: 2.08
531: 2.10
532: 2.11r2_dt = calc_r2_for_factors(model, annots_dt, formula_str, random_var)
anova_dt = calc_lrts(model, annots_dt, formula_str, random_var)Analysis
muscat results vs SD
for (what in c('log10_padj', 'log2FC')) {
cat('### ', what, '\n', sep = '')
print(plot_muscat_vs_sd(res_dt, sd_dt, NULL, what = what))
cat('\n\n')
}log10_padj
log2FC
Cytokine effects
cyto_gs = unique(res_dt$gene_id) %>% str_subset('(^IL[0-9]+|^CCL|^CXCL|^IFN|^TGF|^TNF|^CSF)')
(plot_muscat_vs_sd_min(res_dt[ gene_id %in% cyto_gs ], sd_dt[ gene_id %in% cyto_gs ],
sel_cl, min_sd, max_p, do_labels = TRUE))Ages vs duration of MS
(plot_age_duration(annots_dt))
Data overview
(plot_data_overview(mofa_obj))Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.
Overlapping genes
cat('### All genes\n')All genes
print(plot_gene_overlap(model))
cat('\n\n')for (sel_f in factors_names(model)) {
cat('### Genes in ', sel_f, '\n', sep = '')
suppressWarnings(print(plot_gene_overlap(model, sel_f = sel_f, w_cut = w_cut)))
cat('\n\n')
}Genes in Factor1
Genes in Factor2
Genes in Factor3
Genes in Factor4
Genes in Factor5
Overlapping genes (proportions)
source('code/ms15_mofa.R')
cat('### All genes\n')All genes
suppressWarnings(print(plot_gene_overlap(model, what = 'prop')))
cat('\n\n')for (sel_f in factors_names(model)) {
cat('### Genes in ', sel_f, '\n', sep = '')
suppressWarnings(print(plot_gene_overlap(model, what = 'prop',
sel_f = sel_f, w_cut = w_cut)))
cat('\n\n')
}Genes in Factor1
Genes in Factor2
Genes in Factor3
Genes in Factor4
Genes in Factor5
Factor distributions
for (annot in c('lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker')) {
cat('### by ', annot, '\n', sep = '')
print(plot_factors_univariate(model, annots_dt, pb, by = annot))
cat('\n\n')
}by lesion_type
by diagnosis
by sex
by sample_source
by smoker
Factor distributions - pairwise
for (annot in c('subject_id', 'lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker')) {
cat('### by ', annot, '\n', sep = '')
print(plot_factors_pairwise(model, annots_dt, pb, by = annot))
cat('\n\n')
}by subject_id
by lesion_type
by diagnosis
by sex
by sample_source
by smoker
Factors over MDS layouts
for (cl in broad_ord) {
if (!(broad_short[[cl]] %in% views_names(model)))
next
cat('### ', cl, '\n', sep = '')
print(plot_factors_over_mds_samples(model, mds_sep_dt, cl = cl))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia
Excitatory neurons
Inhibitory neurons
Endothelial cells
Pericytes
Factor distributions with patient annotations - few
for (v in c('score', 'score_scaled')) {
cat('### ', v, '\n', sep = '')
draw(plot_factors_heatmap(model, annots_dt, pb, what = 'few', plot_var = v))
cat('\n\n')
}score
score_scaled
Factor distributions with patient annotations - all
for (v in c('score', 'score_scaled')) {
cat('### ', v, '\n', sep = '')
draw(plot_factors_heatmap(model, annots_dt, pb, what = 'all', plot_var = v))
cat('\n\n')
}score
score_scaled
Factor distributions with patient annotations - with QC
draw(plot_factors_heatmap_w_qc(model, annots_dt, pb, qc_stats))
Coefficients of top genes (by factor)
for (f in factors_names(model)) {
cat('### ', f, '\n', sep = '')
draw(plot_top_weights_heatmap_by_factor(model, var_exp_dt, sel_f = f))
cat('\n\n')
}Factor1
Factor2
Factor3
Factor4
Factor5
Expression of top genes per celltype
# iterate plots
for (i in seq.int(nrow(to_plot_dt))) {
sel_v = as.character(to_plot_dt[i]$view)
sel_f = to_plot_dt[i]$factor
this_r2 = to_plot_dt[i]$var_exp
cat('### ', sel_v, '-F', as.integer(sel_f),
' (', round(this_r2, 0), '%)', '\n', sep = '')
draw(plot_top_weights_expression_sample(model, pb, annots_dt, filter_dt,
tfs_dt, sel_v = sel_v, sel_f = sel_f, n_top = 40, is_regressed = TRUE),
merge_legend = TRUE )
cat('\n\n')
}excit-F1 (21%)
endo-F1 (19%)
peri-F1 (17%)
micro-F1 (17%)
inhib-F1 (15%)
astro-F1 (14%)
oligo-F1 (13%)
opc_cop-F1 (8%)
inhib-F2 (34%)
excit-F2 (32%)
opc_cop-F2 (10%)
astro-F2 (9%)
oligo-F2 (7%)
endo-F2 (6%)
micro-F2 (6%)
peri-F2 (5%)
opc_cop-F3 (15%)
micro-F3 (12%)
astro-F3 (10%)
peri-F3 (7%)
endo-F3 (5%)
oligo-F4 (7%)
endo-F4 (6%)
peri-F4 (5%)
endo-F5 (10%)
Expression of top genes across all factors per celltype
# iterate plots
for (sel_v in broad_short[sel_cl]) {
cat('### ', sel_v, '\n', sep = '')
draw(plot_top_genes_expression_all_factors(model, pb, annots_dt, filter_dt,
tfs_dt, var_exp_dt, sel_v = sel_v, n_top = 10, min_var, is_regressed = TRUE),
merge_legend = TRUE )
cat('\n\n')
}opc_cop
oligo
astro
micro
excit
inhib
endo
peri
Factors vs number of cells
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_n_cells(model, n_cells_dt, sel_f = f))
cat('\n\n')
}Factor1
Factor2
Factor3
Factor4
Factor5
Factors vs top genes
for (f in factors_names(model)) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_logcpm(model, annots_dt, sel_f = f))
cat('\n\n')
}Factor1
Factor2
Factor3
Factor4
Factor5
Factors vs top genes - soup
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_soup_logcpm(model, annots_dt, soup_dt,
sel_f = f, trans = 'linear'))
cat('\n\n')
}Factor1
Factor2
Factor3
Factor4
Factor5
Distributions of factor weights
(plot_mofa_weights(model))
Factor weights vs muscat results
for (what in c('log10_padj', 'log2FC')) {
cat('### ', what, '\n', sep = '')
print(plot_muscat_vs_mofa(model, filter_dt, what = what))
cat('\n\n')
}log10_padj
log2FC
Correlations between factor weights - split by celltype
for (v in broad_short[sel_cl]) {
cat('### ', v, '\n', sep = '')
print(plot_factor_weight_corrs(model, v, by = 'type', how = 'bin'))
cat('\n\n')
}opc_cop
oligo
astro
micro
excit
inhib
endo
peri
Correlations between factor weights - split by factor
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_factor_weight_corrs(model, f, by = 'factor', how = 'point'))
cat('\n\n')
}Factor1
Factor2
Factor3
Factor4
Factor5
Variance explained
(plot_var_exp(var_exp_dt))
GSEA for factors
for (p in names(gsea_list)) {
# restrict to relevant GO terms
cat('### ', p, '\n', sep='')
dt = gsea_list[[p]]
if (nrow(dt[ main_path == TRUE ]) == 0)
next
# plot
print(plot_mofa_gsea_dotplot(dt, labels_dt,
fgsea_cut = fgsea_cut, n_total = 60))
cat('\n\n')
}go_bp
go_cc
go_mf
hallmark
kegg
Outputs
Top filter genes
# merge filtered and weights
xls_dt = calc_xls_dt(model, filtered_dt)
# save outputs
write_xlsx(list(mofa_weights = xls_dt), path = interesting_f)Figures
Illustrative example
for (g in example_gs) {
cat('### ', str_extract(g, '^[^_]+'), '\n', sep = '')
suppressWarnings(print(plot_ranef_example(pb, example_cl, g)))
cat('\n\n')
}HGF
OXTR
Selection of interesting genes
for (what in c('fc_vs_sd_all', 'fc_vs_sd_signif', 'ms_p_vs_lrt_p')) {
cat("### ", what, "\n")
print(plot_ms_vs_random(filter_dt, sel_cl, max_p, min_fc, min_sd, what = what))
cat("\n\n")
}fc_vs_sd_all
fc_vs_sd_signif
ms_p_vs_lrt_p
muscat results vs SD, MAGMA hits only
magma_hits = magma_dt[ p_magma_adj < 0.1 ]$gene_id
(plot_muscat_vs_sd_min(res_dt[ gene_id %in% magma_hits ], sd_dt,
sel_cl, min_sd, max_p))muscat results vs LoFs
(plot_muscat_and_sd_vs_lof(res_dt, sd_dt, lof_dt, sel_cl))
Expression heatmaps
Some notes:
- pca has both rows and columns ordered in a sensible data-driven way.
- clustered has the rows clustered by hierarchical clustering, and the columns the same as pca.
- three_per_patient is the same as clustered but only showing patients where we have >=3 samples.
- by_lesion has the rows ordered by lesion type, and the columns ordered by MS logFC (hopefully this shows the horseshoe a bit).
- FactorX has the rows ordered by each sample’s factor score, and the columns ordered by each gene’s factor weight; I also exclude genes with small weights for that factor.
- is_shared on top of the heatmap indicates whether a gene is unique to the celltype, or was also selected for another celltype.
for (o in c("pca", "clustered", "three_per_patient", "by_lesion", factors_names(model))) {
cat("### ", o, "\n")
draw(plot_expression_heatmap_samples(pb, filtered_dt, annots_dt, sel_cl,
model, ordering = o)
, merge_legend = TRUE)
cat("\n\n")
}pca
clustered
three_per_patient
by_lesion
Factor1
Factor2
Factor3
Factor4
Factor5
MOFA+ factors - diagnosis
(plot_factors_univariate(model, annots_dt, pb, by = 'diagnosis'))
MOFA+ factors - lesions
(plot_factors_univariate(model, annots_dt, pb, by = 'lesion_type'))
Factor 1 vs Factor 2
for (what in c("diagnosis", "lesion_type", "subject_id")) {
cat('### ', what, '\n', sep = '')
print(plot_factors_pair(model, annots_dt, pb,
f_pair = c("Factor2", "Factor1"), by = what))
cat('\n\n')
}diagnosis
lesion_type
subject_id
Interactions between factors and model components
(plot_factor_r2s(r2_dt, var_exp_dt))
Does metadata explain factors?
(plot_factor_anovas(anova_dt))
GO terms for factors
print(plot_mofa_gsea_dotplot(gsea_list[['go_bp']], labels_dt,
fgsea_cut = fgsea_cut, n_total = 50))
Top genes for Factor 1
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor1',
min_var = 10, min_w = 0.2, n_top = 10, is_regressed = TRUE) )
Top genes for Factor 2
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor2',
min_var = 10, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
Top genes for Factor 3
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor3',
min_var = 10, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
Top genes for Factor 4
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor4',
min_var = 5, min_w = 0.2, n_top = 10, is_regressed = TRUE) )
Top genes for Factor 5
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor5',
min_var = 5, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
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 2022-03-04
- Packages -------------------------------------------------------------------
package * version date lib
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]
basilisk 1.2.1 2020-12-16 [1]
basilisk.utils 1.2.2 2021-01-27 [1]
beachmat 2.6.4 2020-12-20 [1]
beeswarm 0.4.0 2021-06-01 [1]
Biobase * 2.50.0 2020-10-27 [1]
BiocGenerics * 0.36.1 2021-04-16 [1]
BiocManager 1.30.16 2021-06-15 [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]
bit 4.0.4 2020-08-04 [2]
bit64 4.0.5 2020-08-30 [2]
bitops 1.0-7 2021-04-24 [2]
blme 1.0-5 2021-01-05 [1]
blob 1.2.2 2021-07-23 [2]
boot 1.3-28 2021-05-03 [2]
broom 0.7.9 2021-07-27 [2]
bslib 0.3.1 2021-10-06 [2]
cachem 1.0.6 2021-08-19 [1]
Cairo 1.5-12.2 2020-07-07 [2]
callr 3.7.0 2021-04-20 [2]
caTools 1.18.2 2021-03-28 [2]
cellranger 1.1.0 2016-07-27 [2]
circlize * 0.4.13 2021-06-09 [1]
cli 3.0.1 2021-07-17 [1]
clue 0.3-60 2021-10-11 [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]
colorRamps 2.3 2012-10-29 [1]
colorspace 2.0-2 2021-06-24 [1]
ComplexHeatmap * 2.6.2 2020-11-12 [1]
corrplot 0.90 2021-06-30 [1]
cowplot 1.1.1 2020-12-30 [2]
crayon 1.4.1 2021-02-08 [2]
data.table * 1.14.2 2021-09-27 [2]
DBI 1.1.1 2021-01-15 [2]
dbplyr 2.1.1 2021-04-06 [2]
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desc 1.4.0 2021-09-28 [1]
DESeq2 1.30.1 2021-02-19 [1]
devtools 2.4.2 2021-06-07 [1]
digest 0.6.28 2021-09-23 [2]
doParallel 1.0.16 2020-10-16 [1]
dplyr * 1.0.7 2021-06-18 [2]
edgeR * 3.32.1 2021-01-14 [1]
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farver 2.1.0 2021-02-28 [2]
fastmap 1.1.0 2021-01-25 [2]
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fs 1.5.0 2020-07-31 [2]
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future.apply 1.8.1 2021-08-10 [2]
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geneplotter 1.68.0 2020-10-27 [1]
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gtools 3.9.2 2021-06-06 [2]
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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]
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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 4.0.5 (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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] rgl_0.107.14 MOFA2_1.0.1
[3] rmarkdown_2.11 writexl_1.4.0
[5] ComplexHeatmap_2.6.2 fgsea_1.16.0
[7] tictoc_1.0.1 performance_0.8.0
[9] edgeR_3.32.1 limma_3.46.0
[11] reshape2_1.4.4 scater_1.18.6
[13] Matrix.utils_0.9.8 Matrix_1.3-4
[15] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[17] Biobase_2.50.0 MatrixGenerics_1.2.1
[19] matrixStats_0.61.0 seriation_1.3.1
[21] UpSetR_1.4.0 BiocParallel_1.24.1
[23] muscat_1.5.1 dplyr_1.0.7
[25] readr_2.0.2 tidyr_1.1.4
[27] tibble_3.1.5 tidyverse_1.3.1
[29] rtracklayer_1.50.0 GenomicRanges_1.42.0
[31] GenomeInfoDb_1.26.7 IRanges_2.24.1
[33] S4Vectors_0.28.1 BiocGenerics_0.36.1
[35] ggbeeswarm_0.6.0 ggrepel_0.9.1
[37] reticulate_1.22 MASS_7.3-54
[39] phyloseq_1.34.0 ANCOMBC_1.0.5
[41] purrr_0.3.4 patchwork_1.1.1
[43] readxl_1.3.1 forcats_0.5.1
[45] ggplot2_3.3.5 scales_1.1.1
[47] viridis_0.6.2 viridisLite_0.4.0
[49] assertthat_0.2.1 stringr_1.4.0
[51] data.table_1.14.2 magrittr_2.0.1
[53] circlize_0.4.13 RColorBrewer_1.1-2
[55] BiocStyle_2.18.1 colorout_1.2-2
[57] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 R.methodsS3_1.8.1
[3] bit64_4.0.5 knitr_1.36
[5] R.utils_2.11.0 irlba_2.3.3
[7] DelayedArray_0.16.3 RCurl_1.98-1.5
[9] doParallel_1.0.16 generics_0.1.1
[11] callr_3.7.0 cowplot_1.1.1
[13] microbiome_1.12.0 usethis_2.1.2
[15] RSQLite_2.2.8 future_1.22.1
[17] bit_4.0.4 tzdb_0.1.2
[19] xml2_1.3.2 lubridate_1.8.0
[21] httpuv_1.6.3 xfun_0.27
[23] hms_1.1.1 jquerylib_0.1.4
[25] evaluate_0.14 promises_1.2.0.1
[27] TSP_1.1-11 fansi_0.5.0
[29] progress_1.2.2 caTools_1.18.2
[31] dbplyr_2.1.1 htmlwidgets_1.5.4
[33] igraph_1.2.7 DBI_1.1.1
[35] geneplotter_1.68.0 ellipsis_0.3.2
[37] corrplot_0.90 backports_1.2.1
[39] insight_0.14.5 permute_0.9-5
[41] annotate_1.68.0 sparseMatrixStats_1.2.1
[43] vctrs_0.3.8 remotes_2.4.1
[45] here_1.0.1 Cairo_1.5-12.2
[47] cachem_1.0.6 withr_2.4.2
[49] grr_0.9.5 sctransform_0.3.2
[51] vegan_2.5-7 GenomicAlignments_1.26.0
[53] prettyunits_1.1.1 cluster_2.1.2
[55] ape_5.5 crayon_1.4.1
[57] basilisk.utils_1.2.2 genefilter_1.72.1
[59] labeling_0.4.2 pkgconfig_2.0.3
[61] pkgload_1.2.3 nlme_3.1-153
[63] vipor_0.4.5 devtools_2.4.2
[65] blme_1.0-5 rlang_0.4.12
[67] globals_0.14.0 lifecycle_1.0.1
[69] filelock_1.0.2 registry_0.5-1
[71] modelr_0.1.8 rsvd_1.0.5
[73] cellranger_1.1.0 rprojroot_2.0.2
[75] Rhdf5lib_1.12.1 boot_1.3-28
[77] reprex_2.0.1 beeswarm_0.4.0
[79] processx_3.5.2 pheatmap_1.0.12
[81] GlobalOptions_0.1.2 png_0.1-7
[83] rjson_0.2.20 bitops_1.0-7
[85] R.oo_1.24.0 KernSmooth_2.23-20
[87] rhdf5filters_1.2.1 Biostrings_2.58.0
[89] blob_1.2.2 DelayedMatrixStats_1.12.3
[91] shape_1.4.6 parallelly_1.28.1
[93] beachmat_2.6.4 memoise_2.0.0
[95] plyr_1.8.6 gplots_3.1.1
[97] zlibbioc_1.36.0 compiler_4.0.5
[99] clue_0.3-60 lme4_1.1-27.1
[101] DESeq2_1.30.1 snakecase_0.11.0
[103] Rsamtools_2.6.0 cli_3.0.1
[105] ade4_1.7-18 XVector_0.30.0
[107] lmerTest_3.1-3 listenv_0.8.0
[109] ps_1.6.0 TMB_1.7.22
[111] mgcv_1.8-38 tidyselect_1.1.1
[113] stringi_1.7.4 highr_0.9
[115] yaml_2.2.1 BiocSingular_1.6.0
[117] locfit_1.5-9.4 sass_0.4.0
[119] fastmatch_1.1-3 tools_4.0.5
[121] future.apply_1.8.1 rstudioapi_0.13
[123] foreach_1.5.1 git2r_0.28.0
[125] janitor_2.1.0 gridExtra_2.3
[127] farver_2.1.0 Rtsne_0.15
[129] digest_0.6.28 BiocManager_1.30.16
[131] Rcpp_1.0.7 broom_0.7.9
[133] scuttle_1.0.4 later_1.3.0
[135] httr_1.4.2 AnnotationDbi_1.52.0
[137] Rdpack_2.1.2 colorspace_2.0-2
[139] rvest_1.0.2 XML_3.99-0.8
[141] fs_1.5.0 splines_4.0.5
[143] uwot_0.1.10 basilisk_1.2.1
[145] multtest_2.46.0 sessioninfo_1.1.1
[147] xtable_1.8-4 jsonlite_1.7.2
[149] nloptr_1.2.2.2 testthat_3.1.0
[151] R6_2.5.1 pillar_1.6.4
[153] htmltools_0.5.2 glue_1.4.2
[155] fastmap_1.1.0 minqa_1.2.4
[157] BiocNeighbors_1.8.2 codetools_0.2-18
[159] pkgbuild_1.2.0 utf8_1.2.2
[161] lattice_0.20-45 bslib_0.3.1
[163] numDeriv_2016.8-1.1 pbkrtest_0.5.1
[165] colorRamps_2.3 gtools_3.9.2
[167] magick_2.7.3 survival_3.2-13
[169] glmmTMB_1.1.2.3 desc_1.4.0
[171] biomformat_1.18.0 munsell_0.5.0
[173] GetoptLong_1.0.5 rhdf5_2.34.0
[175] GenomeInfoDbData_1.2.4 iterators_1.0.13
[177] HDF5Array_1.18.1 variancePartition_1.20.0
[179] haven_2.4.3 gtable_0.3.0
[181] rbibutils_2.2.4