MS: Double-checking selected markers
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandAugust 26, 2021
Last updated: 2021-08-26
Checks: 4 3
Knit directory: MS_lesions/
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- calc_conos_on_panel
- calc_expression_cartana
- calc_expression_roche
- calc_median_counts_cartana
- calc_median_counts_roche
- calc_muscat_on_clusters
- calc_n_expressed
- calc_random_forest
- load_agreed_markers
- load_conos
- load_micro_macro_markers
- load_pseudobulk
- load_qc
- load_umap_many_dt
- plot_astro_dotplot
- plot_classifier_heatmaps
- plot_dotplot_broad
- plot_dotplot_dheeraj
- plot_dotplot_dheeraj_compact
- plot_dotplot_fine
- plot_dotplot_micro_ma
- plot_gsea_results_all
- plot_gsea_results_ctrl
- plot_marker_heatmaps_cartana
- plot_marker_heatmaps_roche
- plot_muscat_dotplot
- plot_muscat_dotplot_all
- plot_muscat_dotplot_ctrl
- plot_muscat_results_all
- plot_muscat_results_ctrl
- plot_n_expressed_distributions
- plot_oligo_dotplot
- plot_panel_conos_clusters
- plot_panel_conos_umap_densities
- plot_panel_conos_umaps
- plot_pb_qc
- plot_umap_final_celltypes
- plot_umap_final_celltypes_sel
- plot_umap_final_celltypes_sel_broad
- save_outputs
- session_info
- session-info-chunk-inserted-by-workflowr
- setup_input
- setup_outputs
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6f577cc | Macnair | 2021-08-24 | Tweak dotplot in ms12_markers |
| html | 6f577cc | Macnair | 2021-08-24 | Tweak dotplot in ms12_markers |
| Rmd | f60e5c1 | Macnair | 2021-08-03 | Add astrocyte layer genes to ms12_markers |
| html | f60e5c1 | Macnair | 2021-08-03 | Add astrocyte layer genes to ms12_markers |
| Rmd | c20e65d | Macnair | 2021-07-02 | Add UMAP annotated with fine celltypes to ms12_markers |
| html | 0ec42e1 | Macnair | 2021-06-20 | Update GSEA analysis, heatmaps, lesion-specific |
| html | be76f21 | Macnair | 2021-06-10 | Add Dheeraj’s genes to dotplots |
| Rmd | 0ee7fc4 | Macnair | 2021-06-08 | Add Dheeraj’s genes to ms12_markers |
| html | 0ee7fc4 | Macnair | 2021-06-08 | Add Dheeraj’s genes to ms12_markers |
| Rmd | 1ea21aa | Macnair | 2021-06-03 | Updated marker analysis |
| html | 1ea21aa | Macnair | 2021-06-03 | Updated marker analysis |
| Rmd | 768da0e | Macnair | 2021-05-27 | Marker panel validation |
| html | 768da0e | Macnair | 2021-05-27 | Marker panel validation |
| Rmd | 58205c2 | Macnair | 2021-05-21 | Update with random effects and markers |
| html | 58205c2 | Macnair | 2021-05-21 | Update with random effects and markers |
| html | 9852840 | Macnair | 2021-05-12 | Adding docs to repo for first time - massive! |
Setup / definitions
Libraries
Helper functions
source('code/ms00_utils.R')
source('code/ms03_SampleQC.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')
source('code/ms10_muscat_runs.R')
source('code/ms12_markers.R')Inputs
# base inputs
sce_f = 'data/sce_raw/ms_sce.rds'
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'
# qc info
qc_dir = 'output/ms03_SampleQC'
qc_f = file.path(qc_dir, 'ms_qc_dt.txt')
# define hand-selected marker location
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")
roche_f = paste0("data/byhand_markers/",
"Copy of Copy of Marker_selection_for_validation_MS_snucseq_30102020",
" Ediinburgh.xlsx - analysis_Roche_April2021.csv")
# umap embedding options
umap_many_f = 'output/ms04_conos/conos_umap_sub_2021-02-11.txt'
# define pseudobulk files
soup_dir = 'output/ms07_soup'
date_soup = '2021-06-01'
pb_fine_f = sprintf('%s/pb_sum_fine_%s.rds', soup_dir, date_soup)
prop_fine_f = sprintf('%s/pb_prop_fine_%s.rds', soup_dir, date_soup)
pb_broad_f = file.path(soup_dir, 'pb_sum_broad_2021-06-01.rds')
pb_soup_f = file.path(soup_dir, 'pb_soup_broad_maximum_2021-06-01.rds')Outputs
# set up directory
save_dir = 'output/ms12_markers'
date_tag = '2021-06-02'
if (!dir.exists(save_dir))
dir.create(save_dir)
medians_pat = sprintf('%s/median_counts_%s_%s.txt', save_dir, '%s', date_tag)
prop_exp_pat = sprintf('%s/prop_exp_%s_%s.txt', save_dir, '%s', date_tag)
n_exp_f = sprintf('%s/n_exp_%s.txt', save_dir, date_tag)
conf_dt_f = sprintf('%s/confusion_matrix_dt_%s.txt', save_dir, date_tag)
conos_chk_f = sprintf('%s/conos_chk_%s.txt', save_dir, date_tag)
# group some celltypes together
broad_list = list(
`OPCs / COPs` = 'OPCs / COPs',
Oligodendrocytes = 'Oligodendrocytes',
`Astrocytes`='Astrocytes',
`Microglia + Immune`=c('Microglia', 'Immune'),
`Excitatory neurons`='Excitatory neurons',
`Inhibitory neurons`='Inhibitory neurons',
`Endothelial + Pericytes`=c('Endothelial cells', 'Pericytes')
)
min_cells = 10
fgsea_cut = 0.1
muscat_ctrl_f = sprintf('%s/muscat_ctrl_markers_%s.txt', save_dir, '2021-07-27')
fgsea_ctrl_f = sprintf('%s/muscat_ctrl_gsea_%s.txt', save_dir, '2021-07-27')
muscat_all_f = sprintf('%s/muscat_markers_%s.txt', save_dir, '2021-07-27')
fgsea_all_f = sprintf('%s/muscat_gsea_%s.txt', save_dir, '2021-07-27')Load inputs
# do pseudobulking
pb_fine = pb_fine_f %>% readRDS
props_fine = prop_fine_f %>% readRDS
# load soup stuff
pb_soup = pb_soup_f %>% readRDS
pb_broad = pb_broad_f %>% readRDS
contam_dt = .calc_contam_dt(pb_soup, pb_broad, min_cells)
rm(pb_soup, pb_broad)
biotypes_dt = .get_biotypes_dt(gtf_f)
labels_dt = load_names_dt(labels_f) %>%
.[, conos := NULL]cols_dt = colData(pb_fine) %>% as.data.frame %>%
as.data.table(keep.rownames = 'sample_id') %>%
.[, .(sample_id, patient_id, lesion_type)]
conos_dt = load_labelled_dt(labelled_f, labels_f) %>%
merge(cols_dt, by = 'sample_id')pb_qc_dt = calc_pb_qc_dt(qc_dir, qc_f)# get genes from panel
byhand_ls = load_byhand_genes(byhand_f, labels_dt, pb_fine)
byhand_gs = map(byhand_ls, ~.x$symbol) %>% unlist %>% unique
# get genes from roche panel
roche_dt = load_roche_genes(roche_f, labels_dt, pb_fine)
roche_gs = roche_dt$symbol %>% unique
# get genes from Dheeraj's list
dheeraj_dt = load_dheeraj_dt(pb_fine)micro_gs = load_micro_macro_markers()final_cols = make_final_cols(labels_dt)
umap_dt = load_umap_many(umap_many_f, conos_dt)Processing / calculations
n_exp_dt = calc_n_exp_dt(n_exp_f, sce_f, conos_dt, byhand_ls)prop_exp_f = sprintf(prop_exp_pat, 'cartana')
prop_exp_dt = calc_prop_exp_dt(prop_exp_f, sce_f, conos_dt, byhand_ls)
seriate_obj = calc_heatmap_ordering(prop_exp_dt)prop_exp_f = sprintf(prop_exp_pat, 'roche')
prop_exp_dt_roche = calc_prop_exp_dt(prop_exp_f, sce_f, conos_dt, list(roche_dt))
seriate_roche = calc_heatmap_ordering(prop_exp_dt_roche)medians_f = sprintf(medians_pat, 'cartana')
medians_dt = calc_medians_dt(medians_f, sce_f, conos_dt, byhand_ls)medians_f = sprintf(medians_pat, 'roche')
medians_dt_roche = calc_medians_dt(medians_f, sce_f, conos_dt, list(roche_dt))set.seed(123)
conf_dt = test_random_forest(conf_dt_f, sce_f, conos_dt,
min_exp_per_cell = 10, n_per_conos = 1000, n_cores = 16)set.seed(123)
conos_chk = calc_conos_on_panel(sce_f, conos_dt, byhand_gs, conos_chk_f)# get muscat markers within healthy samples
muscat_ctrl = calc_muscat_on_clusters(pb_fine, labels_dt,
contam_dt, biotypes_dt, broad_list, muscat_ctrl_f, sel_lesions = c('WM', 'GM'),
min_cells = 10, n_cores = 8)
fgsea_ctrl = calc_fgsea_on_muscat(muscat_ctrl, labels_dt, fgsea_ctrl_f,
fgsea_cut = 0.1, n_cores = 8)
# get muscat markers within all samples
muscat_all = calc_muscat_on_clusters(pb_fine, labels_dt,
contam_dt, biotypes_dt, broad_list, muscat_all_f, sel_lesions = c('WM', 'GM'),
min_cells = 10, n_cores = 8)
fgsea_all = calc_fgsea_on_muscat(muscat_all, labels_dt, fgsea_all_f,
fgsea_cut = 0.1, n_cores = 8)Analysis
UMAP of final celltypes
message('min_dist = 1, spread = 2')min_dist = 1, spread = 2(plot_umap_final_celltypes(umap_dt[ min_dist == 1 & spread == 2 ],
final_cols))
| Version | Author | Date |
|---|---|---|
| 0ec42e1 | Macnair | 2021-06-20 |
UMAP of final celltypes - broad
message('min_dist = 1, spread = 2')min_dist = 1, spread = 2(plot_umap_final_celltypes_broad(umap_dt[ min_dist == 1 & spread == 2 ]))
UMAP of OPCs + oligos only
message('min_dist = 1, spread = 2')min_dist = 1, spread = 2umap_sub = umap_dt[ min_dist == 1 & spread == 2 ] %>%
.[ type_broad %in% c('Oligodendrocytes', 'OPCs / COPs') ]
(plot_umap_fine(umap_sub, final_cols, by_var = 'type_fine',
xlim = c(0.1, 0.6), ylim = c(0, 0.5)))
UMAP of final celltypes
for (d in unique(umap_dt$min_dist)) {
for (s in unique(umap_dt$spread)) {
cat(sprintf('### min_dist = %.2f, spread = %d\n', d, s))
print(plot_umap_final_celltypes(umap_dt[ min_dist == d & spread == s ],
final_cols))
cat('\n\n')
}
}
Dotplots of hand-selected marker genes (broad)
(plot_dotplot(pb_fine, props_fine, byhand_ls$broad, labels_dt,
row_split = 'broad_class'))
Dotplots of Dheeraj’s marker genes, compact (broad)
remove_dt = list(
`OPCs / COPs` = c("VCAN"),
Oligodendrocytes = c("GADD45B", "MOBP", "FSTL5"),
Astrocytes = c("ALDH1L1", "LRAT", "WIF1", "LINC00499"),
Microglia = c("CD74", "APOE"),
`Excitatory neurons` = c("ST18", "SV2B"),
`Inhibitory neurons` = c("CUX2", "GAD2", "EYA4"),
`Endothelial cells` = c("VWF"),
Immune = c("IGH4", "CD96")
) %>% imap( ~ data.table( broad_class = .y, symbol = .x, remove = TRUE )) %>%
rbindlist
dheeraj_tweak = merge(dheeraj_dt, remove_dt, by = c('broad_class', 'symbol'),
all.x = TRUE) %>%
.[ is.na(remove), remove := FALSE ] %>%
.[ remove == FALSE ] %>%
.[, broad_class := factor(broad_class, levels = broad_ord) %>%
fct_collapse(Endo = c("Endothelial cells", "Pericytes")) %>%
fct_recode(
OPCs = 'OPCs / COPs', Oligos = "Oligodendrocytes",
Micro = 'Microglia', Astros = 'Astrocytes',
`Inhib. neurons` = 'Inhibitory neurons', Imm = 'Immune'
)] %>%
.[, symbol := factor(symbol, levels = unique(dheeraj_dt$symbol) )] %>%
unique %>%
.[ order(broad_class, symbol) ] %>%
.[, symbol := as.character(symbol)]
labels_tweak = copy(labels_dt) %>%
.[, type_broad := fct_collapse(type_broad,
Endo = c("Endothelial cells", "Pericytes")) %>%
fct_recode(
OPCs = 'OPCs / COPs', Oligos = "Oligodendrocytes",
Micro = 'Microglia', Astros = 'Astrocytes',
`Inhib. neurons` = 'Inhibitory neurons', Imm = 'Immune'
) ]
(plot_dotplot(pb_fine, props_fine, dheeraj_tweak, labels_tweak,
row_split = 'broad_class'))
Dotplots of Dheeraj’s marker genes, all (broad)
(plot_dotplot(pb_fine, props_fine, dheeraj_dt, labels_dt,
row_split = 'broad_class'))
Dotplots of hand-selected marker genes (split by cluster)
for (b in names(broad_list)) {
cat('### ', b, '\n')
byhand_dt = rbind(
byhand_ls$broad[broad_class %in% broad_list[[b]],
.(fine_class = broad_class, symbol)],
byhand_ls$fine[broad_class %in% broad_list[[b]],
.(fine_class, symbol)]
) %>%
.[, fine_class := fine_class %>% fct_drop %>% fct_relevel(broad_list[[b]])]
suppressWarnings(print(plot_dotplot(pb_fine, props_fine, byhand_dt, labels_dt,
row_split = 'fine_class')))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia + Immune
Excitatory neurons
Inhibitory neurons
QC at pseudobulk level
for (m in c('WM', 'GM')) {
cat('### ', m, '\n')
print(plot_pb_qc(pb_qc_dt[ matter == m ], min_cells = min_cells))
cat('\n\n')
}WM
GM
Dotplots of muscat results (all samples, broad)
for (b in names(broad_list)) {
cat('### ', b, '\n')
print(plot_dotplot_muscat(muscat_all, labels_dt, broad_list[[b]],
fdr_cut = 0.05))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia + Immune
Excitatory neurons
Inhibitory neurons
Endothelial + Pericytes
Dotplots of muscat results (healthy samples, broad)
for (b in names(broad_list)) {
cat('### ', b, '\n')
print(plot_dotplot_muscat(muscat_ctrl, labels_dt, broad_list[[b]],
fdr_cut = 0.05))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia + Immune
Excitatory neurons
Inhibitory neurons
Endothelial + Pericytes
Dotplots of GSEA results (all samples, broad)
for (b in names(broad_list)) {
cat('### ', b, '\n')
print(plot_dotplot_gsea(fgsea_all, labels_dt, broad_list[[b]],
fgsea_cut = fgsea_cut))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia + Immune
Excitatory neurons
Inhibitory neurons
Endothelial + Pericytes
Dotplots of GSEA results (healthy samples, broad)
for (b in names(broad_list)) {
cat('### ', b, '\n')
print(plot_dotplot_gsea(fgsea_ctrl, labels_dt, broad_list[[b]],
fgsea_cut = fgsea_cut))
cat('\n\n')
}OPCs / COPs
Oligodendrocytes
Astrocytes
Microglia + Immune
Excitatory neurons
Inhibitory neurons
Endothelial + Pericytes
Dotplot of microglia / macrophage genes
suppressWarnings(print(plot_dotplot(pb_fine, props_fine, micro_gs, labels_dt,
row_split = 'micro_class')))
How frequently are panel markers expressed in different celltypes?
The dots show the median expressing cell, i.e. 50% of the cells of a given type express this many genes or less. So for example, 50% of the Immune cells in our snRNAseq dataset express at most 4 of the panel genes.
for (what_level in c('type_broad', 'type_fine')) {
cat('### ', what_level, '\n')
suppressWarnings(print(plot_binary_distributions(n_exp_dt, what_level, dot_cut = 0.5)))
cat('\n\n')
}
Heatmaps of panel marker expression - Cartana panel
what_list = c(
'Propn. of cells expressing (scaled)',
'Propn. of cells expressing',
'Median counts per cell'
) %>% setNames(c('mean_scaled', 'mean_exp', 'median_exp'))
for (what in names(what_list)) {
cat('### ', what_list[[what]], '\n')
if (what == 'median_exp') {
suppressWarnings(print(plot_marker_heatmap(medians_dt, labels_dt,
seriate_obj, what)))
} else {
suppressWarnings(print(plot_marker_heatmap(prop_exp_dt, labels_dt,
seriate_obj, what)))
}
cat('\n\n')
}
Heatmaps of panel marker expression - Roche panel
for (what in names(what_list)) {
cat('### ', what_list[[what]], '\n')
if (what == 'median_exp') {
suppressWarnings(print(plot_marker_heatmap(medians_dt_roche, labels_dt,
seriate_roche, what)))
} else {
suppressWarnings(print(plot_marker_heatmap(prop_exp_dt_roche, labels_dt,
seriate_roche, what)))
}
cat('\n\n')
}
Classifier using panel markers
I took 1k cells per celltype, and trained a classifier using binary gene expression (i.e. we just see a 0 or a 1 for each panel gene in each cell). Then I tested this on another 1k cells per celltype. This gives an idea of how easy it is to identify these celltypes with our panel.
The three heatmaps show:
- prob Predictions for each celltype, in proportions, so each row sums to 1. In a perfect world, this would be diagonal, indicating that each celltype was predicted only to be that celltype. The N_ok column on the left shows how many cells had at least 10 panel genes expressed, and that we could use in the classifier.
- logp Same as 1., but with log values of the proportions.
- diff This how far away from the perfect world we are. You see red where this prediction is made too much, e.g. immune cells being predicted as microglia. You see blue where predictions are missing, so for all Immune cells the correct celltype is not predicted sufficiently.
for (what in c('prob', 'logp', 'diff')) {
cat('### ', what, '\n')
suppressWarnings(draw(plot_classifier_heatmap(conf_dt, labels_dt, what)))
cat('\n\n')
}
Conos using panel markers - clusters
draw(plot_conos_check(conos_dt, conos_chk))
Conos using panel markers - UMAP
for (what in c('type_broad', 'type_fine', 'conos_panel')) {
cat('### ', what, '\n')
print(plot_conos_umap(conos_chk, conos_dt, what))
cat('\n\n')
}
Conos using panel markers - UMAP density
for (b in broad_ord) {
cat('### ', b, '\n')
print(plot_conos_density(conos_chk, conos_dt, b))
cat('\n\n')
}
Outputs
devtools::session_info()- 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 2021-07-28
- Packages -------------------------------------------------------------------
! package * version date lib
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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.Figures
Dotplots of muscat genes marker genes (all samples)
top_muscat = muscat_all[ logFC > 0 ] %>%
.[, fdr_rank := frank(FDR, ties.method = 'random'), by = type_fine ] %>%
.[ fdr_rank <= 2 ] %>%
.[ order(type_broad, type_fine, fdr_rank) ] %>%
.[, .(broad_class = type_broad, symbol) ]
(plot_dotplot(pb_fine, props_fine, top_muscat, labels_dt,
row_split = 'broad_class'))
Dotplots of muscat genes marker genes (healthy samples)
top_muscat = muscat_ctrl[ logFC > 0 ] %>%
.[, fdr_rank := frank(FDR, ties.method = 'random'), by = type_fine ] %>%
.[ fdr_rank <= 2 ] %>%
.[ order(type_broad, type_fine, fdr_rank) ] %>%
.[, .(broad_class = type_broad, symbol) ]
(plot_dotplot(pb_fine, props_fine, top_muscat, labels_dt,
row_split = 'broad_class'))
Dotplots of known oligo genes
oligo_gs = list(
oligos = c("MAG", "MOG", "CNP", "MYRF", "PLP1", "MBP"),
lipids = c("ELOVL2", "ELOVL4", "ELOVL5", "ELOVL7"),
myelin = c("CERS2", "CGT", "CST", "DAPAT", "PEX7")
)
oligos_dt = lapply(names(oligo_gs), function(n)
data.table(oligo_fn = n, symbol = oligo_gs[[n]]) ) %>% rbindlist %>%
.[, oligo_fn := factor(oligo_fn, levels = names(oligo_gs))]
olg_types = c("Oligo_A1", "Oligo_A2", "Oligo_B1", "Oligo_B2", "Oligo_B3", "Oligo_B4",
"Oligo_C1", "Oligo_C2", "Oligo_D")
pb_olgs = copy(pb_fine)
assays(pb_olgs) = assays(pb_olgs)[olg_types]
int_colData(pb_olgs)$n_cells =
lapply(int_colData(pb_olgs)$n_cells, function(l) l[olg_types])
props_olgs = copy(props_fine)
assays(props_olgs) = assays(props_olgs)[olg_types]
int_colData(props_olgs)$n_cells =
lapply(int_colData(props_olgs)$n_cells, function(l) l[olg_types])
(plot_dotplot(pb_olgs, props_olgs, oligos_dt, labels_dt,
row_split = 'oligo_fn'))
Dotplots of astrocyte layer genes
astro_gs = list(
pan_astro = c("ALDH1A1", "GFAP"),
upper = c("SCEL", "SPRY1", "DDHD1", "EOGT", "CHRDL1", "ADIPOR2"),
gm_bias = c("GRM3", "KIRREL2", "IGFBP2", "IRAK2", "TRPM3", "CHD9",
"INKA2", "SLC25A34", "AXIN2", "SLC7A5", "LIMD1", "CDO1", "AKT2", "LEF1",
"BSG", "APOE", "MFGE8", "ITM2C", "SLC27A1", "CYP4F2", "FJX1"),
deep = c("ID3", "ID1", "DKK3", "EFHD2", "LRP1B", "DHCR24",
"IL33", "PAQR6")
)
astros_dt = lapply(names(astro_gs), function(n)
data.table(astro_layer = n, symbol = astro_gs[[n]]) ) %>% rbindlist %>%
.[, astro_layer := factor(astro_layer, levels = names(astro_gs))]
# setdiff(astros_dt$symbol, rowData(pb_fine)$symbol)
ast_types = c("Astro_A", "Astro_B", "Astro_C", "Astro_D", "Astro_E")
pb_asts = copy(pb_fine)
assays(pb_asts) = assays(pb_asts)[ast_types]
int_colData(pb_asts)$n_cells =
lapply(int_colData(pb_asts)$n_cells, function(l) l[ast_types])
props_asts = copy(props_fine)
assays(props_asts) = assays(props_asts)[ast_types]
int_colData(props_asts)$n_cells =
lapply(int_colData(props_asts)$n_cells, function(l) l[ast_types])
(plot_dotplot(pb_asts, props_asts, astros_dt, labels_dt,
row_split = 'astro_layer'))
| Version | Author | Date |
|---|---|---|
| f60e5c1 | Macnair | 2021-08-03 |
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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] ggbeeswarm_0.6.0 pagoda2_1.0.3
[3] ranger_0.12.1 rmarkdown_2.8
[5] ggrepel_0.9.1 writexl_1.4.0
[7] readxl_1.3.1 fgsea_1.16.0
[9] tictoc_1.0.1 performance_0.7.2
[11] reshape2_1.4.4 Matrix.utils_0.9.8
[13] UpSetR_1.4.0 dplyr_1.0.7
[15] purrr_0.3.4 readr_1.4.0
[17] tidyr_1.1.3 tibble_3.1.2
[19] tidyverse_1.3.1 rtracklayer_1.50.0
[21] nnls_1.4 muscat_1.5.1
[23] DropletUtils_1.10.3 edgeR_3.32.1
[25] limma_3.46.0 googlesheets_0.3.0
[27] scran_1.18.7 uwot_0.1.10
[29] scater_1.18.6 BiocParallel_1.24.1
[31] ggplot.multistats_1.0.0 seriation_1.2-9
[33] ComplexHeatmap_2.6.2 SeuratObject_4.0.1
[35] Seurat_4.0.1 conos_1.4.1
[37] igraph_1.2.6 SampleQC_0.4.5
[39] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[41] Biobase_2.50.0 GenomicRanges_1.42.0
[43] GenomeInfoDb_1.26.7 IRanges_2.24.1
[45] S4Vectors_0.28.1 BiocGenerics_0.36.1
[47] MatrixGenerics_1.2.1 matrixStats_0.59.0
[49] Matrix_1.3-4 loomR_0.2.0
[51] itertools_0.1-3 iterators_1.0.13
[53] hdf5r_1.3.3 R6_2.5.0
[55] patchwork_1.1.1 forcats_0.5.1
[57] ggplot2_3.3.4 scales_1.1.1
[59] viridis_0.6.1 viridisLite_0.4.0
[61] assertthat_0.2.1 stringr_1.4.0
[63] data.table_1.14.0 magrittr_2.0.1
[65] circlize_0.4.13 RColorBrewer_1.1-2
[67] BiocStyle_2.18.1 colorout_1.2-2
[69] 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 Rsamtools_2.6.0
[5] foreach_1.5.1 lmtest_0.9-38
[7] rprojroot_2.0.2 crayon_1.4.1
[9] spatstat.core_2.1-2 MASS_7.3-54
[11] rhdf5filters_1.2.1 nlme_3.1-152
[13] backports_1.2.1 reprex_2.0.0
[15] rlang_0.4.11 XVector_0.30.0
[17] ROCR_1.0-11 irlba_2.3.3
[19] callr_3.7.0 nloptr_1.2.2.2
[21] rjson_0.2.20 bit64_4.0.5
[23] glue_1.4.2 sctransform_0.3.2
[25] processx_3.5.2 pbkrtest_0.5.1
[27] vipor_0.4.5 spatstat.sparse_2.0-0
[29] AnnotationDbi_1.52.0 spatstat.geom_2.1-0
[31] haven_2.4.1 tidyselect_1.1.1
[33] usethis_2.0.1 fitdistrplus_1.1-5
[35] variancePartition_1.20.0 XML_3.99-0.6
[37] zoo_1.8-9 GenomicAlignments_1.26.0
[39] xtable_1.8-4 evaluate_0.14
[41] cli_2.5.0 scuttle_1.0.4
[43] zlibbioc_1.36.0 rstudioapi_0.13
[45] miniUI_0.1.1.1 whisker_0.4
[47] bslib_0.2.5.1 rpart_4.1-15
[49] fastmatch_1.1-0 shiny_1.6.0
[51] BiocSingular_1.6.0 xfun_0.24
[53] clue_0.3-59 pkgbuild_1.2.0
[55] cluster_2.1.2 caTools_1.18.2
[57] TSP_1.1-10 listenv_0.8.0
[59] Biostrings_2.58.0 png_0.1-7
[61] future_1.21.0 withr_2.4.2
[63] bitops_1.0-7 plyr_1.8.6
[65] cellranger_1.1.0 dqrng_0.3.0
[67] pillar_1.6.1 gplots_3.1.1
[69] GlobalOptions_0.1.2 cachem_1.0.5
[71] fs_1.5.0 kernlab_0.9-29
[73] GetoptLong_1.0.5 DelayedMatrixStats_1.12.3
[75] vctrs_0.3.8 ellipsis_0.3.2
[77] generics_0.1.0 devtools_2.4.2
[79] urltools_1.7.3 tools_4.0.3
[81] beeswarm_0.4.0 munsell_0.5.0
[83] DelayedArray_0.16.3 pkgload_1.2.1
[85] fastmap_1.1.0 compiler_4.0.3
[87] abind_1.4-5 httpuv_1.6.1
[89] segmented_1.3-4 sessioninfo_1.1.1
[91] plotly_4.9.3 GenomeInfoDbData_1.2.4
[93] gridExtra_2.3 glmmTMB_1.0.2.1
[95] lattice_0.20-44 deldir_0.2-10
[97] utf8_1.2.1 later_1.2.0
[99] jsonlite_1.7.2 dendsort_0.3.4
[101] pbapply_1.4-3 sparseMatrixStats_1.2.1
[103] genefilter_1.72.1 lazyeval_0.2.2
[105] promises_1.2.0.1 doParallel_1.0.16
[107] R.utils_2.10.1 goftest_1.2-2
[109] brew_1.0-6 spatstat.utils_2.2-0
[111] reticulate_1.20 cowplot_1.1.1
[113] blme_1.0-5 statmod_1.4.36
[115] Rtsne_0.15 Rook_1.1-1
[117] HDF5Array_1.18.1 survival_3.2-11
[119] numDeriv_2016.8-1.1 yaml_2.2.1
[121] htmltools_0.5.1.1 memoise_2.0.0
[123] locfit_1.5-9.4 digest_0.6.27
[125] mime_0.10 registry_0.5-1
[127] N2R_0.1.1 RSQLite_2.2.7
[129] future.apply_1.7.0 remotes_2.4.0
[131] blob_1.2.1 R.oo_1.24.0
[133] drat_0.2.0 mvnfast_0.2.7
[135] labeling_0.4.2 splines_4.0.3
[137] Rhdf5lib_1.12.1 Cairo_1.5-12.2
[139] mixtools_1.2.0 RCurl_1.98-1.3
[141] broom_0.7.7 hms_1.1.0
[143] modelr_0.1.8 rhdf5_2.34.0
[145] colorspace_2.0-1 BiocManager_1.30.16
[147] shape_1.4.6 sass_0.4.0
[149] Rcpp_1.0.6 mclust_5.4.7
[151] RANN_2.6.1 mvtnorm_1.1-2
[153] fansi_0.5.0 parallelly_1.26.0
[155] ggridges_0.5.3 lifecycle_1.0.0
[157] bluster_1.0.0 minqa_1.2.4
[159] testthat_3.0.3 leiden_0.3.8
[161] jquerylib_0.1.4 snakecase_0.11.0
[163] desc_1.3.0 RcppAnnoy_0.0.18
[165] TMB_1.7.20 htmlwidgets_1.5.3
[167] triebeard_0.3.0 RMTstat_0.3
[169] beachmat_2.6.4 polyclip_1.10-0
[171] rvest_1.0.0 mgcv_1.8-36
[173] globals_0.14.0 insight_0.14.2
[175] leidenAlg_0.1.1 lubridate_1.7.10
[177] codetools_0.2-18 gtools_3.9.2
[179] prettyunits_1.1.1 dbplyr_2.1.1
[181] R.methodsS3_1.8.1 gtable_0.3.0
[183] DBI_1.1.1 git2r_0.28.0
[185] tensor_1.5 httr_1.4.2
[187] highr_0.9 KernSmooth_2.23-20
[189] stringi_1.6.2 progress_1.2.2
[191] farver_2.1.0 annotate_1.68.0
[193] hexbin_1.28.2 xml2_1.3.2
[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] geneplotter_1.68.0 scattermore_0.7
[203] DESeq2_1.30.1 bit_4.0.4
[205] spatstat.data_2.1-0 janitor_2.1.0
[207] pkgconfig_2.0.3 lmerTest_3.1-3
[209] knitr_1.33