Last updated: 2022-01-06
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Knit directory: cacoaAnalysis/
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| File | Version | Author | Date | Message |
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| Rmd | d864029 | viktor_petukhov | 2021-12-10 | Compositional figure notebooks |
PF
cao_pf <- DataPath("PF/cao.rds") %>% readr::read_rds() %>% Cacoa$new()
cao_pf$plot.theme %<>% {. + p_theme}leg_theme <- theme(legend.key.height=unit(10, "pt"), legend.text=element_text(size=9),
legend.background=element_blank())
leg_theme_small <- leg_theme + theme(legend.key.width=unit(6, "pt"), legend.key.height=unit(6, "pt"))
gene_lab_size <- 12
p_lims <- list("Endothelial Cells", c("Macrophages", "cDCs", "Monocytes")) %>% lapply(function(ct) {
getCellTypeEmbeddingLimits(cao_pf$embedding, cell.groups=cao_pf$cell.groups, groups.to.plot=ct, quant=0.004)
})
p_genes <- c("SPP1", "DKK2", "COL15A1", "CPE")
gg_markers <- mapply(function(g, pl) {
p.cols <- cao_pf$cache$joint.count.matrix.norm[,g] %>% pmin(., quantile(., 0.995))
cao_pf$data.object$plotGraph(colors=p.cols, size=0.2, alpha=0.1, show.legend=TRUE,
legend.position=c(1, 0), legend.title="Expr.") +
leg_theme_small + lims(x=pl$x, y=pl$y) +
scale_color_gradientn(colors=cacoa:::dark.red.palette(100),
breaks=seq(0, signif(max(p.cols) / 3, 1) * 3, length.out=4)) +
theme(panel.grid=element_blank())
}, p_genes, list(p_lims[[2]], p_lims[[1]], p_lims[[1]], p_lims[[1]]), SIMPLIFY=FALSE)
gg_markers %>%
plot_grid(plotlist=., labels=p_genes, nrow=2, label_fontface="italic", label_x=0.05,
hjust=0, label_size=gene_lab_size)
cao_pf$estimateDiffCellDensity(type='permutation', n.permutations=1000, verbose=TRUE,
n.cores=45, name='cell.density.kde')gg_pf_emb <- cao_pf$plotEmbedding(color.by='cell.groups', font.size=c(2, 3.5), size=0.25, alpha=0.1) +
theme(panel.grid=element_blank())
ggs_pf_dens <- cao_pf$plotCellDensity(
add.points=FALSE, show.grid=TRUE, show.cell.groups=FALSE, legend.title="Density",
name='cell.density.kde'
) %>% lapply(`+`, theme(plot.title=element_blank()))
c_pal <- c("#FFFFFF", brewerPalette("YlOrRd", rev=FALSE)(9)) %>% colorRampPalette()
pf_dens_col <- sccore::val2ggcol(ggs_pf_dens[[1]]$data$z, palette=c_pal, return.fill=TRUE,
color.range=c(0, 1e-4), breaks=seq(0, 1.2e-4, length.out=4))
ggs_pf_dens %<>% lapply(`+`, pf_dens_col)
ggs_pf_dens[[1]] %<>% {. + theme_legend_position(c(0, 0)) + leg_theme_small +
theme(legend.text=element_text(size=8))}
gg_pf_diff <- cao_pf$plotDiffCellDensity(adjust.pvalues=FALSE, legend.position=c(0, 1),
color.range=c("1%", "99%"), name='cell.density.kde',
type='permutation')
gg_pf_diff_adj <- cao_pf$plotDiffCellDensity(adjust.pvalues=TRUE, legend.position=c(0, 1), min.z=0.5,
name='cell.density.kde', type='permutation')
gg_prop_change <- cao_pf$plotDiffCellDensity(type="subtract", legend.position=c(0, 1),
color.range=c("1%", "99%"), name='cell.density.kde') +
leg_theme
plot_grid(plotlist=rast(ggs_pf_dens), ncol=2)
MS
cao_ms <- DataPath("MS/cao.rds") %>% readr::read_rds() %>% Cacoa$new()
cao_ms$plot.theme %<>% {. + p_theme}gg_ms_emb <- cao_ms$plotEmbedding(color.by='cell.groups', font.size=c(2.5, 4.5), size=0.25, alpha=0.1) +
theme(panel.grid=element_blank())
gg_ms_diff_adj <- cao_ms$plotDiffCellDensity(
adjust.pvalues=TRUE, legend.position=c(0, 0), min.z=0.5, size=0.1, alpha=0.1,
name="cell.density.graph"
) + leg_themep_lims_ms <- getCellTypeEmbeddingLimits(
cao_ms$embedding, cell.groups=cao_ms$cell.groups, groups.to.plot="Astrocytes", quant=0.01
)
p_genes_ms <- c("GFAP", "CD44", "TNC")
astro_lims <- lims(x=p_lims_ms$x, y=p_lims_ms$y)
col_range <- c(0, 0.8)
gg_ms_markers <- lapply(p_genes_ms, function(g) {
p.cols <- cao_ms$cache$joint.count.matrix.norm[,g]# %>% pmin(., quantile(., 0.995))
cao_ms$plotEmbedding(colors=p.cols, size=0.2, alpha=0.1, show.legend=TRUE,
legend.position=c(1, 0), legend.title="Expr.", color.range=col_range,
palette=cacoa:::dark.red.palette) + astro_lims
})
gg_ms_markers[2:3] %<>% lapply(`+`, theme(legend.position="none"))
adj_list <- list(astro_lims, theme(plot.title=element_blank()))
gg_shisa_markers <- cao_ms$plotGeneExpressionComparison(
"SHISA9", plots="expression", build.panel=FALSE, size=0.2, alpha=0.5,
legend.position="none", adj.list=adj_list, max.expr=col_range[2]
)
cd44_ast <- cao_ms$cell.groups %>% {names(.)[. == "Astrocytes"]} %>%
.[cao_ms$test.results$cell.density.graph$diff$wilcox$adj[.] > 1]
cm_agg <- cao_ms$cache$joint.count.matrix[cd44_ast,] %>%
sccore::colSumByFactor(cao_ms$sample.per.cell[cd44_ast])
p_df <- (cm_agg[,"SHISA9"] / rowSums(cm_agg)) %>%
.[!is.na(.)] %>% {tibble(expr=1000 * ., sg=cao_ms$sample.groups[names(.)], type=names(.))}
gg_shisa_box <- ggplot(p_df, aes(x=sg, y=expr, fill=sg)) +
geom_boxplot(outlier.size=0) +
ggbeeswarm::geom_quasirandom(size=1) +
ylab("SHISA9 expr.") +
scale_fill_manual(values=cao_ms$sample.groups.palette) +
theme(legend.position="none", axis.title.x=element_blank(), panel.grid.major.x=element_blank(),
panel.grid.minor=element_blank())
gg_mark_list <- c(
gg_ms_markers,
gg_shisa_markers[c(2, 1)],
list(gg_shisa_box)
# list(gg_shisa_box + theme(plot.margin=margin(l=3)))
) %>% lapply(rast) %>% lapply(`+`, theme(panel.grid=element_blank()))
gg_mark_list[[1]] %<>% {. + leg_theme_small}Compile figure
theme_nm <- theme(
plot.margin=margin(), panel.border=element_rect(size=0.25)
)
scale_row <- 0.97
scale_inner <- 0.99
plot_grid(
plot_grid(
ggplot() + theme_nothing(),
rast(gg_pf_emb, dpi=75) + theme_nm,
plot_grid(
plotlist=rast(lapply(ggs_pf_dens, `+`, theme_nm)), labels=names(ggs_pf_dens),
label_fontface="italic", label_x=0.05, hjust=0, label_size=gene_lab_size,
ncol=1, scale=scale_inner
),
ncol=3, rel_widths=c(1.5, 2, 1), scale=scale_row
),
plot_grid(
rast(gg_prop_change) + leg_theme + theme_nm + theme(plot.margin=margin(l=2)),
rast(gg_pf_diff) + leg_theme + theme_nm,
rast(gg_pf_diff_adj) + leg_theme + theme_nm,
plot_grid(
plotlist=rast(lapply(gg_markers, `+`, theme_nm)), labels=p_genes,
label_fontface="italic", label_x=0.05, hjust=0, label_size=gene_lab_size, ncol=2,
scale=scale_inner
),
nrow=1, scale=scale_row
),
plot_grid(
rast(gg_ms_emb) + theme_nm,
rast(gg_ms_diff_adj) + theme_nm,
plot_grid(
plotlist=rast(lapply(gg_mark_list, `+`, theme_nm)),
labels=c(p_genes_ms, "SHISA9, CNT", "SHISA9, MS"),
label_fontface="italic", label_x=0.05, hjust=0, label_size=gene_lab_size,
nrow=2, scale=scale_inner
),
nrow=1, rel_widths=c(1, 1, 1.5), scale=scale_row
),
nrow=3, rel_heights=c(1.7, 1, 1.1), scale=0.98
)
ggsave(figurePath("2_composition_cf.pdf"))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.3/lib/R/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.3/lib/R/lib/libRlapack.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cacoaAnalysis_0.1.0 dataorganizer_0.1.0 cowplot_1.1.1
[4] reshape2_1.4.4 ggrastr_1.0.1 ggpubr_0.4.0
[7] cacoa_0.2.0 Matrix_1.2-18 magrittr_2.0.1
[10] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.4
[16] tibble_3.1.5 ggplot2_3.3.5 tidyverse_1.3.0
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] N2R_0.1.1 readxl_1.3.1 backports_1.2.1
[4] circlize_0.4.13 systemfonts_1.0.0 plyr_1.8.6
[7] igraph_1.2.6 splines_4.0.3 usethis_1.6.3
[10] urltools_1.7.3 digest_0.6.28 foreach_1.5.1
[13] htmltools_0.5.2 fansi_0.5.0 RMTstat_0.3
[16] memoise_2.0.0 cluster_2.1.0 doParallel_1.0.16
[19] openxlsx_4.2.3 remotes_2.2.0 ComplexHeatmap_2.9.4
[22] modelr_0.1.8 matrixStats_0.61.0 R.utils_2.10.1
[25] sccore_1.0.1 prettyunits_1.1.1 colorspace_2.0-2
[28] rvest_0.3.6 ggrepel_0.9.1 pagoda2_1.0.7
[31] textshaping_0.2.1 haven_2.4.1 xfun_0.26
[34] callr_3.5.1 crayon_1.4.1 jsonlite_1.7.2
[37] brew_1.0-6 iterators_1.0.13 ape_5.5
[40] glue_1.4.2 gtable_0.3.0 GetoptLong_1.0.5
[43] car_3.0-10 leidenAlg_0.1.0 pkgbuild_1.1.0
[46] Rook_1.1-1 shape_1.4.6 BiocGenerics_0.36.1
[49] abind_1.4-5 scales_1.1.1 DBI_1.1.1
[52] rstatix_0.7.0 Rcpp_1.0.7 clue_0.3-59
[55] reticulate_1.22 foreign_0.8-80 stats4_4.0.3
[58] httr_1.4.2 RColorBrewer_1.1-2 ellipsis_0.3.2
[61] farver_2.1.0 R.methodsS3_1.8.1 pkgconfig_2.0.3
[64] dbplyr_2.0.0 utf8_1.2.2 labeling_0.4.2
[67] tidyselect_1.1.1 rlang_0.4.11 later_1.1.0.1
[70] pbmcapply_1.5.0 munsell_0.5.0 cellranger_1.1.0
[73] tools_4.0.3 cachem_1.0.6 cli_3.0.1
[76] generics_0.1.0 devtools_2.3.2 broom_0.7.9
[79] evaluate_0.14 fastmap_1.1.0 ragg_0.4.1
[82] yaml_2.2.1 processx_3.4.5 knitr_1.36
[85] fs_1.5.0 zip_2.2.0 nlme_3.1-149
[88] whisker_0.4 R.oo_1.24.0 grr_0.9.5
[91] xml2_1.3.2 compiler_4.0.3 rstudioapi_0.13
[94] beeswarm_0.4.0 curl_4.3.2 png_0.1-7
[97] testthat_3.0.0 ggsignif_0.6.1 reprex_0.3.0
[100] stringi_1.7.5 highr_0.9 drat_0.1.8
[103] ps_1.4.0 desc_1.3.0 lattice_0.20-41
[106] vctrs_0.3.8 pillar_1.6.3 lifecycle_1.0.1
[109] triebeard_0.3.0 jquerylib_0.1.4 GlobalOptions_0.1.2
[112] irlba_2.3.3 data.table_1.14.2 Matrix.utils_0.9.8
[115] httpuv_1.5.4 conos_1.4.4 R6_2.5.1
[118] promises_1.1.1 gridExtra_2.3 rio_0.5.26
[121] vipor_0.4.5 IRanges_2.24.1 sessioninfo_1.1.1
[124] codetools_0.2-16 MASS_7.3-53 assertthat_0.2.1
[127] pkgload_1.2.1 rprojroot_2.0.2 rjson_0.2.20
[130] withr_2.4.2 S4Vectors_0.28.1 mgcv_1.8-33
[133] parallel_4.0.3 hms_1.1.1 grid_4.0.3
[136] rmarkdown_2.11 dendsort_0.3.3 carData_3.0-4
[139] Rtsne_0.15 git2r_0.27.1 lubridate_1.7.9.2
[142] ggbeeswarm_0.6.0