Last updated: 2022-01-14
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Knit directory: cacoaAnalysis/
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| Rmd | c4d4786 | viktor_petukhov | 2021-12-27 | Interpretation figure |
Epithelial cells
Extract
cao_ept <- readOrCreate(CachePath("cao_pf_ept.rds"), function() {
cao.pf <- read_rds(DataPath("PF/cao.rds")) %>% Cacoa$new()
epithelial.types <- c(
"AT1", "Transitional AT2", "AT2", "Basal", "KRT5-/KRT17+", "MUC5AC+ High", "MUC5B+",
"Proliferating Epithelial Cells", "SCGB3A2+", "SCGB3A2+ SCGB1A1+"
)
ept.cms <- lapply(cao.pf$data.object$samples, function(p2) {
p2$misc$rawCounts %>% .[cao.pf$cell.groups[rownames(.)] %in% epithelial.types,] %>% t()
}) %>% .[sapply(., ncol) > 80]
ept.p2s <- plapply(ept.cms, createPagoda, n.pcs=50, n.cores=N_CORES, progress=TRUE,
mc.preschedule=TRUE)
if ("value" %in% names(ept.p2s)) ept.p2s <- ept.p2s$value
ept.con <- conos::Conos$new(ept.p2s, n.cores=N_CORES)
ept.con$buildGraph(k=30, k.self.weight=0.5)
ept.con$embedGraph(min.prob.lower=1e-4, method="UMAP", verbose=FALSE)
cao.ept <- Cacoa$new(
ept.con, cell.groups=cao.pf$cell.groups[names(ept.con$getDatasetPerCell())],
sample.groups=cao.pf$sample.groups[names(ept.con$samples)],
ref.level=cao.pf$ref.level, target.level=cao.pf$target.level, n.cores=N_CORES
)
cao.ept$plot.theme %<>% `+`(theme(legend.background=element_blank()))
cao.ept$estimateDEPerCellType(independent.filtering=TRUE, test="DESeq2.Wald")
cao.ept$estimateOntology(org.db=org.db, type='GSEA')
return(cao.ept)
}) %>% Cacoa$new()Cluster-based
cao_ept$estimateOntology(org.db=org.db, type='GSEA') # TODO: remove thisgg_at_apopt <- cao_ept$plotOntologyHeatmap(
name='GSEA', genes="up", description.regex='death|apopt|proliferation', min.genes=10,
description.exclude.regex='neur', max.log.p=5
)
gg_at_apopt
immune_regex <- 'vir|immune|interferon|inflam'
gg_at_immune <- cao_ept$plotOntologyHeatmap(
name='GSEA', genes="all", legend.title='-log10(p-value) * S',
description.regex=immune_regex, min.genes=10, max.log.p=5
)
gg_at_immune
gg_at_matrix <- cao_ept$plotOntologyHeatmap(
name='GSEA', genes="up", description.regex='matrix|mesen', min.genes=10, max.log.p=5
)
gg_at_matrix
Cluster-free
cao_ept$estimateClusterFreeDE(n.top.genes=1000, min.expr.frac=0.01, adjust.pvalues=TRUE,
smooth=TRUE)Estimating cluster-free Z-scores for 1000 most expressed genes0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
***************************************************Gene programs
cao_ept$estimateGenePrograms(n.programs=9, z.adj=TRUE, abs.scores=TRUE, smooth=FALSE, verbose=FALSE)cao_ept$plotGeneProgramScores(legend.position=c(0, 1), size=0.1, alpha=0.5, plot.na=FALSE,
adj.list=theme(plot.margin=margin()))
ggs_cf_scores <- cao_ept$plotGeneProgramScores(
prog.ids=c(6, 8), legend.position=c(0, 1), size=0.1, alpha=0.3, build.panel=FALSE,
plot.na=FALSE, adj.list=theme(plot.margin=margin(), plot.title=element_blank())
)
plot_grid(plotlist=ggs_cf_scores)
go_env <- cao_ept$getGOEnvironment(org.db=org.db)Using stored GO environment. Use `ignore.cache=TRUE` if you want to re-estimate it. Set `ignore.cache=FALSE` to suppress this message.gene_universe_global <- colnames(cao_ept$test.results$cluster.free.de$z.adj) %>%
cacoa:::mapGeneIds(org.db)
length(gene_universe_global)[1] 932t_scores <- c(6, 8) %>%
{setNames(cao_ept$test.results$gene.programs$sim.scores[.], .)} %>%
lapply(function(x) x[x > 0.5])
sapply(t_scores, length) 6 8
104 47 go_global <- lapply(t_scores, function(x) head(names(x[x > 0.5]), 50)) %>%
lapply(cacoa:::mapGeneIds, org.db) %>%
cacoa:::estimateEnrichedGO(org.db=org.db, go.environment=go_env, universe=gene_universe_global)
go_dfs <- lapply(go_global$BP, function(r) filter(r@result, p.adjust < 0.05)) %>%
.[sapply(., nrow) > 0]
sapply(go_dfs, nrow) 6 8
11 25 AT2 -> AT1
go_dfs$`6` %$% setNames(strsplit(geneID, "/"), Description) %>%
cacoa:::estimateClusterPerGO(cut.h=0.4) %>% {split(names(.), .)} %>%
sapply(paste, collapse='"; "') %>% {paste0('"', ., '"\n')} %>% cat()"cellular lipid metabolic process"; "lipid metabolic process"
"small molecule metabolic process"; "organic acid metabolic process"; "carboxylic acid metabolic process"; "oxoacid metabolic process"
"small molecule catabolic process"
"fatty acid metabolic process"
"fatty acid oxidation"; "lipid oxidation"; "lipid modification"gg_go_at <- clusteredOntologyDotplot(go_global$BP$`6`, orderBy='x', cut.h=0.4)
gg_go_at
Transitional state program
c_go_clusts <- go_dfs$`8` %$% setNames(strsplit(geneID, "/"), Description) %>%
cacoa:::estimateClusterPerGO(cut.h=0.4) %>% {split(names(.), .)}
c_go_clusts %>% sapply(paste, collapse='"; "') %>% {paste0('"', ., '"\n')} %>% cat"regulation of viral entry into host cell"; "modulation by symbiont of entry into host"; "negative regulation of viral entry into host cell"; "negative regulation of viral life cycle"; "regulation of viral life cycle"; "negative regulation of viral process"; "viral entry into host cell"; "entry into host"; "regulation of viral process"; "regulation of biological process involved in symbiotic interaction"; "movement in host environment"; "biological process involved in interaction with host"
"response to interferon-gamma"
"innate immune response"; "defense response"; "defense response to other organism"; "response to external biotic stimulus"; "response to other organism"
"response to external stimulus"; "immune system process"; "response to stress"; "cellular response to chemical stimulus"
"cytokine-mediated signaling pathway"; "cellular response to cytokine stimulus"; "response to cytokine"gg_go_trans <- clusteredOntologyDotplot(go_global$BP[["8"]], orderBy='x', cut.h=0.4)
gg_go_trans
cao_ept$plot.params <- list(size=0.5, alpha=0.5)
ggs_at_genes <- c("AGER", "HOPX", "SFTPC") %>% sccore::sn() %>% lapply(function(gn) {
cao_ept$plotEmbedding(colors=cao_ept$cache$joint.count.matrix.norm[,gn], legend.title="Expr.",
legend.position=c(0, 1))
})
ggs_at_genes$AGER
$HOPX
$SFTPC
Endothelial cells
# Requires running cluster-free expression figure first
cao_endo <- read_rds(CachePath("cao_pf_endo.rds")) %>% Cacoa$new()
cao_endo$estimateOntology(type="GSEA", org.db=org.Hs.eg.db::org.Hs.eg.db)gg_end_viral <- cao_endo$plotOntologyHeatmap(
name='GSEA', genes="all", legend.title='-log10(p-value) * S',
description.regex='vir|immune|interferon|inflam', min.genes=10, max.log.p=5,
description.exclude.regex='built from' # Remove one super-long GO for cluster name
)
gg_end_viral
cao_endo$plotOntologyHeatmap(name='GSEA', genes="up", description.regex='matrix|mesen')
Compile the figure
theme_ax <- theme(
axis.text.x=element_text(size=8),
axis.text.y=element_text(size=8, lineheight=0.75),
plot.title=element_blank(),
plot.margin=margin()
)
fill_guide <- guides(fill=guide_colorbar(
title='-log10(p-value) * S', title.theme=element_text(size=12),
title.position="top"
))
fill_scale <- gg_at_immune$scales$scales[[3]]
plt_list <- list(gg_at_apopt, gg_at_immune, gg_at_matrix, gg_end_viral) %>% lapply(function(gg) {
levels(gg$data$G1) %<>% str_wrap(50)
gg <- gg + theme_ax + fill_guide + fill_scale + theme_legend_position("none")
gg
})Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which
will replace the existing scale.plt_list[[2]] %<>% {. + theme(
legend.position=c(2.3, 1.3), legend.justification=c(1, 1),
legend.direction="horizontal", legend.margin=margin(),
legend.box.margin=margin(),
legend.key.height=unit(12, "pt"), legend.key.width=unit(16, "pt")
)}
go_fill_scale <- scale_color_continuous(low="red", high="blue", limits=c(0, 0.05),
guide=guide_colorbar(reverse=TRUE))
go_size_scale <- scale_size_continuous(limits=c(4, 20))
gg_go_at %<>% {. + go_fill_scale + go_size_scale + xlab("Gene ratio")}Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.Scale for 'size' is already present. Adding another scale for 'size', which
will replace the existing scale.gg_go_trans %<>% {. + go_fill_scale + go_size_scale + xlab("Gene ratio")}Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
Scale for 'size' is already present. Adding another scale for 'size', which
will replace the existing scale.go_leg_grob <- ggpubr::get_legend(gg_go_at)
ggs_cf_scores_rast <- lapply(ggs_cf_scores, ggrastr::rasterise, dev="ragg_png", dpi=100)
plot_grid(
plot_grid(
plotlist=plt_list,
ncol=2, rel_heights=c(1, 0.6), align="hv", scale=0.95
),
plot_grid(
plot_grid(
ggs_cf_scores_rast[[1]] +
theme(legend.key.width=unit(8, "pt"), legend.key.height=unit(10, "pt")),
ggs_cf_scores_rast[[2]] + theme(legend.position="none"),
ncol=1, scale=0.97
),
plot_grid(
gg_go_at + theme_ax + theme(legend.position="none", axis.text.y=element_text(size=10)),
gg_go_trans + theme_ax + theme(legend.position="none", axis.text.y=element_text(size=10)),
ncol=1, align="v", scale=0.95
),
go_leg_grob,
nrow=1, rel_widths=c(1, 2, 0.5)
),
ncol=1, rel_heights=c(7.5, 3.5), scale=0.97
)
ggsave(figurePath("7_functional_interpretation.pdf"))Saving 8.5 x 11 in image
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 sccore_1.0.1
[4] cacoa_0.2.0 cowplot_1.1.1 conos_1.4.4
[7] igraph_1.2.6 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] utf8_1.2.2 reticulate_1.22 R.utils_2.10.1
[4] tidyselect_1.1.1 RSQLite_2.2.8 AnnotationDbi_1.52.0
[7] grid_4.0.3 BiocParallel_1.24.1 Rtsne_0.15
[10] scatterpie_0.1.5 devtools_2.3.2 munsell_0.5.0
[13] ragg_0.4.1 codetools_0.2-16 withr_2.4.2
[16] GOSemSim_2.16.1 colorspace_2.0-2 Biobase_2.50.0
[19] highr_0.9 knitr_1.36 rstudioapi_0.13
[22] stats4_4.0.3 ggsignif_0.6.1 pbmcapply_1.5.0
[25] DOSE_3.16.0 labeling_0.4.2 git2r_0.27.1
[28] urltools_1.7.3 polyclip_1.10-0 farver_2.1.0
[31] bit64_4.0.5 downloader_0.4 rprojroot_2.0.2
[34] Matrix.utils_0.9.8 vctrs_0.3.8 generics_0.1.0
[37] xfun_0.26 R6_2.5.1 doParallel_1.0.16
[40] graphlayouts_0.7.1 ggbeeswarm_0.6.0 clue_0.3-59
[43] fgsea_1.16.0 cachem_1.0.6 assertthat_0.2.1
[46] promises_1.1.1 scales_1.1.1 ggraph_2.0.4
[49] enrichplot_1.10.1 beeswarm_0.4.0 gtable_0.3.0
[52] processx_3.4.5 tidygraph_1.2.0 drat_0.1.8
[55] rlang_0.4.11 systemfonts_1.0.0 GlobalOptions_0.1.2
[58] splines_4.0.3 rstatix_0.7.0 broom_0.7.9
[61] brew_1.0-6 BiocManager_1.30.10 yaml_2.2.1
[64] reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[67] backports_1.2.1 httpuv_1.5.4 qvalue_2.22.0
[70] clusterProfiler_3.18.0 tools_4.0.3 usethis_1.6.3
[73] ellipsis_0.3.2 jquerylib_0.1.4 RColorBrewer_1.1-2
[76] BiocGenerics_0.36.1 sessioninfo_1.1.1 Rcpp_1.0.7
[79] plyr_1.8.6 ps_1.4.0 prettyunits_1.1.1
[82] ggpubr_0.4.0 dendsort_0.3.3 viridis_0.6.1
[85] GetoptLong_1.0.5 S4Vectors_0.28.1 grr_0.9.5
[88] haven_2.4.1 ggrepel_0.9.1 cluster_2.1.0
[91] fs_1.5.0 data.table_1.14.2 DO.db_2.9
[94] openxlsx_4.2.3 circlize_0.4.13 triebeard_0.3.0
[97] reprex_0.3.0 whisker_0.4 matrixStats_0.61.0
[100] pkgload_1.2.1 hms_1.1.1 evaluate_0.14
[103] rio_0.5.26 RMTstat_0.3 readxl_1.3.1
[106] N2R_0.1.1 IRanges_2.24.1 gridExtra_2.3
[109] shape_1.4.6 testthat_3.0.0 compiler_4.0.3
[112] shadowtext_0.0.7 crayon_1.4.1 R.oo_1.24.0
[115] htmltools_0.5.2 mgcv_1.8-33 later_1.1.0.1
[118] lubridate_1.7.9.2 DBI_1.1.1 tweenr_1.0.1
[121] dbplyr_2.0.0 pagoda2_1.0.7 ComplexHeatmap_2.9.4
[124] MASS_7.3-53 car_3.0-10 cli_3.0.1
[127] R.methodsS3_1.8.1 parallel_4.0.3 pkgconfig_2.0.3
[130] rvcheck_0.1.8 foreign_0.8-80 xml2_1.3.2
[133] foreach_1.5.1 vipor_0.4.5 leidenAlg_0.1.0
[136] rvest_0.3.6 callr_3.5.1 digest_0.6.28
[139] fastmatch_1.1-0 rmarkdown_2.11 cellranger_1.1.0
[142] Rook_1.1-1 curl_4.3.2 rjson_0.2.20
[145] lifecycle_1.0.1 nlme_3.1-149 jsonlite_1.7.2
[148] carData_3.0-4 viridisLite_0.4.0 desc_1.3.0
[151] fansi_0.5.0 pillar_1.6.3 lattice_0.20-41
[154] GO.db_3.12.1 ggrastr_1.0.1 fastmap_1.1.0
[157] httr_1.4.2 pkgbuild_1.1.0 glue_1.4.2
[160] remotes_2.2.0 zip_2.2.0 png_0.1-7
[163] iterators_1.0.13 bit_4.0.4 ggforce_0.3.2
[166] stringi_1.7.5 blob_1.2.2 textshaping_0.2.1
[169] org.Hs.eg.db_3.12.0 memoise_2.0.0 irlba_2.3.3
[172] ape_5.5