library(tidyverse) library(tidysc) library(viridis) library(future) plan(multisession, workers=10) options(future.globals.maxSize = 10000 * 1024^2) source("functions.R") myeloid <- readRDS("myeloid.rds") my_theme = theme_bw() + theme( panel.border = element_blank(), axis.line = element_line(), panel.grid.major = element_line(size = 0.2), panel.grid.minor = element_line(size = 0.1), text = element_text(size=8), legend.position="bottom", strip.background = element_blank() ) myeloid_adj = myeloid %>% tidysc::adjust_abundance(~ integrate(sample)) myeloid_adj_cluster = myeloid_adj %>% tidysc::reduce_dimensions("UMAP") %>% tidysc::cluster_elements(resolution = 0.2) myeloid_adj_cluster %>% ggplot(aes(`UMAP 1`, `UMAP 2`)) + geom_point(aes( color =cluster), size=1, alpha=0.5) + facet_wrap(~sample, ncol=2,dir = "v") + my_theme + theme(aspect.ratio=1) myeloid_adj_cluster_cell = myeloid_adj_cluster %>% tidysc::deconvolve_cellularity() myeloid_marker_heatmap = myeloid_adj_cluster_cell %>% attr("seurat") %>% .[[1]] %>% ComputeMarkers markers_myeloid = list( macrophage = c("GPNMB", "APOE", "A2M", "NRP1", "CD11b", "CD33", "CD11c", "CD36", "ITGAM"), monocyte = c("SELL", "CFP", "PROK2", "CD14"), granulocyte = c("TRPM6", "DAPK2", "MMP25", "FUT4"), dendritic = c("CCL13", "FN1", "A2M", "CD209", "CD1C", "CCL26", "ITGAM", "CD11b", "CD11a"), other = c("FCGR3A", "CD163", "CD34", "GZMH") ) myeloid_adj_cluster_cell %>% # Extract abundance extract_abundance(markers_myeloid %>% unlist %>% unique) %>% { ggplot((.), aes(`UMAP 1`, `UMAP 2`, z = abundance_SCT)) + geom_point(data = (.) %>% dplyr::filter(abundance_SCT==0), color="grey", shape=".") + stat_summary_hex(data = (.) %>% dplyr::filter(abundance_SCT>0), binwidth = c(.2, .2), fun=sum) + facet_wrap(~transcript) + scale_fill_viridis(option="magma") + my_theme + theme(aspect.ratio=1) } (myeloid_adj_cluster_cell %>% dplyr::select( sample, cell, cluster, label_hpca, label_curated_pass_1, contains("UMAP") ) %>% pivot_longer( cols = c(cluster, label_hpca, label_curated_pass_1), names_to = "database", values_to="label" ) %>% ggplot(aes(`UMAP 1`, `UMAP 2`)) + geom_point(aes( color =label), size=1, alpha=0.5) + facet_wrap(~database, ncol=2,dir = "v") + my_theme + theme(aspect.ratio=1) ) %>% plotly::ggplotly() myeloid_adj_cluster_cell = myeloid_adj_cluster_cell %>% tidysc::left_join( c( "5" = "dendritic_CD1c+_CD74+_FCER.IgE_CD166+(activated)", "2" = "monocytic_mature_CD163-_CD16+_IFITM2+_CD11b-(citotoxic)", "7" = "monocyte_IL32+_CD2+_TNFc+(inflammatory, infiltration)", "3" = "monocyte_CD163-_(unspecific)", "1" = "monocye_CD163+_(unspecific)", "6" = "macrophage_CD163+_SELL+_CFP+CD11b+(resident,infiltration, complement)", "4" = "monocyte_(unspecific)", "0" = "monocyte_S100A8+_S100A9+_PROK2+(inflamatory)" ) %>% enframe(name = "cluster", value = "label_curated_pass_2"), by="cluster" ) myeloid_adj_cluster_cell %>% sample_n(5000) %>% plotly::plot_ly( x = ~`UMAP 1`, y = ~`UMAP 2`, z = ~`UMAP 3`, color = ~`label_curated_pass_2` ) %>% plotly::layout(legend = list(orientation = 'h'))

my_theme = theme_bw() + theme( panel.border = element_blank(), axis.line = element_line(), panel.grid.major = element_line(size = 0.2), panel.grid.minor = element_line(size = 0.1), text = element_text(size=8), legend.position="bottom", strip.background = element_blank() ) lymphoid_adj = lymphoid %>% tidysc::adjust_abundance(~ integrate(sample)) lymphoid_adj_cluster = lymphoid_adj %>% tidysc::reduce_dimensions("UMAP") %>% tidysc::cluster_elements(resolution = 0.2) lymphoid_adj_cluster %>% ggplot(aes(`UMAP 1`, `UMAP 2`)) + geom_point(aes( color =cluster), size=1, alpha=0.5) + facet_wrap(~sample, ncol=2,dir = "v") + my_theme + theme(aspect.ratio=1) lymphoid_adj_cluster_cell = lymphoid_adj_cluster %>% tidysc::deconvolve_cellularity() lymphoid_marker_heatmap = xx %>% attr("seurat") %>% .[[1]] %>% ComputeMarkers markers_lymphoid = list( t_cell = c("CD248", "ALS2CL", "FAAH2"), natural_killer = c("KRT81", "CATSPER1", "CCNJL", "MTRNR2L6"), CD4 = c("FBLN7", "ST6GALNAC1"), CD8 = c("KLRC3"), others = c("CD4", "CD8A", "CD3E", "CD3G", "CD19", "MS4A1", "NCAM1","FOXP3", "CTLA4", "PDCD1", "MKI67", "NKG7", "PECAM", "ICOS", "IL6R", "IL10RB") ) lymphoid_adj_cluster_cell %>% # Extract abundance extract_abundance(markers_lymphoid %>% unlist %>% unique) %>% { ggplot((.), aes(`UMAP 1`, `UMAP 2`, z = abundance_SCT)) + geom_point(data = (.) %>% dplyr::filter(abundance_SCT==0), color="grey", shape=".") + stat_summary_hex(data = (.) %>% dplyr::filter(abundance_SCT>0), binwidth = c(.2, .2), fun=sum) + facet_wrap(~transcript) + scale_fill_viridis(option="magma") + my_theme + theme(aspect.ratio=1) } lymphoid_adj_cluster_cell = lymphoid_adj_cluster_cell %>% tidysc::left_join( c( "3" = "b_cell_TNFR+", "6" = "pre_b_cell_CD4+_IL6R+_IL3R+_TGFB+_", "4" = "NK_IL2R+_CCL5+", "2" = "NK_T_CCL5+_KLRG1+_gzmk+(antiinflamm, checkpoint, granulation)", "5" = "CD8_cytotoxic_Letin+_(similar to 2 but KLRB1+)", "1" = "CD4_mix_CD8_CCR7+_LEF1+(naive)", "0" = "CD4_IL6R+_VIM+_IL2R+", "7" = "CD8_small_cluster_BRCA1+_NUSAP1+(proliferating)", "8" = "CD8_small_cluster_MYC+(proliferating)" ) %>% enframe(name = "cluster", value = "label_curated_pass_2"), by="cluster" ) lymphoid_adj_cluster_cell %>% sample_n(5000) %>% plotly::plot_ly( x = ~`UMAP 1`, y = ~`UMAP 2`, z = ~`UMAP 3`, color = ~`label_curated_pass_2` ) %>% plotly::layout(legend = list(orientation = 'h'))

Readme of ttBulk package