correlate ATAC data per cluster using markerpeaks or chromvar of marker peaks
09 April, 2025
# **MODIFY THIS CHUNK**
project_id <- "HDMA-public" # determines the name of the cache folder
doc_id <- "02-global_analysis/03" # determines name of the subfolder of `figures` where pngs/pdfs are saved
out <- here::here("output/", doc_id); dir.create(out, recursive = TRUE)
figout <- here::here("figures/", doc_id, "/")
cache <- paste0("~/scratch/cache/", project_id, "/", doc_id, "/")
script_path <- here::here("code/utils")1 Overview
In this analysis we correlate sample clusters using either markerpeaks or chromvar of markerpeaks.
2 Set up
# libraries
library(here)
library(ArchR)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(glue)
library(readr)
library(tibble)
library(cowplot)
library(Seurat)
library(BSgenome.Hsapiens.UCSC.hg38)
library(dendextend)
library(ComplexHeatmap)
library(chromVAR)
library(motifmatchr)
library(circlize)
# shared project scripts/functions
source(file.path(script_path, "plotting_config.R"))
source(file.path(script_path, "hdma_palettes.R"))
#source(file.path(script_path, "sj_scRNAseq_helpers.R"))
ggplot2::theme_set(theme_minimal())
# selin functions
# adapting from scrattch.hicat
# https://github.com/AllenInstitute/scrattch.hicat/blob/7ccfbc24e3a51326740d08b5b306b18afeb890d9/R/dendro.R#L49
pvclust_show_signif_gradient <- function(dend, pvclust_obj, signif_type = c("bp", "au"),
signif_col_fun = NULL, ...) {
signif_type <- match.arg(signif_type)
pvalue_per_node <- pvclust_obj$edges[[signif_type]]
ord <- rank(get_branches_heights(dend, sort = FALSE))
pvalue_per_node <- pvalue_per_node[ord]
signif_col <- signif_col_fun(100)
pvalue_by_all_nodes <- rep(NA, dendextend::nnodes(dend))
ss_leaf <- which_leaf(dend)
pvalue_by_all_nodes[!ss_leaf] <- pvalue_per_node
pvalue_by_all_nodes <- na_locf(pvalue_by_all_nodes)
the_cols <- signif_col[round(pvalue_by_all_nodes * 100)]
signif_lwd = seq(0.5,2,length.out=100)
the_lwds = signif_lwd[round(pvalue_by_all_nodes * 100)]
dend = dend %>%
assign_values_to_branches_edgePar(the_cols, "col") %>%
assign_values_to_branches_edgePar(the_lwds, "lwd") %>%
assign_values_to_branches_edgePar(pvalue_by_all_nodes, "conf")
}
build_dend <- function(mat, n_boot = 100, distance = "cor", hclust = "complete") {
pvclust_res <- pvclust::pvclust(mat,
nboot = n_boot,
method.dist = distance,
method.hclust = hclust)
dend <- as.dendrogram(pvclust_res$hclust)
dend <- dend %>% pvclust_show_signif_gradient(
pvclust_res,
signif_type = "bp",
signif_col_fun = colorRampPalette(c("gray90", "gray50", "black")))
message("done next step")
return(list(dend = dend, pvclust_res = pvclust_res))
}3 Get tissue metadata
tissue_meta <- read_tsv(here::here("code/02-global_analysis/01-organs_keep.tsv"))## Rows: 12 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): organcode, organ, iteration
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4 Get all annotations
# add organ colors
col.data.frame <- data.frame(Color=cmap_organ) %>% dplyr::mutate(organ=names(cmap_organ))
organ.code.list <- tissue_meta$organcode
all.annots <- lapply(1:length(organ.code.list), function(i){
# read cluster id annotation
annot <- read.csv(sprintf(here::here("output/01-preprocessing/02/shared/meta/%s_meta_cluster.txt"), organ.code.list[i]), sep="\t") %>% as.data.frame
rownames(annot) <- annot$cb
annot$L0_clusterID <- paste0(organ.code.list[i],"_",annot$L1_clusterID)
annot$L3_clusterID <- paste0(organ.code.list[i],"_",annot$L3_clusterName)
return(annot)
})
all.annots <- dplyr::bind_rows(all.annots)
rownames(all.annots) <- all.annots$L0_clusterID
all.annots <- all.annots %>%
mutate(Cluster = paste0(organ_code, "_",L1_clusterID)) %>%
mutate(Cluster_labelled = paste0(organ_code, "_", L1_clusterID, "_", L3_clusterName)) %>%
left_join(col.data.frame) %>%
tibble::column_to_rownames(var = "Cluster")## Joining with `by = join_by(organ)`all.annots## organ organ_code L1_clusterID L0_clusterName L1_clusterName
## AG_0 Adrenal AG 0 epi AG_epi
## AG_1 Adrenal AG 1 epi AG_epi
## AG_2 Adrenal AG 2 epi AG_epi
## AG_3 Adrenal AG 3 epi AG_epi
## AG_4 Adrenal AG 4 epi AG_epi
## AG_5 Adrenal AG 5 end AG_end
## AG_6 Adrenal AG 6 imm AG_imm
## AG_7 Adrenal AG 7 str AG_str
## AG_8 Adrenal AG 8 epi AG_epi
## AG_9 Adrenal AG 9 epi AG_epi
## BR_0 Brain BR 0 epi BR_epi
## BR_1 Brain BR 1 epi BR_epi
## BR_10 Brain BR 10 epi BR_epi
## BR_11 Brain BR 11 str BR_str
## BR_12 Brain BR 12 epi BR_epi
## BR_13 Brain BR 13 end BR_end
## BR_14 Brain BR 14 epi BR_epi
## BR_15 Brain BR 15 epi BR_epi
## BR_16 Brain BR 16 end BR_end
## BR_17 Brain BR 17 imm BR_imm
## BR_2 Brain BR 2 epi BR_epi
## BR_3 Brain BR 3 epi BR_epi
## BR_4 Brain BR 4 epi BR_epi
## BR_5 Brain BR 5 epi BR_epi
## BR_6 Brain BR 6 epi BR_epi
## BR_7 Brain BR 7 epi BR_epi
## BR_8 Brain BR 8 str BR_str
## BR_9 Brain BR 9 epi BR_epi
## EY_0 Eye EY 0 epi EY_epi
## EY_1 Eye EY 1 str EY_str
## EY_10 Eye EY 10 epi EY_epi
## EY_11 Eye EY 11 epi EY_epi
## EY_12 Eye EY 12 str EY_str
## EY_13 Eye EY 13 epi EY_epi
## EY_14 Eye EY 14 str EY_str
## EY_15 Eye EY 15 epi EY_epi
## EY_16 Eye EY 16 epi EY_epi
## EY_17 Eye EY 17 str EY_str
## EY_18 Eye EY 18 imm EY_imm
## EY_19 Eye EY 19 epi EY_epi
## EY_2 Eye EY 2 epi EY_epi
## EY_20 Eye EY 20 epi EY_epi
## EY_21 Eye EY 21 epi EY_epi
## EY_3 Eye EY 3 epi EY_epi
## EY_4 Eye EY 4 str EY_str
## EY_5 Eye EY 5 str EY_str
## EY_6 Eye EY 6 epi EY_epi
## EY_7 Eye EY 7 epi EY_epi
## EY_8 Eye EY 8 epi EY_epi
## EY_9 Eye EY 9 end EY_end
## HT_0 Heart HT 0 str HT_str
## HT_1 Heart HT 1 str HT_str
## HT_10 Heart HT 10 str HT_str
## HT_11 Heart HT 11 end HT_end
## HT_12 Heart HT 12 str HT_str
## HT_13 Heart HT 13 epi HT_epi
## HT_14 Heart HT 14 imm HT_imm
## HT_15 Heart HT 15 epi HT_epi
## HT_2 Heart HT 2 str HT_str
## HT_3 Heart HT 3 str HT_str
## HT_4 Heart HT 4 end HT_end
## HT_5 Heart HT 5 str HT_str
## HT_6 Heart HT 6 str HT_str
## HT_7 Heart HT 7 str HT_str
## HT_8 Heart HT 8 str HT_str
## HT_9 Heart HT 9 str HT_str
## LI_0 Liver LI 0 imm LI_imm
## LI_1 Liver LI 1 epi LI_epi
## LI_10 Liver LI 10 end LI_end
## LI_11 Liver LI 11 imm LI_imm
## LI_12 Liver LI 12 imm LI_imm
## LI_13 Liver LI 13 epi LI_epi
## LI_2 Liver LI 2 imm LI_imm
## LI_3 Liver LI 3 epi LI_epi
## LI_4 Liver LI 4 epi LI_epi
## LI_5 Liver LI 5 imm LI_imm
## LI_6 Liver LI 6 epi LI_epi
## LI_7 Liver LI 7 imm LI_imm
## LI_8 Liver LI 8 imm LI_imm
## LI_9 Liver LI 9 str LI_str
## LU_0 Lung LU 0 epi LU_epi
## LU_1 Lung LU 1 str LU_str
## LU_10 Lung LU 10 epi LU_epi
## LU_11 Lung LU 11 end LU_end
## LU_12 Lung LU 12 imm LU_imm
## LU_13 Lung LU 13 str LU_str
## LU_14 Lung LU 14 epi LU_epi
## LU_15 Lung LU 15 epi LU_epi
## LU_16 Lung LU 16 epi LU_epi
## LU_17 Lung LU 17 imm LU_imm
## LU_2 Lung LU 2 end LU_end
## LU_3 Lung LU 3 epi LU_epi
## LU_4 Lung LU 4 str LU_str
## LU_5 Lung LU 5 epi LU_epi
## LU_6 Lung LU 6 str LU_str
## LU_7 Lung LU 7 str LU_str
## LU_8 Lung LU 8 epi LU_epi
## LU_9 Lung LU 9 epi LU_epi
## MU_0 Muscle MU 0 str MU_str
## MU_1 Muscle MU 1 str MU_str
## MU_10 Muscle MU 10 str MU_str
## MU_11 Muscle MU 11 end MU_end
## MU_12 Muscle MU 12 str MU_str
## MU_13 Muscle MU 13 epi MU_epi
## MU_14 Muscle MU 14 str MU_str
## MU_15 Muscle MU 15 imm MU_imm
## MU_16 Muscle MU 16 str MU_str
## MU_17 Muscle MU 17 str MU_str
## MU_18 Muscle MU 18 str MU_str
## MU_19 Muscle MU 19 str MU_str
## MU_2 Muscle MU 2 str MU_str
## MU_20 Muscle MU 20 epi MU_epi
## MU_21 Muscle MU 21 str MU_str
## MU_3 Muscle MU 3 str MU_str
## MU_4 Muscle MU 4 str MU_str
## MU_5 Muscle MU 5 end MU_end
## MU_6 Muscle MU 6 end MU_end
## MU_7 Muscle MU 7 str MU_str
## MU_8 Muscle MU 8 str MU_str
## MU_9 Muscle MU 9 str MU_str
## SK_0 Skin SK 0 str SK_str
## SK_1 Skin SK 1 str SK_str
## SK_10 Skin SK 10 epi SK_epi
## SK_11 Skin SK 11 str SK_str
## SK_12 Skin SK 12 epi SK_epi
## SK_13 Skin SK 13 end SK_end
## SK_14 Skin SK 14 epi SK_epi
## SK_15 Skin SK 15 imm SK_imm
## SK_16 Skin SK 16 str SK_str
## SK_17 Skin SK 17 epi SK_epi
## SK_18 Skin SK 18 epi SK_epi
## SK_2 Skin SK 2 epi SK_epi
## SK_3 Skin SK 3 str SK_str
## SK_4 Skin SK 4 end SK_end
## SK_5 Skin SK 5 str SK_str
## SK_6 Skin SK 6 str SK_str
## SK_7 Skin SK 7 epi SK_epi
## SK_8 Skin SK 8 str SK_str
## SK_9 Skin SK 9 str SK_str
## SP_0 Spleen SP 0 str SP_str
## SP_1 Spleen SP 1 str SP_str
## SP_10 Spleen SP 10 imm SP_imm
## SP_11 Spleen SP 11 str SP_str
## SP_2 Spleen SP 2 end SP_end
## SP_3 Spleen SP 3 imm SP_imm
## SP_4 Spleen SP 4 str SP_str
## SP_5 Spleen SP 5 imm SP_imm
## SP_6 Spleen SP 6 end SP_end
## SP_7 Spleen SP 7 str SP_str
## SP_8 Spleen SP 8 imm SP_imm
## SP_9 Spleen SP 9 imm SP_imm
## ST_0 StomachEsophagus ST 0 epi ST_epi
## ST_1 StomachEsophagus ST 1 str ST_str
## ST_10 StomachEsophagus ST 10 end ST_end
## ST_11 StomachEsophagus ST 11 str ST_str
## ST_12 StomachEsophagus ST 12 str ST_str
## ST_13 StomachEsophagus ST 13 epi ST_epi
## ST_14 StomachEsophagus ST 14 str ST_str
## ST_15 StomachEsophagus ST 15 epi ST_epi
## ST_16 StomachEsophagus ST 16 epi ST_epi
## ST_17 StomachEsophagus ST 17 str ST_str
## ST_18 StomachEsophagus ST 18 str ST_str
## ST_19 StomachEsophagus ST 19 imm ST_imm
## ST_2 StomachEsophagus ST 2 str ST_str
## ST_20 StomachEsophagus ST 20 end ST_end
## ST_21 StomachEsophagus ST 21 str ST_str
## ST_3 StomachEsophagus ST 3 epi ST_epi
## ST_4 StomachEsophagus ST 4 epi ST_epi
## ST_5 StomachEsophagus ST 5 str ST_str
## ST_6 StomachEsophagus ST 6 str ST_str
## ST_7 StomachEsophagus ST 7 str ST_str
## ST_8 StomachEsophagus ST 8 str ST_str
## ST_9 StomachEsophagus ST 9 epi ST_epi
## TM_0 Thymus TM 0 imm TM_imm
## TM_1 Thymus TM 1 imm TM_imm
## TM_10 Thymus TM 10 str TM_str
## TM_11 Thymus TM 11 imm TM_imm
## TM_12 Thymus TM 12 imm TM_imm
## TM_13 Thymus TM 13 str TM_str
## TM_14 Thymus TM 14 epi TM_epi
## TM_15 Thymus TM 15 imm TM_imm
## TM_16 Thymus TM 16 epi TM_epi
## TM_17 Thymus TM 17 str TM_str
## TM_2 Thymus TM 2 imm TM_imm
## TM_3 Thymus TM 3 epi TM_epi
## TM_4 Thymus TM 4 epi TM_epi
## TM_5 Thymus TM 5 str TM_str
## TM_6 Thymus TM 6 epi TM_epi
## TM_7 Thymus TM 7 end TM_end
## TM_8 Thymus TM 8 epi TM_epi
## TM_9 Thymus TM 9 epi TM_epi
## TR_0 Thyroid TR 0 epi TR_epi
## TR_1 Thyroid TR 1 epi TR_epi
## TR_10 Thyroid TR 10 end TR_end
## TR_11 Thyroid TR 11 epi TR_epi
## TR_2 Thyroid TR 2 epi TR_epi
## TR_3 Thyroid TR 3 str TR_str
## TR_4 Thyroid TR 4 str TR_str
## TR_5 Thyroid TR 5 str TR_str
## TR_6 Thyroid TR 6 epi TR_epi
## TR_7 Thyroid TR 7 str TR_str
## TR_8 Thyroid TR 8 epi TR_epi
## TR_9 Thyroid TR 9 str TR_str
## L2_clusterID L2_clusterName
## AG_0 AG_epi1 adrenal cortex
## AG_1 AG_epi2 adrenal cortex
## AG_2 AG_epi3 adrenal cortex
## AG_3 AG_epi4 adrenal cortex
## AG_4 AG_epi5 sympathoadrenal lineage
## AG_5 AG_end endothelial
## AG_6 AG_imm macrophages
## AG_7 AG_str stromal
## AG_8 AG_epi6 sympathoadrenal lineage
## AG_9 AG_epi7 schwann cell precursors
## BR_0 BR_epi1 excitatory neurons
## BR_1 BR_epi2 dorsal radial glia
## BR_10 BR_epi10 CGE-derived inhibitory neurons
## BR_11 BR_str2 smooth muscle cells
## BR_12 BR_epi11 excitatory neurons
## BR_13 BR_end1 endothelial
## BR_14 BR_epi12 excitatory neurons
## BR_15 BR_epi13 oligodendrocyte precursor cells
## BR_16 BR_end2 endothelial
## BR_17 BR_imm macrophages
## BR_2 BR_epi3 excitatory neurons
## BR_3 BR_epi4 MGE-derived inhibitory neurons
## BR_4 BR_epi5 excitatory neurons
## BR_5 BR_epi6 excitatory neurons
## BR_6 BR_epi7 excitatory neurons
## BR_7 BR_epi8 excitatory neurons
## BR_8 BR_str1 smooth muscle cells
## BR_9 BR_epi9 MGE-derived inhibitory neurons
## EY_0 EY_epi1 retinal progenitor cell
## EY_1 EY_str1 fibroblast
## EY_10 EY_epi7 RPE
## EY_11 EY_epi8 horizontal
## EY_12 EY_str4 smooth muscle
## EY_13 EY_epi9 astrocyte
## EY_14 EY_str5 myogenic progenitor
## EY_15 EY_epi10 ciliated pigmented epithelium
## EY_16 EY_epi11 schwann
## EY_17 EY_str6 melanocyte
## EY_18 EY_imm macrophage
## EY_19 EY_epi12 cornea
## EY_2 EY_epi2 rod
## EY_20 EY_epi13 amacrine
## EY_21 EY_epi14 cornea
## EY_3 EY_epi3 amacrine
## EY_4 EY_str2 trabecular meshwork
## EY_5 EY_str3 extraocular muscle
## EY_6 EY_epi4 retinal ganglion
## EY_7 EY_epi5 bipolar
## EY_8 EY_epi6 cone
## EY_9 EY_end vascular endothelium
## HT_0 HT_str1 vCM
## HT_1 HT_str2 vCM
## HT_10 HT_str10 fibroblast
## HT_11 HT_end2 endocardial
## HT_12 HT_str11 fibroblast
## HT_13 HT_epi1 neuronal
## HT_14 HT_imm macrophage
## HT_15 HT_epi2 epicardial
## HT_2 HT_str3 aCM
## HT_3 HT_str4 fibroblast
## HT_4 HT_end1 endocardial
## HT_5 HT_str5 aCM
## HT_6 HT_str6 fibroblast
## HT_7 HT_str7 vCM
## HT_8 HT_str8 cycling vCM
## HT_9 HT_str9 pericyte
## LI_0 LI_imm1 cycling erythroblast
## LI_1 LI_epi1 hepatocyte
## LI_10 LI_end endothelial
## LI_11 LI_imm6 B cell progenitor
## LI_12 LI_imm7 megakaryocyte
## LI_13 LI_epi5 cholangiocyte
## LI_2 LI_imm2 erythroblast
## LI_3 LI_epi2 hepatocyte
## LI_4 LI_epi3 hepatocyte
## LI_5 LI_imm3 early erythroblast
## LI_6 LI_epi4 hepatocyte
## LI_7 LI_imm4 Kupffer
## LI_8 LI_imm5 erythroblast
## LI_9 LI_str stellate
## LU_0 LU_epi1 airway epithelial progenitor
## LU_1 LU_str1 airway fibroblast
## LU_10 LU_epi6 airway epithelial progenitor
## LU_11 LU_end2 lymphatics
## LU_12 LU_imm1 macrophage
## LU_13 LU_str5 airway smooth muscle
## LU_14 LU_epi7 pulmonary neuroendocrine
## LU_15 LU_epi8 ciliated
## LU_16 LU_epi9 mesothelial
## LU_17 LU_imm2 megakaryocyte
## LU_2 LU_end1 vascular endothelial
## LU_3 LU_epi2 ciliated
## LU_4 LU_str2 airway smooth muscle
## LU_5 LU_epi3 airway epithelial progenitor
## LU_6 LU_str3 pericytes
## LU_7 LU_str4 airway fibroblast
## LU_8 LU_epi4 airway epithelial progenitor
## LU_9 LU_epi5 airway epithelial progenitor
## MU_0 MU_str1 myocytes (fast/Type II)
## MU_1 MU_str2 smooth muscle
## MU_10 MU_str9 myocytes (slow/Type I)
## MU_11 MU_end3 endothelial
## MU_12 MU_str10 myogenic progenitors
## MU_13 MU_epi1 keratinocytes
## MU_14 MU_str11 pericytes
## MU_15 MU_imm macrophages
## MU_16 MU_str12 myocytes (slow/Type I)
## MU_17 MU_str13 myoblasts
## MU_18 MU_str14 myocytes
## MU_19 MU_str15 pericytes
## MU_2 MU_str3 myocytes (fast/Type II)
## MU_20 MU_epi2 keratinocytes
## MU_21 MU_str16 myogenic progenitors
## MU_3 MU_str4 myogenic progenitors
## MU_4 MU_str5 fibroblasts
## MU_5 MU_end1 endothelial
## MU_6 MU_end2 endothelial
## MU_7 MU_str6 myoblasts (cycling)
## MU_8 MU_str7 smooth muscle
## MU_9 MU_str8 tenogenic cells
## SK_0 SK_str1 fibroblast
## SK_1 SK_str2 fibroblast
## SK_10 SK_epi3 melanocyte
## SK_11 SK_str8 skeletal muscle
## SK_12 SK_epi4 interfollicular cells
## SK_13 SK_end2 lymphatic endothelium
## SK_14 SK_epi5 melanocyte progenitor
## SK_15 SK_imm macrophage
## SK_16 SK_str9 fibroblast
## SK_17 SK_epi6 keratinocyte
## SK_18 SK_epi7 epidermal progenitor
## SK_2 SK_epi1 keratinocyte
## SK_3 SK_str3 fibroblast
## SK_4 SK_end1 vascular endothelium
## SK_5 SK_str4 fibroblast
## SK_6 SK_str5 pericyte
## SK_7 SK_epi2 schwann
## SK_8 SK_str6 skeletal muscle
## SK_9 SK_str7 fibroblast
## SP_0 SP_str1 Fibroblast
## SP_1 SP_str2 Fibroblast
## SP_10 SP_imm5 Erythroblast
## SP_11 SP_str5 Pericyte
## SP_2 SP_end1 Endothelial progenitor
## SP_3 SP_imm1 B cells
## SP_4 SP_str3 Fibroblast
## SP_5 SP_imm2 Macrophage
## SP_6 SP_end2 Endothelial progenitor
## SP_7 SP_str4 Fibroblast
## SP_8 SP_imm3 Megakaryocyte
## SP_9 SP_imm4 NK T cells
## ST_0 ST_epi1 chief and neck cells
## ST_1 ST_str1 smooth muscle
## ST_10 ST_end1 endothelial
## ST_11 ST_str7 cycling stromal
## ST_12 ST_str8 pericytes
## ST_13 ST_epi5 epithelial basal/keratinocyte
## ST_14 ST_str9 interstitial cells of Cajal
## ST_15 ST_epi6 ciliated epithelium
## ST_16 ST_epi7 endocrine cells
## ST_17 ST_str10 smooth muscle
## ST_18 ST_str11 MYH3+ smooth muscle
## ST_19 ST_imm macrophage
## ST_2 ST_str2 smooth muscle
## ST_20 ST_end2 lymphatics
## ST_21 ST_str12 mesothelial
## ST_3 ST_epi2 parietal cells
## ST_4 ST_epi3 keratinocytes/tuft cells
## ST_5 ST_str3 fibroblasts
## ST_6 ST_str4 fibroblasts
## ST_7 ST_str5 smooth muscle
## ST_8 ST_str6 enteric glia
## ST_9 ST_epi4 enteric neurons
## TM_0 TM_imm1 CD8+ CD4+ thymocytes
## TM_1 TM_imm2 CD8+ CD4+ thymocytes
## TM_10 TM_str2 vascular smooth muscle
## TM_11 TM_imm4 dendritic cells
## TM_12 TM_imm5 macrophages
## TM_13 TM_str3 fibroblasts
## TM_14 TM_epi6 medullary TEC (myoid Type I)
## TM_15 TM_imm6 B cells
## TM_16 TM_epi7 medullary TEC (neuromuscular)
## TM_17 TM_str4 fibroblasts
## TM_2 TM_imm3 CD8+ CD4+ thymocytes
## TM_3 TM_epi1 medullary TEC (myoid Type IIa)
## TM_4 TM_epi2 medullary TEC (myoid Type IIa)
## TM_5 TM_str1 fibroblasts
## TM_6 TM_epi3 cortical TEC
## TM_7 TM_end endothelial cells
## TM_8 TM_epi4 medullary TEC (myoid)
## TM_9 TM_epi5 medullary TEC (myoid)
## TR_0 TR_epi1 thyroid follicular
## TR_1 TR_epi2 thyroid follicular
## TR_10 TR_end endothelial
## TR_11 TR_epi6 parathyroid
## TR_2 TR_epi3 thyroid follicular
## TR_3 TR_str1 stromal
## TR_4 TR_str2 fibroblast
## TR_5 TR_str3 skeletal muscle
## TR_6 TR_epi4 thyroid follicular
## TR_7 TR_str4 thyroid follicular
## TR_8 TR_epi5 thyroid follicular
## TR_9 TR_str5 smooth muscle
## L3_clusterName ncell median_numi median_ngene
## AG_0 adrenal cortex 1 823 9498.0 3280.0
## AG_1 adrenal cortex 2 576 6827.5 3114.0
## AG_2 adrenal cortex 3 511 10567.0 3661.0
## AG_3 adrenal cortex 4 483 6340.0 2487.0
## AG_4 sympathoadrenal lineage 1 155 5262.0 2564.0
## AG_5 endothelial 115 4117.0 2188.0
## AG_6 macrophages 89 4511.0 2478.0
## AG_7 stromal 77 5502.0 2741.0
## AG_8 sympathoadrenal lineage 2 29 5121.0 2596.0
## AG_9 schwann cell precursors 25 4319.0 2382.0
## BR_0 excitatory neurons 1 9750 3355.5 1803.0
## BR_1 dorsal radial glia 9436 5506.0 2694.5
## BR_10 CGE-derived inhibitory neurons 3502 3304.0 1801.0
## BR_11 smooth muscle cells 2 3373 2095.0 1464.0
## BR_12 excitatory neurons 7 3372 7511.0 3102.5
## BR_13 endothelial 1 3234 2067.0 1491.5
## BR_14 excitatory neurons 8 2996 4389.0 2140.5
## BR_15 oligodendrocyte precursor cells 1070 5853.5 2692.0
## BR_16 endothelial 2 648 12580.0 4536.0
## BR_17 macrophages 335 4456.0 2425.0
## BR_2 excitatory neurons 2 6705 6587.0 2809.0
## BR_3 MGE-derived inhibitory neurons 1 4939 10606.0 3750.0
## BR_4 excitatory neurons 3 4766 2563.0 1526.0
## BR_5 excitatory neurons 4 4721 7868.0 3003.0
## BR_6 excitatory neurons 5 4463 12916.0 4204.0
## BR_7 excitatory neurons 6 4405 4842.0 2302.0
## BR_8 smooth muscle cells 1 4070 4973.0 2856.5
## BR_9 MGE-derived inhibitory neurons 2 3812 4171.0 2099.0
## EY_0 retinal progenitor cell 10546 1747.0 1304.0
## EY_1 fibroblast 6420 1773.5 1328.0
## EY_10 RPE 2441 2226.0 1593.0
## EY_11 horizontal 2303 2353.0 1583.0
## EY_12 smooth muscle 2141 1748.0 1295.0
## EY_13 astrocyte 1932 1884.5 1390.5
## EY_14 myogenic progenitor 1923 2012.0 1484.0
## EY_15 ciliated pigmented epithelium 1081 2053.0 1490.0
## EY_16 schwann 1017 2043.0 1464.0
## EY_17 melanocyte 686 2210.0 1563.0
## EY_18 macrophage 640 2468.5 1754.0
## EY_19 cornea 1 519 1948.0 1441.0
## EY_2 rod 6100 1139.5 917.0
## EY_20 amacrine 2 329 3041.0 1853.0
## EY_21 cornea 2 67 4192.0 2493.0
## EY_3 amacrine 1 5672 1599.0 1161.0
## EY_4 trabecular meshwork 4704 1844.0 1357.5
## EY_5 extraocular muscle 4008 3553.5 2147.0
## EY_6 retinal ganglion 3616 5408.5 2844.5
## EY_7 bipolar 2907 1305.0 1000.0
## EY_8 cone 2592 2059.5 1438.0
## EY_9 vascular endothelium 2451 2297.0 1665.0
## HT_0 vCM 1 33620 2865.0 1588.0
## HT_1 vCM 2 14883 2702.0 1530.0
## HT_10 fibroblast 3 2550 5004.5 2657.0
## HT_11 endocardial 2 2438 4112.0 2359.5
## HT_12 fibroblast 4 1728 2145.5 1423.5
## HT_13 neuronal 1036 2440.0 1616.0
## HT_14 macrophage 869 2386.0 1586.0
## HT_15 epicardial 528 5563.0 2913.5
## HT_2 aCM 1 13925 4281.0 2404.0
## HT_3 fibroblast 1 10121 2293.0 1507.0
## HT_4 endocardial 1 8253 4041.0 2346.0
## HT_5 aCM 2 8102 7503.0 3492.0
## HT_6 fibroblast 2 7147 1699.0 1256.0
## HT_7 vCM 3 5716 1781.5 1166.0
## HT_8 cycling vCM 5654 3687.0 2014.5
## HT_9 pericyte 3684 2350.0 1526.0
## LI_0 cycling erythroblast 16461 1480.0 1132.0
## LI_1 hepatocyte 1 14247 2051.0 1477.0
## LI_10 endothelial 1674 1773.5 1348.0
## LI_11 B cell progenitor 1154 1469.0 1144.5
## LI_12 megakaryocyte 1042 1960.0 1406.0
## LI_13 cholangiocyte 1004 1986.0 1462.5
## LI_2 erythroblast 1 8716 1250.0 931.5
## LI_3 hepatocyte 2 8171 1655.0 1250.0
## LI_4 hepatocyte 3 3807 3849.0 2315.0
## LI_5 early erythroblast 3762 1846.5 1415.5
## LI_6 hepatocyte 4 3701 2070.0 1521.0
## LI_7 Kupffer 3467 1800.0 1358.0
## LI_8 erythroblast 2 2362 1492.0 1168.5
## LI_9 stellate 2239 1769.0 1368.0
## LU_0 airway epithelial progenitor 1 28899 1839.0 1385.0
## LU_1 airway fibroblast 1 22426 1674.0 1256.5
## LU_10 airway epithelial progenitor 5 6086 3427.0 2111.0
## LU_11 lymphatics 3748 3297.0 2198.0
## LU_12 macrophage 3747 2134.0 1566.0
## LU_13 airway smooth muscle 2 3507 3113.0 1915.0
## LU_14 pulmonary neuroendocrine 3041 3994.0 2447.0
## LU_15 ciliated 2 1709 3981.0 2500.0
## LU_16 mesothelial 395 3727.0 2393.0
## LU_17 megakaryocyte 284 2742.5 1840.0
## LU_2 vascular endothelial 21309 2530.0 1768.0
## LU_3 ciliated 1 13959 3880.0 2488.0
## LU_4 airway smooth muscle 1 12194 1667.0 1260.5
## LU_5 airway epithelial progenitor 2 10353 1737.0 1335.0
## LU_6 pericytes 9897 2429.0 1696.0
## LU_7 airway fibroblast 2 8418 1566.0 1134.0
## LU_8 airway epithelial progenitor 3 8389 2206.0 1529.0
## LU_9 airway epithelial progenitor 4 6559 2413.0 1685.0
## MU_0 myocytes (fast/Type II) 1 12909 2491.0 1576.0
## MU_1 smooth muscle 1 8838 1226.0 944.0
## MU_10 myocytes (slow/Type I) 1 4136 1845.0 1400.0
## MU_11 endothelial 3 3590 1149.0 955.0
## MU_12 myogenic progenitors 2 3372 1454.0 1148.5
## MU_13 keratinocytes 1 2660 3016.0 1987.5
## MU_14 pericytes 1 1887 1961.0 1403.0
## MU_15 macrophages 1755 2404.0 1678.0
## MU_16 myocytes (slow/Type I) 2 1263 4471.0 2589.0
## MU_17 myoblasts 1008 3007.5 1901.5
## MU_18 myocytes 1006 3318.0 2012.0
## MU_19 pericytes 2 928 1594.5 1183.0
## MU_2 myocytes (fast/Type II) 2 7879 1991.0 1461.0
## MU_20 keratinocytes 2 921 3651.0 2256.0
## MU_21 myogenic progenitors 3 546 3320.5 2087.5
## MU_3 myogenic progenitors 1 7496 2613.5 1720.0
## MU_4 fibroblasts 6093 1575.0 1149.0
## MU_5 endothelial 1 5955 3137.0 2041.0
## MU_6 endothelial 2 5944 3318.5 2170.0
## MU_7 myoblasts (cycling) 5393 1883.0 1368.0
## MU_8 smooth muscle 2 4571 1382.0 1062.0
## MU_9 tenogenic cells 4392 2146.0 1510.0
## SK_0 fibroblast 1 9770 1045.0 839.0
## SK_1 fibroblast 2 6716 997.0 802.0
## SK_10 melanocyte 1878 2086.0 1538.5
## SK_11 skeletal muscle 2 1799 1556.0 1165.0
## SK_12 interfollicular cells 1731 1624.0 1268.0
## SK_13 lymphatic endothelium 1171 2216.0 1620.0
## SK_14 melanocyte progenitor 764 1337.0 1014.5
## SK_15 macrophage 632 1968.5 1478.0
## SK_16 fibroblast 6 323 2437.0 1680.0
## SK_17 keratinocyte 2 243 2745.0 1923.0
## SK_18 epidermal progenitor 226 3222.0 2219.0
## SK_2 keratinocyte 1 6480 1614.5 1251.5
## SK_3 fibroblast 3 5251 2099.0 1546.0
## SK_4 vascular endothelium 4889 2314.0 1684.0
## SK_5 fibroblast 4 4846 2110.0 1549.0
## SK_6 pericyte 3366 1694.5 1294.0
## SK_7 schwann 3111 2319.0 1685.0
## SK_8 skeletal muscle 1 2354 3035.0 2104.5
## SK_9 fibroblast 5 2251 1157.0 923.0
## SP_0 Fibroblast 1 6992 1306.0 1052.0
## SP_1 Fibroblast 2 4525 1280.0 1052.0
## SP_10 Erythroblast 1310 1247.5 1026.5
## SP_11 Pericyte 605 1599.0 1237.0
## SP_2 Endothelial progenitor 1 3925 1261.0 1024.0
## SP_3 B cells 3866 1148.0 955.0
## SP_4 Fibroblast 3 2645 1870.0 1426.0
## SP_5 Macrophage 2635 1228.0 1010.0
## SP_6 Endothelial progenitor 2 2012 1364.5 1106.0
## SP_7 Fibroblast 4 1787 1538.0 1183.0
## SP_8 Megakaryocyte 1462 1270.0 1008.5
## SP_9 NK T cells 1413 1031.0 869.0
## ST_0 chief and neck cells 11861 1884.0 1402.0
## ST_1 smooth muscle 1 7577 1717.0 1205.0
## ST_10 endothelial 3381 2925.0 1952.0
## ST_11 cycling stromal 3130 2137.0 1507.5
## ST_12 pericytes 2550 2157.5 1496.5
## ST_13 epithelial basal/keratinocyte 2197 2630.0 1760.0
## ST_14 interstitial cells of Cajal 1893 1928.0 1347.0
## ST_15 ciliated epithelium 1430 5941.5 2942.5
## ST_16 endocrine cells 1289 2003.0 1438.0
## ST_17 smooth muscle 4 1070 2215.0 1428.5
## ST_18 MYH3+ smooth muscle 1003 1649.0 1225.0
## ST_19 macrophage 595 1753.0 1299.0
## ST_2 smooth muscle 2 6860 2179.0 1561.5
## ST_20 lymphatics 383 4337.0 2589.0
## ST_21 mesothelial 243 3002.0 2020.0
## ST_3 parietal cells 6473 2339.0 1604.0
## ST_4 keratinocytes/tuft cells 5869 3064.0 1883.0
## ST_5 fibroblasts 1 5672 1577.0 1173.0
## ST_6 fibroblasts 2 5555 1804.0 1287.0
## ST_7 smooth muscle 3 5518 2597.5 1553.5
## ST_8 enteric glia 4858 2408.5 1629.0
## ST_9 enteric neurons 4256 2897.5 1811.0
## TM_0 CD8+ CD4+ thymocytes 1 9138 1641.0 1209.0
## TM_1 CD8+ CD4+ thymocytes 2 6268 2216.5 1567.5
## TM_10 vascular smooth muscle 812 2944.0 1952.0
## TM_11 dendritic cells 787 2983.0 2012.0
## TM_12 macrophages 554 2802.0 1952.0
## TM_13 fibroblasts 2 380 2547.5 1779.0
## TM_14 medullary TEC (myoid Type I) 358 3772.0 2199.0
## TM_15 B cells 290 2530.0 1743.0
## TM_16 medullary TEC (neuromuscular) 98 3763.0 2211.5
## TM_17 fibroblasts 3 93 4355.0 2727.0
## TM_2 CD8+ CD4+ thymocytes 3 5635 3161.0 2069.0
## TM_3 medullary TEC (myoid Type IIa) 1 5033 2833.0 1753.0
## TM_4 medullary TEC (myoid Type IIa) 2 3228 5237.0 3106.0
## TM_5 fibroblasts 1 2959 2706.0 1832.0
## TM_6 cortical TEC 2783 3354.0 2183.0
## TM_7 endothelial cells 1332 3690.0 2352.5
## TM_8 medullary TEC (myoid) 1 1086 2212.0 1594.0
## TM_9 medullary TEC (myoid) 2 868 4783.5 2854.5
## TR_0 thyroid follicular 1 2956 877.0 690.5
## TR_1 thyroid follicular 2 1833 1729.0 1253.0
## TR_10 endothelial 104 1424.0 1098.0
## TR_11 parathyroid 38 1086.5 851.5
## TR_2 thyroid follicular 3 1280 1576.5 1132.0
## TR_3 stromal 842 1211.0 945.0
## TR_4 fibroblast 781 1442.0 1092.0
## TR_5 skeletal muscle 643 1633.0 1137.0
## TR_6 thyroid follicular 4 299 3412.0 2199.0
## TR_7 thyroid follicular 5 205 5072.0 2997.0
## TR_8 thyroid follicular 6 200 3141.0 1972.0
## TR_9 smooth muscle 118 1315.5 1012.5
## median_pctmt median_nfrags median_tss median_frip
## AG_0 0.008694896 6784.0 9.8320 0.2906086
## AG_1 0.000000000 4341.5 10.3445 0.2400227
## AG_2 0.008910274 5692.0 9.6830 0.2679032
## AG_3 0.000000000 5519.0 9.8760 0.3020710
## AG_4 0.000000000 4425.0 10.1390 0.2104465
## AG_5 0.006551792 4192.0 10.1250 0.2417165
## AG_6 0.000000000 4198.0 9.2670 0.2150213
## AG_7 0.012624669 4658.0 9.6150 0.2005007
## AG_8 0.008352126 3915.0 8.7370 0.2073171
## AG_9 0.000000000 4698.0 10.0740 0.2070255
## BR_0 0.000000000 1866.0 11.8610 0.3870669
## BR_1 0.013566987 3350.0 9.7770 0.3424843
## BR_10 0.017761216 1721.0 10.3635 0.3210436
## BR_11 0.000000000 2463.0 9.0570 0.3364532
## BR_12 0.016878451 4449.0 6.6585 0.2419875
## BR_13 0.000000000 2956.5 11.9775 0.4422478
## BR_14 0.019928261 1770.5 8.3715 0.2613537
## BR_15 0.018828005 3057.0 8.0480 0.2774552
## BR_16 0.026992980 7422.0 7.7325 0.2867702
## BR_17 0.012419660 2876.0 8.7530 0.2997575
## BR_2 0.017807853 3471.0 7.9430 0.2751980
## BR_3 0.023658000 4681.0 8.3430 0.2800320
## BR_4 0.000000000 1651.0 8.5700 0.2553894
## BR_5 0.021318552 3541.0 8.9220 0.3049986
## BR_6 0.010365111 6408.0 7.8950 0.3039353
## BR_7 0.017404421 4021.0 8.0160 0.2696100
## BR_8 0.006265568 6185.5 9.6930 0.3446895
## BR_9 0.018750308 2208.0 9.0720 0.2879843
## EY_0 0.000000000 5474.5 8.5635 0.3559524
## EY_1 0.000000000 3965.0 7.3525 0.2440456
## EY_10 0.000000000 6760.0 7.3900 0.2914894
## EY_11 0.000000000 5421.0 8.9110 0.3297352
## EY_12 0.000000000 3926.0 8.2430 0.2751092
## EY_13 0.000000000 5165.5 8.5235 0.3331338
## EY_14 0.000000000 3226.0 7.6910 0.2893633
## EY_15 0.000000000 6337.0 8.1430 0.2944911
## EY_16 0.000000000 4000.0 7.7680 0.2543410
## EY_17 0.000000000 5778.5 7.0585 0.2545192
## EY_18 0.000000000 5739.5 7.3670 0.2633844
## EY_19 0.000000000 5142.0 8.3490 0.3057549
## EY_2 0.000000000 4668.5 9.5120 0.3241456
## EY_20 0.000000000 6701.0 8.1680 0.2851702
## EY_21 0.000000000 7417.0 8.3510 0.2563750
## EY_3 0.000000000 4972.5 8.9510 0.3095179
## EY_4 0.000000000 4115.0 7.2200 0.2540445
## EY_5 0.000000000 5811.0 8.0715 0.2820939
## EY_6 0.000000000 9299.0 6.8285 0.2790853
## EY_7 0.000000000 5283.0 9.2810 0.3275157
## EY_8 0.000000000 4681.0 8.6270 0.3193018
## EY_9 0.000000000 5664.0 6.9130 0.2389978
## HT_0 0.000000000 4297.0 13.0730 0.5849987
## HT_1 0.000000000 4711.0 14.5430 0.6116706
## HT_10 0.029456571 9394.0 10.7620 0.4604975
## HT_11 0.026073392 7079.5 11.0075 0.4731459
## HT_12 0.000000000 9705.0 7.2080 0.2459824
## HT_13 0.007520954 9193.5 8.0300 0.2719164
## HT_14 0.035555556 4379.0 11.2630 0.4472385
## HT_15 0.032058994 11645.5 9.3880 0.3545280
## HT_2 0.044483986 5549.0 10.2720 0.4554882
## HT_3 0.000000000 4224.0 11.2210 0.4792648
## HT_4 0.026326181 8340.0 12.7750 0.5446288
## HT_5 0.049415836 7897.0 11.4300 0.5061523
## HT_6 0.000000000 9416.0 6.4470 0.2140711
## HT_7 0.000000000 5337.5 12.9645 0.5901435
## HT_8 0.000000000 6596.5 12.6755 0.5622221
## HT_9 0.000000000 4504.0 11.8595 0.5127287
## LI_0 0.000000000 6066.0 14.2760 0.5300218
## LI_1 0.000000000 7801.0 12.2550 0.5354654
## LI_10 0.021967808 7757.0 11.8550 0.4986895
## LI_11 0.033882012 6372.0 12.2955 0.4746923
## LI_12 0.000000000 9223.5 10.1870 0.4494615
## LI_13 0.000000000 7509.5 11.8260 0.5161030
## LI_2 0.000000000 4234.5 16.4680 0.5759017
## LI_3 0.000000000 8819.0 12.8870 0.5419121
## LI_4 0.015588465 6913.0 11.2550 0.5166667
## LI_5 0.000000000 9036.5 9.3785 0.4327077
## LI_6 0.017373176 9119.0 13.3640 0.5323080
## LI_7 0.000000000 7607.0 11.0190 0.4858573
## LI_8 0.033444850 5765.5 13.6765 0.5003090
## LI_9 0.045977011 7426.0 12.4530 0.4615881
## LU_0 0.000000000 7820.0 11.1980 0.4708435
## LU_1 0.000000000 5717.0 9.6560 0.4111272
## LU_10 0.000000000 4225.0 9.5650 0.4305648
## LU_11 0.000000000 9018.0 10.4675 0.4077182
## LU_12 0.000000000 6820.0 10.0760 0.4071856
## LU_13 0.000000000 3664.0 8.6170 0.3560707
## LU_14 0.000000000 8665.0 10.1120 0.4037476
## LU_15 0.000000000 10085.0 10.7760 0.4472972
## LU_16 0.000000000 9957.0 10.1170 0.3761694
## LU_17 0.000000000 8176.0 10.2535 0.4212004
## LU_2 0.000000000 7937.0 10.8950 0.4397472
## LU_3 0.000000000 10875.0 10.7160 0.4447208
## LU_4 0.000000000 5780.5 10.8435 0.4538845
## LU_5 0.000000000 6975.0 10.4590 0.4261281
## LU_6 0.000000000 7272.0 10.1860 0.4290918
## LU_7 0.000000000 2819.0 10.6270 0.4685009
## LU_8 0.000000000 3804.0 12.2710 0.5301370
## LU_9 0.000000000 6574.0 10.7530 0.4515553
## MU_0 0.000000000 4924.0 9.2820 0.3641249
## MU_1 0.000000000 3615.5 8.4270 0.2421676
## MU_10 0.000000000 1644.0 7.6600 0.2488060
## MU_11 0.000000000 1671.5 8.8250 0.2584469
## MU_12 0.000000000 1581.0 6.6480 0.1691530
## MU_13 0.000000000 7178.5 7.1465 0.2303479
## MU_14 0.000000000 4554.0 8.5580 0.3142937
## MU_15 0.000000000 5947.0 8.5440 0.2917408
## MU_16 0.017576752 6073.0 8.4300 0.3162202
## MU_17 0.000000000 4162.0 6.2295 0.1954142
## MU_18 0.000000000 6221.0 8.8710 0.3319132
## MU_19 0.000000000 4097.0 8.2765 0.2476429
## MU_2 0.000000000 1667.0 8.2940 0.2873815
## MU_20 0.000000000 4834.0 6.6890 0.2181361
## MU_21 0.000000000 7041.0 6.9000 0.2197898
## MU_3 0.000000000 5669.0 7.6355 0.2170886
## MU_4 0.000000000 3530.0 9.1570 0.3380994
## MU_5 0.000000000 6804.0 8.6260 0.3092818
## MU_6 0.000000000 8562.0 8.9390 0.2570856
## MU_7 0.000000000 4248.0 9.3690 0.3424157
## MU_8 0.000000000 3719.0 10.5890 0.2993061
## MU_9 0.000000000 5618.0 8.3485 0.3076372
## SK_0 0.000000000 5309.0 11.2075 0.3673519
## SK_1 0.000000000 5082.0 9.0645 0.2814145
## SK_10 0.000000000 11650.5 9.7015 0.3197439
## SK_11 0.000000000 7535.0 8.1570 0.2758354
## SK_12 0.000000000 11598.0 8.1770 0.2690161
## SK_13 0.000000000 11785.0 9.0030 0.2726693
## SK_14 0.000000000 6240.0 9.1795 0.3365651
## SK_15 0.000000000 11487.0 8.0150 0.2447416
## SK_16 0.000000000 12720.0 8.8010 0.2663116
## SK_17 0.000000000 15798.0 8.5560 0.3043914
## SK_18 0.000000000 20768.5 8.0565 0.2561533
## SK_2 0.000000000 10549.5 8.1130 0.2869286
## SK_3 0.000000000 11838.0 9.2720 0.2968277
## SK_4 0.000000000 12033.0 9.7170 0.2975901
## SK_5 0.000000000 11837.5 8.7125 0.2684831
## SK_6 0.000000000 9537.5 9.0510 0.2878892
## SK_7 0.000000000 13144.0 10.2560 0.3130526
## SK_8 0.000000000 15036.5 6.7370 0.2249189
## SK_9 0.000000000 5735.0 12.1460 0.3830239
## SP_0 0.000000000 10301.5 9.1640 0.3842029
## SP_1 0.000000000 9449.0 8.0220 0.2990939
## SP_10 0.000000000 11752.0 10.3905 0.3671281
## SP_11 0.000000000 11802.0 8.7450 0.3540848
## SP_2 0.000000000 9814.0 9.6270 0.4070856
## SP_3 0.000000000 9672.0 8.7675 0.3449239
## SP_4 0.000000000 12785.0 8.1290 0.3132419
## SP_5 0.000000000 11904.0 7.7250 0.3233659
## SP_6 0.000000000 11179.0 8.3495 0.3201988
## SP_7 0.000000000 11094.0 8.5640 0.3604484
## SP_8 0.000000000 8970.0 9.4900 0.3690736
## SP_9 0.000000000 7903.0 9.2720 0.3540741
## ST_0 0.000000000 6182.0 6.6410 0.3739692
## ST_1 0.000000000 3480.0 9.4640 0.4137931
## ST_10 0.000000000 8246.0 6.7800 0.2916590
## ST_11 0.000000000 5848.0 7.7360 0.3343433
## ST_12 0.000000000 5742.0 6.6140 0.2982265
## ST_13 0.000000000 5906.0 7.6730 0.3458430
## ST_14 0.000000000 4286.0 7.2950 0.3242678
## ST_15 0.000000000 8573.0 6.7095 0.3558540
## ST_16 0.000000000 5616.0 7.5370 0.3401291
## ST_17 0.000000000 3380.0 7.5435 0.3645593
## ST_18 0.000000000 9000.0 7.5520 0.3222845
## ST_19 0.000000000 4929.0 6.8560 0.2915455
## ST_2 0.000000000 8330.5 7.0580 0.3101341
## ST_20 0.008760019 12228.0 6.3050 0.2757800
## ST_21 0.000000000 9200.0 6.4170 0.2511874
## ST_3 0.000000000 6494.0 6.8990 0.3467225
## ST_4 0.000000000 5267.0 7.8290 0.4141766
## ST_5 0.000000000 4232.5 6.9900 0.3065838
## ST_6 0.000000000 4140.0 6.8950 0.3107011
## ST_7 0.000000000 3830.0 8.5245 0.3725678
## ST_8 0.000000000 6390.5 7.9080 0.3282216
## ST_9 0.000000000 7484.5 7.7910 0.3051967
## TM_0 0.000000000 7320.5 10.3715 0.4011234
## TM_1 0.000000000 8730.5 9.9710 0.3883311
## TM_10 0.016287522 9510.0 8.2820 0.2570674
## TM_11 0.000000000 18951.0 9.0350 0.3684325
## TM_12 0.000000000 11846.0 9.1155 0.3482074
## TM_13 0.000000000 11344.0 9.3675 0.3222823
## TM_14 0.017678080 9345.0 9.6745 0.3108051
## TM_15 0.000000000 10797.5 8.9465 0.3503555
## TM_16 0.015671902 10618.5 10.2670 0.3287925
## TM_17 0.000000000 14998.0 8.0290 0.2405268
## TM_2 0.000000000 15301.0 9.7400 0.3649286
## TM_3 0.000000000 7791.0 10.3690 0.3334088
## TM_4 0.052861108 6042.0 8.5540 0.2751473
## TM_5 0.000000000 8797.0 7.8470 0.2495930
## TM_6 0.000000000 15137.0 9.2760 0.3598779
## TM_7 0.020995179 10580.0 8.5815 0.2643730
## TM_8 0.000000000 7036.5 9.9080 0.2941622
## TM_9 0.023935129 15510.0 6.9870 0.2000755
## TR_0 0.094652175 2672.5 8.2640 0.2180735
## TR_1 0.075642965 5348.0 8.3060 0.2246005
## TR_10 0.000000000 4631.5 9.4335 0.2279165
## TR_11 0.000000000 5151.0 9.5625 0.2916235
## TR_2 0.000000000 4630.5 9.6495 0.3123944
## TR_3 0.000000000 3763.0 9.0520 0.2203774
## TR_4 0.000000000 5173.0 8.1310 0.2126551
## TR_5 0.000000000 4934.0 9.3890 0.2602256
## TR_6 0.086580087 9162.0 8.3230 0.2308157
## TR_7 0.088222320 14047.0 8.5760 0.2340779
## TR_8 0.000000000 6855.0 9.3005 0.3006673
## TR_9 0.000000000 4462.5 9.0155 0.2268618
## note
## AG_0 NR5A1+ CYP11A1/CYP11B1+; mostly PCW21; groups with 2 and 3 in cluster tree
## AG_1 AGTR1+ likely zona glomerulosa; all PCW17
## AG_2 NR5A1+ AKR1B1+ SULT2A1+ CYP11A1/CYP11B1+; all PCW18
## AG_3 NR5A1+; mosly PCW21
## AG_4 PHOX2A/B+ ISL1+ GATA3+; likely mix of chromaffin cells and sympathoblasts from all samples
## AG_5 PECAM1+ TEK+; mix of samples
## AG_6 PTPRC+ MRC+ CD163+ IKZF1+; mix of samples
## AG_7 COL3A1+ TRPC6+; possibly mix of fibroblasts & pericytes; mix of all samples
## AG_8 PHOX2A/B+ ISL1+ GATA3+; likely mix of chromaffin cells and sympathoblasts from all samples
## AG_9 CDH19 MPZ ERBB3 PLP1+
## BR_0 TBR1/EMX1+ CUX2+ excitatory neurons
## BR_1 VIM+ TOP2A+ (cycling) MOXD1+; EMX1/2+ indicating likely dorsal
## BR_10 DLX+ GAD1 GAD2 inhibitory neuron; ADARB2/CALB2 indicative of CGE origin
## BR_11 ACTA2/PRDM6, clusters with 8
## BR_12 CNTN4 KCNQ5 excitatory neurons;entirely T63_b4_Brain_PCW20
## BR_13 entirely T382_b4_Brain_PCW15; MECOM+
## BR_14 CUX2+ excitatory neurons
## BR_15 NOVA1 SOX6 LHFPL3 OPCML+ although lacking canonical PDGFRA OLIG2
## BR_16 VWF/MECOM/TEK
## BR_17 PTPRC LY86 MRC1 CD163L1+; subset with high estimated contamination
## BR_2 NEUROD2 BCL11B TBR1 POU6F2 excitatory neurons
## BR_3 GABRG3 (GABA receptor) SGCZ marker; GAD1 GAD2 inhibitory neurons; mostly T64_b4_Brain_PCW18; LHX6+ indicating medial ganglionic eminence (MGE) origin
## BR_4 NEUROD2 excitatory neurons; UNC5D+
## BR_5 mostly T64_b4_Brain_PCW18; RFX3 RORB excitatory neurons
## BR_6 mostly T47_b4_Brain_PCW17; high SATB2 NEUROD2 FEZF2 excitatory neurons
## BR_7 CNTN4 excitatory neurons
## BR_8 ACTA2/PRDM6, clusters with 11
## BR_9 DLX+ GAD1 GAD2 inhibitory neuron; LHX6/SOX6+ indicating MGE-derived
## EY_0 CRB1+
## EY_1 high COL1A2 COL1A1 fibroblast
## EY_10 TRPM3 TRPM1 PLD5 OPCML BEST1 markers RPE
## EY_11 ONECUT1 ONECUT2 horizontal
## EY_12 MYH11 smooth muscle cell
## EY_13 high PAX2 GFAP; DCLK1 ERBB4 marker
## EY_14 CLCN5 BMPR1B NCOA1
## EY_15 SLC4A4 SLC38A11 ATP6V1C2
## EY_16 NRXN1 NCAM2
## EY_17 PAX3 TYR
## EY_18 PTPRC CD163
## EY_19 PIP5K1B COL4A3 COL4A4
## EY_2 rod ANO2+ NR2E3+
## EY_20 GAD1 GAD2 GABA_amacrine/amacrine
## EY_21 PIP5K1B COL4A3 COL4A4
## EY_3 GAD1 GAD2 GABA_amacrine/amacrine
## EY_4 BICC1 LAMA4 PIEZO2
## EY_5 high TRPC6; TTN MYH3 skeletal muscle
## EY_6 RBPMS, GAP43, EPHA6
## EY_7 GRIK1 CA10 NRXN3 oncone/offcone bipolar
## EY_8 THRB, PRDM1 +
## EY_9 PECAM1
## HT_0 high ACTN2 ANKRD1 MYOCD no NPPA MYH6
## HT_1 high ACTN2 ANKRD1 MYOCD no NPPA MYH6; entirely T014_b11_Heart_PCW18
## HT_10 DCN COL1A2 COL1A1 marker
## HT_11 PECAM1 marker; high CDH5 NOTCH1
## HT_12 entirely cluster 0 of T379_b11_Heart_PCW15; MYH3 NEB MYBPC1; same cluster level as 6
## HT_13 NRXN1 XKR4 LRRTM4 marker; half is cluster 6 of T379_b11_Heart_PCW15; neuronal receptor
## HT_14 PTPRC marker CD163
## HT_15 EFEMP1 WT1 marker
## HT_2 NPPA MYH6 MYL7 marker; entirely T032_b11_Heart_PCW12
## HT_3 DCN COL1A2 COL1A1 marker
## HT_4 PECAM1 CDH5 marker; high NOTCH1
## HT_5 MYL2 ANKRD1 MYL7 TNNI3 ACTN2 CNN1 marker; mostly T104_b11_Heart_PCW17; high MYL7 MYH6 NPPA atrial
## HT_6 COL1A2 COL1A1 MYH3 marker; entirely T379_b11_Heart_PCW15
## HT_7 ANKRD1 ACTN2 MYL2 marker; mostly T104_b11_Heart_PCW17
## HT_8 cell cycle dependent
## HT_9 PDGFRB RGS5 NOTCH3 marker
## LI_0 cycling; ANK1 TFRC SLC4A1
## LI_1 high AFP ALB APOB
## LI_10 LDB2 STAB2 KDR endothelial
## LI_11 EBF1 PAX5 marker; B lineage cells https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3323878/
## LI_12 high PECAM1 and TRPC6; ITGA2B (CD41) DNM3 marker
## LI_13 PKHD1 ANXA4 cholangiocyte (secretes bile)
## LI_2 ANK1 SLC4A1 ALAS2 TFRC marker
## LI_3 high AFP ALB APOB
## LI_4 entirely T375_b15_Liver_PCW15; AFP marker
## LI_5 cell cycle dependent; high ANK1 TFRC
## LI_6 high AFP ALB APOB
## LI_7 LGMN Kupffer cells (liver resident macrophages)
## LI_8 TFRC
## LI_9 stellate cells (lipocytes that become myofibroblast-like when liver is injured); high COL3A1 DCN COL6A3
## LU_0 SFTPB SFTPC marker
## LU_1 MEOX2 marker; high WNT2
## LU_10 cycling; entirely T164_b12_Lung_PCW10; high ETV5 some SOX9
## LU_11 PROX1 RELN marker; lymphatics
## LU_12 PTPRC CD163
## LU_13 cycling; entirely T164_b12_Lung_PCW10; EYA4 HHIP MYH11 MYOCD markers
## LU_14 GRP NRG1 NRXN1 GRIK2 markers; high GHRL (although not markers)
## LU_15 DNAH11 DNAH5 dynein markers
## LU_16 C3
## LU_17 ITGA2B (CD41) DNM3 markers
## LU_2 PECAM1
## LU_3 DNAH11 DNAH5 dynein markers
## LU_4 HHIP EYA4 MYH11 DACH2 HPSE2 markers
## LU_5 SFTPB marker
## LU_6 PDGFRB TRPC6 LRRTM4 SLIT3
## LU_7 MEOX2 marker; high WNT2
## LU_8 SFTPB marker
## LU_9 cycling; high ETV5 some SFPTB
## MU_0 high TTN/MYLPF; high MYOG; low PAX3 (PMID: 32396864)
## MU_1 low UMI; MYLK high; mostly PCW17; G2/M phases
## MU_10 MYBPC1/MYH7B/TNNT1 (PMID: PMC9569562); MYOD1-low but MYOG/ACTC1 high (PMID: 37003036); TNNT1 contractile gene; MUSK+ (neuromuscular synaptic gene)
## MU_11 PECAM1;VWF;ESAM+; mostly PCW17
## MU_12 PAX3+
## MU_13 KRT10/SCEL
## MU_14 RGS5/KCNJ8
## MU_15 expression of macrophage markers inlcuding PTPRC/MRC/F13A1
## MU_16 MYH7+; MYOD1-low but MYOG/ACTC1 high (PMID: 37003036); TNNT1 contractile gene
## MU_17 PAX7+
## MU_18 clusters with 0/2/10/16
## MU_19 low UMI; ITGA1+ TRPC6+
## MU_2 high PAX7; high TTN/MYLPF; Myosin heavy chain embryonic (MYH3)+; high MYOG/PAX7 low PAX3 (PMID: 32396864)
## MU_20 KRT4/17+, SCEL
## MU_21 PAX3+
## MU_3 PAX3+
## MU_4 FBN1+
## MU_5 MECOM/VWF+; also expressing VEGF recepors (KDR/FLT4/NRP2)
## MU_6 PECAM1;VWF;ESAM+; mostly PCW17
## MU_7 PAX7+ and G2/M cell cycle states; somewhat higher markers TOP2A/CENPE+
## MU_8 MECOM/SFTPB+
## MU_9 TNMD/MKX+
## SK_0 RHOJ, CUBN
## SK_1 LRRTM4+, COL6A6+, ABCA10+
## SK_10 TYRP1+, TRPM1+, DCT+, all components of pigment producing genes
## SK_11 NEB, MYH3, DMD, ACTN2
## SK_12 EDA+, TSPEAR+, FRAS1+
## SK_13 PROX1+, STAB2+
## SK_14 EDNRB+ ITGA8+ temporary until additional marker validation
## SK_15 PTPRC+, MRC1+, LGMN
## SK_16 PDZRN4, BMP5, PRLR
## SK_17 KRT4, KRT13, KRT23+, MYO5B+ (either granular or basal keratinocyte)
## SK_18 DNMT1+, BMP7+
## SK_2 KRT10+, TP63+, MME+, DSC3+
## SK_3 RHOJ, CUBN
## SK_4 PECAM1, VWF, FLT1(VEGFR1)+
## SK_5 LRRTM4+, COL6A6+, ABCA10+
## SK_6 TRPC6+, ACTA2+, PDGFRB+
## SK_7 NRXN1+, CDH19+
## SK_8 NEB, MYH3, DMD, ACTN2
## SK_9 KIF26B+, SPON1+, DAAM2+, KCNN2+
## SP_0 ATXN1+
## SP_1 ATXN1+
## SP_10 SPTA1 and ANK1+ erythroblastoid markers
## SP_11 ACTA2+, TRPC6
## SP_2 CD34+ and PECAM positive
## SP_3 MS4A1, CD19, IGHM+
## SP_4 ATXN1+
## SP_5 CD163+
## SP_6 CD34+ and PECAM positive
## SP_7 ATXN1+
## SP_8 RIPOR3+ DNM3+
## SP_9 CD247+
## ST_0 MUC6+
## ST_1 ACTA2+ MYH11+
## ST_10 PECAM1+ VWF+
## ST_11 cell cycle dependent; MKI67+
## ST_12 TRPC6+ PDGFRB+ RGS5+
## ST_13 mostly esophagus sample; KRT5+ KRT15+ high SCPEP1
## ST_14 ANO1+ KIT+
## ST_15 mostly esophagus sample; high umi; DNAH12+ CFAP299+
## ST_16 GHRL+ high SST HDC
## ST_17 mostly esophagus sample; MYH11+ COL12A1+ ACTA2+ MYH11+
## ST_18 TTN+ MYH3+ TNNT3+
## ST_19 high PTPRC CD163
## ST_2 COL1A1+ ACTA2+ COL3A1+ COL1A2+ MYH11+
## ST_20 high umi; high STAB2 RELN PROX1
## ST_21 high umi; mesothelial WT1+
## ST_3 ATP4A+ ATP4B+
## ST_4 mostly esophagus sample; KRT13+ KRT4+ KRT7+ IRAG2+
## ST_5 high DCN COL1A2 FBLN1
## ST_6 high DCN COL1A2 FBLN1
## ST_7 mostly esophagus sample; ACTA2+ MYH11+
## ST_8 same cluster level as 19 immune
## ST_9 DSCAM+ CHAT+ NOS1+ NRG1+
## TM_0 PTPRC+; clusters with 1/3; low CD8+
## TM_1 TOX/TOX2 markers; PTPRC+; clusters with 0/3; low CD8+
## TM_10 ACTA2/RGS5/PDGFRB+
## TM_11 FLT3+; high expresison of Park et al DC genes
## TM_12 CD163L1+; high expression of Park et al macrophage genes
## TM_13 COL1A2/COl3A1/DCN+
## TM_14 MYH7+; clusters with 2
## TM_15 PAX5+; high expression of Park et al B cell genes
## TM_16 expression of BCHE (neurotransmitter enzyme)
## TM_17 FN1+/FLRT2+
## TM_2 PTPRC+; cycling (G2/M and S-assigned); clusters with 0/1; low CD8+
## TM_3 MYOG/MYPN/MYOT/MYH2+ (similar to TEC(myo) in Park et al); clusters with 14
## TM_4 MYH2+
## TM_5 COL1A2/COl3A1/DCN+
## TM_6 CDH1+; ADARB2+; SLIT-ROBO signaling; very minor AIRE+ pre-decontX; high expression of Park et al cTEC/mcTEC genes
## TM_7 PECAM1+/MECOM+
## TM_8 PAX7+; high expression of TEC(myo) signature
## TM_9 PAX7+; high expression of TEC(myo) signature
## TR_0 TPO, TG, TSHR+
## TR_1 TPO, TG, TSHR+
## TR_10 PECAM1+
## TR_11 CASR, SPOCK3, DNAH11 positive
## TR_2 TPO, TG, TSHR+
## TR_3 unclear markers but still has NKAIN3, P3H2 expression
## TR_4 COL1A1, COL1A2 positive but not contractile
## TR_5 MYO18B, MYOM1, SOX6
## TR_6 TPO, TG, TSHR+
## TR_7 TPO, TG, TSHR+
## TR_8 TPO, TG, TSHR+
## TR_9 ACTA2, TAGLN+
## L0_clusterID L3_clusterID
## AG_0 AG_0 AG_adrenal cortex 1
## AG_1 AG_1 AG_adrenal cortex 2
## AG_2 AG_2 AG_adrenal cortex 3
## AG_3 AG_3 AG_adrenal cortex 4
## AG_4 AG_4 AG_sympathoadrenal lineage 1
## AG_5 AG_5 AG_endothelial
## AG_6 AG_6 AG_macrophages
## AG_7 AG_7 AG_stromal
## AG_8 AG_8 AG_sympathoadrenal lineage 2
## AG_9 AG_9 AG_schwann cell precursors
## BR_0 BR_0 BR_excitatory neurons 1
## BR_1 BR_1 BR_dorsal radial glia
## BR_10 BR_10 BR_CGE-derived inhibitory neurons
## BR_11 BR_11 BR_smooth muscle cells 2
## BR_12 BR_12 BR_excitatory neurons 7
## BR_13 BR_13 BR_endothelial 1
## BR_14 BR_14 BR_excitatory neurons 8
## BR_15 BR_15 BR_oligodendrocyte precursor cells
## BR_16 BR_16 BR_endothelial 2
## BR_17 BR_17 BR_macrophages
## BR_2 BR_2 BR_excitatory neurons 2
## BR_3 BR_3 BR_MGE-derived inhibitory neurons 1
## BR_4 BR_4 BR_excitatory neurons 3
## BR_5 BR_5 BR_excitatory neurons 4
## BR_6 BR_6 BR_excitatory neurons 5
## BR_7 BR_7 BR_excitatory neurons 6
## BR_8 BR_8 BR_smooth muscle cells 1
## BR_9 BR_9 BR_MGE-derived inhibitory neurons 2
## EY_0 EY_0 EY_retinal progenitor cell
## EY_1 EY_1 EY_fibroblast
## EY_10 EY_10 EY_RPE
## EY_11 EY_11 EY_horizontal
## EY_12 EY_12 EY_smooth muscle
## EY_13 EY_13 EY_astrocyte
## EY_14 EY_14 EY_myogenic progenitor
## EY_15 EY_15 EY_ciliated pigmented epithelium
## EY_16 EY_16 EY_schwann
## EY_17 EY_17 EY_melanocyte
## EY_18 EY_18 EY_macrophage
## EY_19 EY_19 EY_cornea 1
## EY_2 EY_2 EY_rod
## EY_20 EY_20 EY_amacrine 2
## EY_21 EY_21 EY_cornea 2
## EY_3 EY_3 EY_amacrine 1
## EY_4 EY_4 EY_trabecular meshwork
## EY_5 EY_5 EY_extraocular muscle
## EY_6 EY_6 EY_retinal ganglion
## EY_7 EY_7 EY_bipolar
## EY_8 EY_8 EY_cone
## EY_9 EY_9 EY_vascular endothelium
## HT_0 HT_0 HT_vCM 1
## HT_1 HT_1 HT_vCM 2
## HT_10 HT_10 HT_fibroblast 3
## HT_11 HT_11 HT_endocardial 2
## HT_12 HT_12 HT_fibroblast 4
## HT_13 HT_13 HT_neuronal
## HT_14 HT_14 HT_macrophage
## HT_15 HT_15 HT_epicardial
## HT_2 HT_2 HT_aCM 1
## HT_3 HT_3 HT_fibroblast 1
## HT_4 HT_4 HT_endocardial 1
## HT_5 HT_5 HT_aCM 2
## HT_6 HT_6 HT_fibroblast 2
## HT_7 HT_7 HT_vCM 3
## HT_8 HT_8 HT_cycling vCM
## HT_9 HT_9 HT_pericyte
## LI_0 LI_0 LI_cycling erythroblast
## LI_1 LI_1 LI_hepatocyte 1
## LI_10 LI_10 LI_endothelial
## LI_11 LI_11 LI_B cell progenitor
## LI_12 LI_12 LI_megakaryocyte
## LI_13 LI_13 LI_cholangiocyte
## LI_2 LI_2 LI_erythroblast 1
## LI_3 LI_3 LI_hepatocyte 2
## LI_4 LI_4 LI_hepatocyte 3
## LI_5 LI_5 LI_early erythroblast
## LI_6 LI_6 LI_hepatocyte 4
## LI_7 LI_7 LI_Kupffer
## LI_8 LI_8 LI_erythroblast 2
## LI_9 LI_9 LI_stellate
## LU_0 LU_0 LU_airway epithelial progenitor 1
## LU_1 LU_1 LU_airway fibroblast 1
## LU_10 LU_10 LU_airway epithelial progenitor 5
## LU_11 LU_11 LU_lymphatics
## LU_12 LU_12 LU_macrophage
## LU_13 LU_13 LU_airway smooth muscle 2
## LU_14 LU_14 LU_pulmonary neuroendocrine
## LU_15 LU_15 LU_ciliated 2
## LU_16 LU_16 LU_mesothelial
## LU_17 LU_17 LU_megakaryocyte
## LU_2 LU_2 LU_vascular endothelial
## LU_3 LU_3 LU_ciliated 1
## LU_4 LU_4 LU_airway smooth muscle 1
## LU_5 LU_5 LU_airway epithelial progenitor 2
## LU_6 LU_6 LU_pericytes
## LU_7 LU_7 LU_airway fibroblast 2
## LU_8 LU_8 LU_airway epithelial progenitor 3
## LU_9 LU_9 LU_airway epithelial progenitor 4
## MU_0 MU_0 MU_myocytes (fast/Type II) 1
## MU_1 MU_1 MU_smooth muscle 1
## MU_10 MU_10 MU_myocytes (slow/Type I) 1
## MU_11 MU_11 MU_endothelial 3
## MU_12 MU_12 MU_myogenic progenitors 2
## MU_13 MU_13 MU_keratinocytes 1
## MU_14 MU_14 MU_pericytes 1
## MU_15 MU_15 MU_macrophages
## MU_16 MU_16 MU_myocytes (slow/Type I) 2
## MU_17 MU_17 MU_myoblasts
## MU_18 MU_18 MU_myocytes
## MU_19 MU_19 MU_pericytes 2
## MU_2 MU_2 MU_myocytes (fast/Type II) 2
## MU_20 MU_20 MU_keratinocytes 2
## MU_21 MU_21 MU_myogenic progenitors 3
## MU_3 MU_3 MU_myogenic progenitors 1
## MU_4 MU_4 MU_fibroblasts
## MU_5 MU_5 MU_endothelial 1
## MU_6 MU_6 MU_endothelial 2
## MU_7 MU_7 MU_myoblasts (cycling)
## MU_8 MU_8 MU_smooth muscle 2
## MU_9 MU_9 MU_tenogenic cells
## SK_0 SK_0 SK_fibroblast 1
## SK_1 SK_1 SK_fibroblast 2
## SK_10 SK_10 SK_melanocyte
## SK_11 SK_11 SK_skeletal muscle 2
## SK_12 SK_12 SK_interfollicular cells
## SK_13 SK_13 SK_lymphatic endothelium
## SK_14 SK_14 SK_melanocyte progenitor
## SK_15 SK_15 SK_macrophage
## SK_16 SK_16 SK_fibroblast 6
## SK_17 SK_17 SK_keratinocyte 2
## SK_18 SK_18 SK_epidermal progenitor
## SK_2 SK_2 SK_keratinocyte 1
## SK_3 SK_3 SK_fibroblast 3
## SK_4 SK_4 SK_vascular endothelium
## SK_5 SK_5 SK_fibroblast 4
## SK_6 SK_6 SK_pericyte
## SK_7 SK_7 SK_schwann
## SK_8 SK_8 SK_skeletal muscle 1
## SK_9 SK_9 SK_fibroblast 5
## SP_0 SP_0 SP_Fibroblast 1
## SP_1 SP_1 SP_Fibroblast 2
## SP_10 SP_10 SP_Erythroblast
## SP_11 SP_11 SP_Pericyte
## SP_2 SP_2 SP_Endothelial progenitor 1
## SP_3 SP_3 SP_B cells
## SP_4 SP_4 SP_Fibroblast 3
## SP_5 SP_5 SP_Macrophage
## SP_6 SP_6 SP_Endothelial progenitor 2
## SP_7 SP_7 SP_Fibroblast 4
## SP_8 SP_8 SP_Megakaryocyte
## SP_9 SP_9 SP_NK T cells
## ST_0 ST_0 ST_chief and neck cells
## ST_1 ST_1 ST_smooth muscle 1
## ST_10 ST_10 ST_endothelial
## ST_11 ST_11 ST_cycling stromal
## ST_12 ST_12 ST_pericytes
## ST_13 ST_13 ST_epithelial basal/keratinocyte
## ST_14 ST_14 ST_interstitial cells of Cajal
## ST_15 ST_15 ST_ciliated epithelium
## ST_16 ST_16 ST_endocrine cells
## ST_17 ST_17 ST_smooth muscle 4
## ST_18 ST_18 ST_MYH3+ smooth muscle
## ST_19 ST_19 ST_macrophage
## ST_2 ST_2 ST_smooth muscle 2
## ST_20 ST_20 ST_lymphatics
## ST_21 ST_21 ST_mesothelial
## ST_3 ST_3 ST_parietal cells
## ST_4 ST_4 ST_keratinocytes/tuft cells
## ST_5 ST_5 ST_fibroblasts 1
## ST_6 ST_6 ST_fibroblasts 2
## ST_7 ST_7 ST_smooth muscle 3
## ST_8 ST_8 ST_enteric glia
## ST_9 ST_9 ST_enteric neurons
## TM_0 TM_0 TM_CD8+ CD4+ thymocytes 1
## TM_1 TM_1 TM_CD8+ CD4+ thymocytes 2
## TM_10 TM_10 TM_vascular smooth muscle
## TM_11 TM_11 TM_dendritic cells
## TM_12 TM_12 TM_macrophages
## TM_13 TM_13 TM_fibroblasts 2
## TM_14 TM_14 TM_medullary TEC (myoid Type I)
## TM_15 TM_15 TM_B cells
## TM_16 TM_16 TM_medullary TEC (neuromuscular)
## TM_17 TM_17 TM_fibroblasts 3
## TM_2 TM_2 TM_CD8+ CD4+ thymocytes 3
## TM_3 TM_3 TM_medullary TEC (myoid Type IIa) 1
## TM_4 TM_4 TM_medullary TEC (myoid Type IIa) 2
## TM_5 TM_5 TM_fibroblasts 1
## TM_6 TM_6 TM_cortical TEC
## TM_7 TM_7 TM_endothelial cells
## TM_8 TM_8 TM_medullary TEC (myoid) 1
## TM_9 TM_9 TM_medullary TEC (myoid) 2
## TR_0 TR_0 TR_thyroid follicular 1
## TR_1 TR_1 TR_thyroid follicular 2
## TR_10 TR_10 TR_endothelial
## TR_11 TR_11 TR_parathyroid
## TR_2 TR_2 TR_thyroid follicular 3
## TR_3 TR_3 TR_stromal
## TR_4 TR_4 TR_fibroblast
## TR_5 TR_5 TR_skeletal muscle
## TR_6 TR_6 TR_thyroid follicular 4
## TR_7 TR_7 TR_thyroid follicular 5
## TR_8 TR_8 TR_thyroid follicular 6
## TR_9 TR_9 TR_smooth muscle
## Cluster_labelled Color
## AG_0 AG_0_adrenal cortex 1 #876941
## AG_1 AG_1_adrenal cortex 2 #876941
## AG_2 AG_2_adrenal cortex 3 #876941
## AG_3 AG_3_adrenal cortex 4 #876941
## AG_4 AG_4_sympathoadrenal lineage 1 #876941
## AG_5 AG_5_endothelial #876941
## AG_6 AG_6_macrophages #876941
## AG_7 AG_7_stromal #876941
## AG_8 AG_8_sympathoadrenal lineage 2 #876941
## AG_9 AG_9_schwann cell precursors #876941
## BR_0 BR_0_excitatory neurons 1 #0C727C
## BR_1 BR_1_dorsal radial glia #0C727C
## BR_10 BR_10_CGE-derived inhibitory neurons #0C727C
## BR_11 BR_11_smooth muscle cells 2 #0C727C
## BR_12 BR_12_excitatory neurons 7 #0C727C
## BR_13 BR_13_endothelial 1 #0C727C
## BR_14 BR_14_excitatory neurons 8 #0C727C
## BR_15 BR_15_oligodendrocyte precursor cells #0C727C
## BR_16 BR_16_endothelial 2 #0C727C
## BR_17 BR_17_macrophages #0C727C
## BR_2 BR_2_excitatory neurons 2 #0C727C
## BR_3 BR_3_MGE-derived inhibitory neurons 1 #0C727C
## BR_4 BR_4_excitatory neurons 3 #0C727C
## BR_5 BR_5_excitatory neurons 4 #0C727C
## BR_6 BR_6_excitatory neurons 5 #0C727C
## BR_7 BR_7_excitatory neurons 6 #0C727C
## BR_8 BR_8_smooth muscle cells 1 #0C727C
## BR_9 BR_9_MGE-derived inhibitory neurons 2 #0C727C
## EY_0 EY_0_retinal progenitor cell #ff9f0f
## EY_1 EY_1_fibroblast #ff9f0f
## EY_10 EY_10_RPE #ff9f0f
## EY_11 EY_11_horizontal #ff9f0f
## EY_12 EY_12_smooth muscle #ff9f0f
## EY_13 EY_13_astrocyte #ff9f0f
## EY_14 EY_14_myogenic progenitor #ff9f0f
## EY_15 EY_15_ciliated pigmented epithelium #ff9f0f
## EY_16 EY_16_schwann #ff9f0f
## EY_17 EY_17_melanocyte #ff9f0f
## EY_18 EY_18_macrophage #ff9f0f
## EY_19 EY_19_cornea 1 #ff9f0f
## EY_2 EY_2_rod #ff9f0f
## EY_20 EY_20_amacrine 2 #ff9f0f
## EY_21 EY_21_cornea 2 #ff9f0f
## EY_3 EY_3_amacrine 1 #ff9f0f
## EY_4 EY_4_trabecular meshwork #ff9f0f
## EY_5 EY_5_extraocular muscle #ff9f0f
## EY_6 EY_6_retinal ganglion #ff9f0f
## EY_7 EY_7_bipolar #ff9f0f
## EY_8 EY_8_cone #ff9f0f
## EY_9 EY_9_vascular endothelium #ff9f0f
## HT_0 HT_0_vCM 1 #D51F26
## HT_1 HT_1_vCM 2 #D51F26
## HT_10 HT_10_fibroblast 3 #D51F26
## HT_11 HT_11_endocardial 2 #D51F26
## HT_12 HT_12_fibroblast 4 #D51F26
## HT_13 HT_13_neuronal #D51F26
## HT_14 HT_14_macrophage #D51F26
## HT_15 HT_15_epicardial #D51F26
## HT_2 HT_2_aCM 1 #D51F26
## HT_3 HT_3_fibroblast 1 #D51F26
## HT_4 HT_4_endocardial 1 #D51F26
## HT_5 HT_5_aCM 2 #D51F26
## HT_6 HT_6_fibroblast 2 #D51F26
## HT_7 HT_7_vCM 3 #D51F26
## HT_8 HT_8_cycling vCM #D51F26
## HT_9 HT_9_pericyte #D51F26
## LI_0 LI_0_cycling erythroblast #3b46a3
## LI_1 LI_1_hepatocyte 1 #3b46a3
## LI_10 LI_10_endothelial #3b46a3
## LI_11 LI_11_B cell progenitor #3b46a3
## LI_12 LI_12_megakaryocyte #3b46a3
## LI_13 LI_13_cholangiocyte #3b46a3
## LI_2 LI_2_erythroblast 1 #3b46a3
## LI_3 LI_3_hepatocyte 2 #3b46a3
## LI_4 LI_4_hepatocyte 3 #3b46a3
## LI_5 LI_5_early erythroblast #3b46a3
## LI_6 LI_6_hepatocyte 4 #3b46a3
## LI_7 LI_7_Kupffer #3b46a3
## LI_8 LI_8_erythroblast 2 #3b46a3
## LI_9 LI_9_stellate #3b46a3
## LU_0 LU_0_airway epithelial progenitor 1 #f0643e
## LU_1 LU_1_airway fibroblast 1 #f0643e
## LU_10 LU_10_airway epithelial progenitor 5 #f0643e
## LU_11 LU_11_lymphatics #f0643e
## LU_12 LU_12_macrophage #f0643e
## LU_13 LU_13_airway smooth muscle 2 #f0643e
## LU_14 LU_14_pulmonary neuroendocrine #f0643e
## LU_15 LU_15_ciliated 2 #f0643e
## LU_16 LU_16_mesothelial #f0643e
## LU_17 LU_17_megakaryocyte #f0643e
## LU_2 LU_2_vascular endothelial #f0643e
## LU_3 LU_3_ciliated 1 #f0643e
## LU_4 LU_4_airway smooth muscle 1 #f0643e
## LU_5 LU_5_airway epithelial progenitor 2 #f0643e
## LU_6 LU_6_pericytes #f0643e
## LU_7 LU_7_airway fibroblast 2 #f0643e
## LU_8 LU_8_airway epithelial progenitor 3 #f0643e
## LU_9 LU_9_airway epithelial progenitor 4 #f0643e
## MU_0 MU_0_myocytes (fast/Type II) 1 #89C75F
## MU_1 MU_1_smooth muscle 1 #89C75F
## MU_10 MU_10_myocytes (slow/Type I) 1 #89C75F
## MU_11 MU_11_endothelial 3 #89C75F
## MU_12 MU_12_myogenic progenitors 2 #89C75F
## MU_13 MU_13_keratinocytes 1 #89C75F
## MU_14 MU_14_pericytes 1 #89C75F
## MU_15 MU_15_macrophages #89C75F
## MU_16 MU_16_myocytes (slow/Type I) 2 #89C75F
## MU_17 MU_17_myoblasts #89C75F
## MU_18 MU_18_myocytes #89C75F
## MU_19 MU_19_pericytes 2 #89C75F
## MU_2 MU_2_myocytes (fast/Type II) 2 #89C75F
## MU_20 MU_20_keratinocytes 2 #89C75F
## MU_21 MU_21_myogenic progenitors 3 #89C75F
## MU_3 MU_3_myogenic progenitors 1 #89C75F
## MU_4 MU_4_fibroblasts #89C75F
## MU_5 MU_5_endothelial 1 #89C75F
## MU_6 MU_6_endothelial 2 #89C75F
## MU_7 MU_7_myoblasts (cycling) #89C75F
## MU_8 MU_8_smooth muscle 2 #89C75F
## MU_9 MU_9_tenogenic cells #89C75F
## SK_0 SK_0_fibroblast 1 #ad0773
## SK_1 SK_1_fibroblast 2 #ad0773
## SK_10 SK_10_melanocyte #ad0773
## SK_11 SK_11_skeletal muscle 2 #ad0773
## SK_12 SK_12_interfollicular cells #ad0773
## SK_13 SK_13_lymphatic endothelium #ad0773
## SK_14 SK_14_melanocyte progenitor #ad0773
## SK_15 SK_15_macrophage #ad0773
## SK_16 SK_16_fibroblast 6 #ad0773
## SK_17 SK_17_keratinocyte 2 #ad0773
## SK_18 SK_18_epidermal progenitor #ad0773
## SK_2 SK_2_keratinocyte 1 #ad0773
## SK_3 SK_3_fibroblast 3 #ad0773
## SK_4 SK_4_vascular endothelium #ad0773
## SK_5 SK_5_fibroblast 4 #ad0773
## SK_6 SK_6_pericyte #ad0773
## SK_7 SK_7_schwann #ad0773
## SK_8 SK_8_skeletal muscle 1 #ad0773
## SK_9 SK_9_fibroblast 5 #ad0773
## SP_0 SP_0_Fibroblast 1 #3BBCA8
## SP_1 SP_1_Fibroblast 2 #3BBCA8
## SP_10 SP_10_Erythroblast #3BBCA8
## SP_11 SP_11_Pericyte #3BBCA8
## SP_2 SP_2_Endothelial progenitor 1 #3BBCA8
## SP_3 SP_3_B cells #3BBCA8
## SP_4 SP_4_Fibroblast 3 #3BBCA8
## SP_5 SP_5_Macrophage #3BBCA8
## SP_6 SP_6_Endothelial progenitor 2 #3BBCA8
## SP_7 SP_7_Fibroblast 4 #3BBCA8
## SP_8 SP_8_Megakaryocyte #3BBCA8
## SP_9 SP_9_NK T cells #3BBCA8
## ST_0 ST_0_chief and neck cells #208A42
## ST_1 ST_1_smooth muscle 1 #208A42
## ST_10 ST_10_endothelial #208A42
## ST_11 ST_11_cycling stromal #208A42
## ST_12 ST_12_pericytes #208A42
## ST_13 ST_13_epithelial basal/keratinocyte #208A42
## ST_14 ST_14_interstitial cells of Cajal #208A42
## ST_15 ST_15_ciliated epithelium #208A42
## ST_16 ST_16_endocrine cells #208A42
## ST_17 ST_17_smooth muscle 4 #208A42
## ST_18 ST_18_MYH3+ smooth muscle #208A42
## ST_19 ST_19_macrophage #208A42
## ST_2 ST_2_smooth muscle 2 #208A42
## ST_20 ST_20_lymphatics #208A42
## ST_21 ST_21_mesothelial #208A42
## ST_3 ST_3_parietal cells #208A42
## ST_4 ST_4_keratinocytes/tuft cells #208A42
## ST_5 ST_5_fibroblasts 1 #208A42
## ST_6 ST_6_fibroblasts 2 #208A42
## ST_7 ST_7_smooth muscle 3 #208A42
## ST_8 ST_8_enteric glia #208A42
## ST_9 ST_9_enteric neurons #208A42
## TM_0 TM_0_CD8+ CD4+ thymocytes 1 #6E4B9E
## TM_1 TM_1_CD8+ CD4+ thymocytes 2 #6E4B9E
## TM_10 TM_10_vascular smooth muscle #6E4B9E
## TM_11 TM_11_dendritic cells #6E4B9E
## TM_12 TM_12_macrophages #6E4B9E
## TM_13 TM_13_fibroblasts 2 #6E4B9E
## TM_14 TM_14_medullary TEC (myoid Type I) #6E4B9E
## TM_15 TM_15_B cells #6E4B9E
## TM_16 TM_16_medullary TEC (neuromuscular) #6E4B9E
## TM_17 TM_17_fibroblasts 3 #6E4B9E
## TM_2 TM_2_CD8+ CD4+ thymocytes 3 #6E4B9E
## TM_3 TM_3_medullary TEC (myoid Type IIa) 1 #6E4B9E
## TM_4 TM_4_medullary TEC (myoid Type IIa) 2 #6E4B9E
## TM_5 TM_5_fibroblasts 1 #6E4B9E
## TM_6 TM_6_cortical TEC #6E4B9E
## TM_7 TM_7_endothelial cells #6E4B9E
## TM_8 TM_8_medullary TEC (myoid) 1 #6E4B9E
## TM_9 TM_9_medullary TEC (myoid) 2 #6E4B9E
## TR_0 TR_0_thyroid follicular 1 #8A9FD1
## TR_1 TR_1_thyroid follicular 2 #8A9FD1
## TR_10 TR_10_endothelial #8A9FD1
## TR_11 TR_11_parathyroid #8A9FD1
## TR_2 TR_2_thyroid follicular 3 #8A9FD1
## TR_3 TR_3_stromal #8A9FD1
## TR_4 TR_4_fibroblast #8A9FD1
## TR_5 TR_5_skeletal muscle #8A9FD1
## TR_6 TR_6_thyroid follicular 4 #8A9FD1
## TR_7 TR_7_thyroid follicular 5 #8A9FD1
## TR_8 TR_8_thyroid follicular 6 #8A9FD1
## TR_9 TR_9_smooth muscle #8A9FD15 Load RNA dendrogram order
# use RNA dendrogram order
rna_dend_order <- read.csv(here::here("output/02-global_analysis/02/cluster_metadata_dend_order.tsv"), sep="\t")6 Load marker peaks
marker_peaks_all <- readRDS(here::here("output/01-preprocessing/03/markerPeaks_Clusters.rds"))7 Lenient marker peak cutoff
Cutoff FDR <= 0.1 and Log2FC >=0.5
marker_peaks <- getMarkers(marker_peaks_all, cutOff = "FDR <= 0.1 & Log2FC >= 0.5")
marker_peaks7.1 Clean up the marker peaks and remove duplicates
# unique(GRanges) only dedups based on genomic ranges not considering other columns
tmp <- marker_peaks %>% unlist %>% as.data.frame %>% rownames_to_column(var="organ") %>% GRanges %>% unique
tmp <- tmp %>% as.data.frame
marker_peaks_unique <- tmp[,c("seqnames", "start", "end")]
marker_peaks_unique7.2 Get pseudobulked Peak Matrix
pb_peak <- readRDS(here::here("output/01-preprocessing/03/allorgan_peakmatrix_pseudobulked.rds"))
# filter for marker peaks only
rowData(pb_peak)$rowid <- rownames(rowData(pb_peak))
marker_df <- merge(rowData(pb_peak), marker_peaks_unique)
pb_peak_marker <- pb_peak[marker_df$rowid,]
saveRDS(pb_peak_marker, file.path(out, "pseudobulked_marker_peak_matrix.rds"))
pb_peak_marker7.2.1 Run correlation on marker peak matrix
pb_peak_marker <- readRDS(file.path(out, "pseudobulked_marker_peak_matrix.rds"))
mat <- pb_peak_marker@assays@data$PeakMatrix
cluster_pb <- cor(mat)dend_res_pb <- build_dend(cluster_pb, n_boot = 100)
saveRDS(dend_res_pb, file.path(out, "dend_res_markerpeak_pseudobulked.rds"))dend_res_pb <- readRDS(file.path(out, "dend_res_markerpeak_pseudobulked.rds"))
dend_labeled_pb <- dend_res_pb$dend7.2.2 Plot correlation based on pseudobulk
clust_order <- rev(order.dendrogram(dend_labeled_pb))
dff1 <- cluster_pb[clust_order, clust_order]
ht1 <- Heatmap(dff1, name="Pseudobulked marker peaks \ncount correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots[colnames(cluster_pb)[clust_order], "Cluster_labelled"],
row_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_pb)[clust_order], "Color"]),
column_labels = all.annots[colnames(cluster_pb)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_pb)[clust_order], "Color"]),
col = cmap_chromvar
)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(cluster_pb),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- rev(all.annots.rnadend$origOrder)
dff2 <- cluster_pb[clust_order, clust_order]
ht2 <- Heatmap(dff2, name="Pseudobulked marker peaks \ncount correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots.rnadend[colnames(cluster_pb)[clust_order], "Cluster_labelled"],
row_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_pb)[clust_order], "Color"]),
column_labels = all.annots.rnadend[colnames(cluster_pb)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_pb)[clust_order], "Color"]),
col = cmap_chromvar
)
# draw
draw(ht1)
draw(ht2)
7.3 ChromVAR on pseudobulked Peak Matrix
chromvar_pb <- pb_peak_marker
# convert into a RangedSummarizedExperiment
allseqinfo <- BSgenome.Hsapiens.UCSC.hg38@seqinfo
seqlevels(allseqinfo) <- c(paste0("chr",1:22),"chrX")
marker_peaks <- GRanges(seqnames = rowData(chromvar_pb)$seqnames,IRanges(rowData(chromvar_pb)$start, rowData(chromvar_pb)$end), seqinfo = allseqinfo)
rowRanges(chromvar_pb) <- marker_peaks
assayNames(chromvar_pb) <- "counts"
# add gc bias, filter, motif matching
chromvar_pb <- addGCBias(chromvar_pb, genome = BSgenome.Hsapiens.UCSC.hg38)
#chromvar_pb <- filterPeaks(chromvar_pb)
#chromvar_pb.filt <- filterPeaks(chromvar_pb, min_fragments_per_peak = 1000)
saveRDS(chromvar_pb, file.path(out, "marker_peaks_pseudobulk_bias.rds"))
motifs <- readRDS(here::here("data/external/Kartha2022_cisbp/cisBP_human_pfms_2021.rds")) # this is from Kartha et al 2022 Cell Genomics
motif_ix <- matchMotifs(motifs, chromvar_pb,
genome = BSgenome.Hsapiens.UCSC.hg38)
saveRDS(motif_ix, file.path(out,"marker_peaks_pseudobulk_matched_motif_cisbp2021JB.rds"))
# computing deviations
all.dev <- computeDeviations(object = chromvar_pb,
annotations = motif_ix)
saveRDS(all.dev, file.path(out, "marker_peaks_pseudobulk_chromvar.rds"))7.3.1 Run correlation on chromvar deviation z score matrix
all.dev <- readRDS(file.path(out, "marker_peaks_pseudobulk_chromvar.rds"))
mat <- all.dev@assays@data$z
cluster_chromvar <- cor(mat)
cluster_chromvar_feat <- cor(t(mat))# cluster columns (cell clusters)
dend_res_chromvar <- build_dend(cluster_chromvar, n_boot = 100)
saveRDS(dend_res_chromvar, file.path(out, "dend_res_markerpeak_chromvar.rds"))
# cluster rows (features)
dend_res_chromvar_feat <- build_dend(cluster_chromvar_feat, n_boot = 100)
saveRDS(dend_res_chromvar_feat, file.path(out, "dend_res_markerpeak_chromvar_feat.rds"))dend_res_chromvar <- readRDS(file.path(out, "dend_res_markerpeak_chromvar.rds"))
dend_labeled_chromvar <- dend_res_chromvar$dend
dend_res_chromvar_feat <- readRDS(file.path(out, "dend_res_markerpeak_chromvar_feat.rds"))
dend_labeled_chromvar_feat <- dend_res_chromvar_feat$dend7.3.2 Plot TF vs cluster heatmap
clust_order <- rev(order.dendrogram(dend_labeled_chromvar))
feat_order <- rev(order.dendrogram(dend_labeled_chromvar_feat))
df <- mat[feat_order, clust_order]
# Calculate breaks based on quantiles to handle outliers
break_points <- quantile(df, probs = seq(0.01, 0.99, length.out = length(cmap_chromvar)))
color_mapping <- colorRamp2(break_points, cmap_chromvar)
ht1 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 4),
column_labels = all.annots[colnames(df), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(df), "Color"]),
col = color_mapping
)
draw(ht1)
# instead of the dendrograms we built, try ComplexHeatmap built in hierarchical clustering
ht2 <- Heatmap(mat, name="ChromVAR deviations z-score",
cluster_columns = T,
cluster_rows = T,
row_names_gp = gpar(fontsize = 4),
column_labels = all.annots[colnames(mat), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(mat), "Color"]),
col = color_mapping
)
draw(ht2)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(mat),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- rev(all.annots.rnadend$origOrder)
feat_order <- rev(order.dendrogram(dend_labeled_chromvar_feat))
df <- mat[feat_order, clust_order]
ht3 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 4),
column_labels = all.annots.rnadend[colnames(df), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(df), "Color"]),
col = color_mapping
)
draw(ht3)
7.3.3 Plot correlation based on chromvar
clust_order <- rev(order.dendrogram(dend_labeled_chromvar))
dff1 <- cluster_chromvar[clust_order, clust_order]
ht1 <- Heatmap(dff1, name="ChromVAR deviations \nz score correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
row_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_chromvar)[clust_order], "Color"]),
column_labels = all.annots[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_chromvar)[clust_order], "Color"]),
col = cmap_chromvar
)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(cluster_chromvar),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- rev(all.annots.rnadend$origOrder)
dff2 <- cluster_chromvar[clust_order, clust_order]
ht2 <- Heatmap(dff2, name="ChromVAR deviations \nz score correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
#row_title = paste0("ChromVAR Deviations (",length(tf.set), " TFs)"),
#row_title_side = "left",
row_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Color"]),
column_labels = all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Color"]),
col = cmap_chromvar
)
# draw
draw(ht1)
draw(ht2)
### Run correlation, filter for top half variable tf only
#all.dev <- readRDS(file.path(out, "marker_peaks_pseudobulk_chromvar.rds"))
mat <- all.dev@assays@data$z
# identify tf subset with most variability
maxz <- rowMaxs(mat)
cutoff <- maxz %>% quantile(0.5)
mat.filtered <- mat[which(maxz>cutoff),]
tf.set <- rownames(mat.filtered)
cluster_chromvar <- cor(mat.filtered)
cluster_chromvar_feat <- cor(t(mat.filtered))# cluster columns (cell clusters)
dend_res_chromvar <- build_dend(cluster_chromvar, n_boot = 100)
saveRDS(dend_res_chromvar, file.path(out, "dend_res_markerpeak_chromvar_tophalfchromvarz.rds"))
# cluster rows (features)
dend_res_chromvar_feat <- build_dend(cluster_chromvar_feat, n_boot = 100)
saveRDS(dend_res_chromvar_feat, file.path(out, "dend_res_markerpeak_chromvar_feat_tophalfchromvarz.rds"))dend_res_chromvar <- readRDS(file.path(out, "dend_res_markerpeak_chromvar_tophalfchromvarz.rds"))
dend_labeled_chromvar <- dend_res_chromvar$dend
dend_res_chromvar_feat <- readRDS(file.path(out, "dend_res_markerpeak_chromvar_feat_tophalfchromvarz.rds"))
dend_labeled_chromvar_feat <- dend_res_chromvar_feat$dend7.3.4 Plot correlation, filter for top half variable tf only
clust_order <- rev(order.dendrogram(dend_labeled_chromvar))
dff1 <- cluster_chromvar[clust_order, clust_order]
ht1 <- Heatmap(dff1, name="ChromVAR deviations \nz score correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
row_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_chromvar)[clust_order], "Color"]),
row_title = paste0("ChromVAR Deviations (",length(tf.set), " TFs)"),
row_title_side = "left",
column_labels = all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_chromvar)[clust_order], "Color"]),
col = cmap_chromvar
)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(cluster_chromvar),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- rev(all.annots.rnadend$origOrder)
dff2 <- cluster_chromvar[clust_order, clust_order]
ht2 <- Heatmap(dff2, name="ChromVAR deviations \nz score correlations",
cluster_columns = F,
cluster_rows = F,
row_labels = all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
row_title = paste0("ChromVAR Deviations (",length(tf.set), " TFs)"),
row_title_side = "left",
row_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Color"]),
column_labels = all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(cluster_chromvar)[clust_order], "Color"]),
col = cmap_chromvar
)
# draw
draw(ht1)
draw(ht2)
7.3.5 Plot TF vs cluster heatmap, top half variable tf only
clust_order <- rev(order.dendrogram(dend_labeled_chromvar))
feat_order <- rev(order.dendrogram(dend_labeled_chromvar_feat))
df <- mat.filtered[feat_order, clust_order]
# Calculate breaks based on quantiles to handle outliers
break_points <- quantile(df, probs = seq(0.01, 0.99, length.out = length(cmap_chromvar)))
color_mapping <- colorRamp2(break_points, cmap_chromvar)
ht1 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 6),
column_labels = all.annots[colnames(cluster_chromvar)[clust_order], "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(cluster_chromvar)[clust_order], "Color"]),
col = color_mapping
)
draw(ht1)
# instead of the dendrograms we built, try ComplexHeatmap built in hierarchical clustering
ht2 <- Heatmap(mat.filtered, name="ChromVAR deviations z-score",
cluster_columns = T,
cluster_rows = T,
row_names_gp = gpar(fontsize = 6),
column_labels = all.annots[colnames(mat), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots[colnames(mat), "Color"]),
col = color_mapping
)
draw(ht2)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(mat.filtered),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- rev(all.annots.rnadend$origOrder)
feat_order <- rev(order.dendrogram(dend_labeled_chromvar_feat))
df <- mat.filtered[feat_order, clust_order]
ht3 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 7),
column_labels = all.annots.rnadend[colnames(df), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(df), "Color"]),
col = color_mapping
)
draw(ht3)
7.3.6 Plot TF vs cluster heatmap, manually selected TFs only
source(here::here("output/01-preprocessing/02/shared/markergenes/markergenes_dendro.R"))
mat <- all.dev@assays@data$z
mat.filtered <- mat[TFSets[TFSets %in% rownames(mat)],]
tf.set <- rownames(mat.filtered)
all.annots.rnadend <- merge(all.annots, rna_dend_order[,c("Cluster", "Order")], by.x="L0_clusterID", by.y="Cluster", all.x=T)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
all.annots.rnadend <- all.annots.rnadend[colnames(mat.filtered),] %>% mutate(origOrder=1:dim(all.annots.rnadend)[1]) %>% arrange(by=Order)
rownames(all.annots.rnadend) <- all.annots.rnadend$L0_clusterID
# plot by RNA dendrogram order
clust_order <- all.annots.rnadend$origOrder
df <- mat.filtered[, clust_order]
# Calculate breaks based on quantiles to handle outliers
# break_points <- quantile(df, probs = seq(0.1, 0.9, length.out = length(cmap_chromvar)))
break_points <- quantile(df, probs=c(0.05, 0.06, 0.1, 0.15, 0.5, 0.85, 0.9, 0.94, 0.95))
color_mapping <- colorRamp2(break_points, cmap_chromvar)
ht3 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 7),
column_labels = all.annots.rnadend[colnames(df), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(df), "Color"]),
col = color_mapping
)
draw(ht3)
# Calculate breaks based on quantiles to handle outliers
cmap_whitered <- c("white", "red")
break_points <- quantile(df, probs = seq(0.1, 0.9, length.out = length(cmap_whitered)))
color_mapping <- colorRamp2(break_points, colors = cmap_whitered)
ht4 <- Heatmap(df, name="ChromVAR deviations z-score",
cluster_columns = F,
cluster_rows = F,
row_names_gp = gpar(fontsize = 7),
column_labels = all.annots.rnadend[colnames(df), "Cluster_labelled"],
column_names_gp = gpar(fontsize = 8, col=all.annots.rnadend[colnames(df), "Color"]),
col = color_mapping
)
draw(ht4)
8 Session info
.libPaths()## [1] "/oak/stanford/groups/wjg/bliu/software/R_lib"
## [2] "/share/software/user/open/R/4.1.2/lib64/R/library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /share/software/user/open/openblas/0.3.10/lib/libopenblas_haswellp-r0.3.10.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 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] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggrastr_1.0.1 GenomicFeatures_1.46.5
## [3] AnnotationDbi_1.56.2 circlize_0.4.15
## [5] motifmatchr_1.16.0 chromVAR_1.16.0
## [7] ComplexHeatmap_2.10.0 dendextend_1.17.1
## [9] BSgenome.Hsapiens.UCSC.hg38_1.4.4 BSgenome_1.62.0
## [11] rtracklayer_1.54.0 Biostrings_2.62.0
## [13] XVector_0.34.0 SeuratObject_4.1.3
## [15] Seurat_4.3.0 cowplot_1.1.1
## [17] tibble_3.2.1 readr_2.1.4
## [19] glue_1.6.2 purrr_1.0.2
## [21] tidyr_1.3.1 dplyr_1.1.4
## [23] rhdf5_2.38.1 SummarizedExperiment_1.24.0
## [25] Biobase_2.54.0 MatrixGenerics_1.6.0
## [27] Rcpp_1.0.10 Matrix_1.5-4
## [29] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [31] IRanges_2.28.0 S4Vectors_0.32.4
## [33] BiocGenerics_0.40.0 matrixStats_0.63.0
## [35] data.table_1.14.8 stringr_1.5.0
## [37] plyr_1.8.8 magrittr_2.0.3
## [39] ggplot2_3.5.0 gtable_0.3.3
## [41] gtools_3.9.4 gridExtra_2.3
## [43] ArchR_1.0.2 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 R.utils_2.12.2
## [3] spatstat.explore_3.1-0 reticulate_1.28
## [5] tidyselect_1.2.0 poweRlaw_0.70.6
## [7] RSQLite_2.3.1 htmlwidgets_1.6.2
## [9] BiocParallel_1.28.3 Rtsne_0.16
## [11] munsell_0.5.0 codetools_0.2-18
## [13] ica_1.0-3 DT_0.27
## [15] future_1.33.2 miniUI_0.1.1.1
## [17] withr_3.0.0 spatstat.random_3.1-4
## [19] colorspace_2.1-0 progressr_0.13.0
## [21] filelock_1.0.2 highr_0.10
## [23] knitr_1.42 ROCR_1.0-11
## [25] tensor_1.5 listenv_0.9.0
## [27] GenomeInfoDbData_1.2.7 polyclip_1.10-4
## [29] bit64_4.0.5 rprojroot_2.0.3
## [31] parallelly_1.35.0 vctrs_0.6.5
## [33] generics_0.1.3 xfun_0.39
## [35] BiocFileCache_2.2.1 R6_2.5.1
## [37] doParallel_1.0.17 ggbeeswarm_0.7.2
## [39] clue_0.3-64 bitops_1.0-7
## [41] rhdf5filters_1.6.0 spatstat.utils_3.0-2
## [43] cachem_1.0.8 DelayedArray_0.20.0
## [45] vroom_1.6.3 promises_1.2.0.1
## [47] BiocIO_1.4.0 scales_1.3.0
## [49] beeswarm_0.4.0 globals_0.16.2
## [51] goftest_1.2-3 seqLogo_1.60.0
## [53] rlang_1.1.3 GlobalOptions_0.1.2
## [55] splines_4.1.2 lazyeval_0.2.2
## [57] spatstat.geom_3.1-0 yaml_2.3.7
## [59] reshape2_1.4.4 abind_1.4-5
## [61] httpuv_1.6.10 tools_4.1.2
## [63] ellipsis_0.3.2 jquerylib_0.1.4
## [65] RColorBrewer_1.1-3 ggridges_0.5.4
## [67] progress_1.2.2 zlibbioc_1.40.0
## [69] RCurl_1.98-1.12 prettyunits_1.1.1
## [71] deldir_1.0-6 pbapply_1.7-0
## [73] GetoptLong_1.0.5 viridis_0.6.3
## [75] zoo_1.8-12 ggrepel_0.9.5
## [77] cluster_2.1.2 magick_2.7.3
## [79] scattermore_1.0 lmtest_0.9-40
## [81] RANN_2.6.1 fitdistrplus_1.1-11
## [83] hms_1.1.3 patchwork_1.2.0
## [85] mime_0.12 evaluate_0.21
## [87] xtable_1.8-4 XML_3.99-0.14
## [89] shape_1.4.6 biomaRt_2.50.3
## [91] compiler_4.1.2 KernSmooth_2.23-20
## [93] crayon_1.5.2 R.oo_1.25.0
## [95] htmltools_0.5.5 later_1.3.1
## [97] tzdb_0.4.0 TFBSTools_1.32.0
## [99] DBI_1.1.3 dbplyr_2.3.2
## [101] rappdirs_0.3.3 MASS_7.3-54
## [103] cli_3.6.2 R.methodsS3_1.8.2
## [105] parallel_4.1.2 igraph_1.4.2
## [107] pkgconfig_2.0.3 TFMPvalue_0.0.9
## [109] GenomicAlignments_1.30.0 sp_1.6-0
## [111] plotly_4.10.1 spatstat.sparse_3.0-1
## [113] xml2_1.3.4 foreach_1.5.2
## [115] annotate_1.72.0 vipor_0.4.5
## [117] DirichletMultinomial_1.36.0 bslib_0.4.2
## [119] digest_0.6.31 pracma_2.4.2
## [121] sctransform_0.3.5 RcppAnnoy_0.0.20
## [123] CNEr_1.30.0 spatstat.data_3.0-1
## [125] rmarkdown_2.21 leiden_0.4.2
## [127] uwot_0.1.14 curl_5.0.0
## [129] restfulr_0.0.15 shiny_1.7.4
## [131] Rsamtools_2.10.0 rjson_0.2.21
## [133] lifecycle_1.0.3 nlme_3.1-153
## [135] jsonlite_1.8.4 Rhdf5lib_1.16.0
## [137] viridisLite_0.4.2 fansi_1.0.4
## [139] pillar_1.9.0 lattice_0.20-45
## [141] GO.db_3.14.0 KEGGREST_1.34.0
## [143] fastmap_1.1.1 httr_1.4.6
## [145] survival_3.2-13 png_0.1-8
## [147] iterators_1.0.14 bit_4.0.5
## [149] stringi_1.7.12 sass_0.4.6
## [151] blob_1.2.4 memoise_2.0.1
## [153] caTools_1.18.2 irlba_2.3.5.1
## [155] future.apply_1.10.0