EpiTrace

Computing cell age with bulk and single-cell ATAC-seq data

For example code, see vignettes/Hematopoiesis_2016_demo.notebook.Rmd or vignettes/Hematopoiesis_2016_demo.notebook.nb.html

Maintainer: Zhang Yi synapse@pku.edu.cn

Updates

20221021

Updated usage (see below).

EpiTraceAge_Convergence function
Using the non-human reference putative clock-like loci
Determining non-human reference putative clock-like loci in vertebrates
Determining non-human reference putative clock-like loci in remote species (for example, invertebrates)

20221008

Added frequently used non-human clockDML-like genomic regions as reference set for EpiTrace inference in these species.

Drosophila (interesting one)
Zebrafish
Mouse

20220910

Added mouse ClockDML sites (from Zhou et al 2022, PMID 35873672) for mouse single cell EpiTrace inference.

20220611

Added .rds files similar to the clock_gr_list data (for external loading).
Added %ni% function (taken from ArchR).

20220510

Iteration code has been updated. Please use EpiTraceAge_Convergence().
Example demos for EpiTraceAge_Convergence() would be avaiable soon.

Citation

Xinghuan Wang, Wan Jin, Gang Wang, Lingao Ju, Fangjin Chen, Kaiyu Qian, Yu Xiao and Yi Zhang, Tracking single cell evolution via clock-like chromatin accessibility, (2022) bioRxiv 2022.05.12.491736; doi: https://doi.org/10.1101/2022.05.12.491736

Installation

library(devtools)   
devtools::install_github('MagpiePKU/EpiTrace')

Basic Usage (Example)

library(EpiTrace)  
data("clock_gr_list")  
data("hematopoiesis.2018")   
initiated_peaks <- Init_Peakset(inputpeak)  
inputmatrix <- Init_Matrix(cell_metadata$celltype,initiated_peaks,inputmatrix)
epitrace_obj <- EpiTrace_prepare_object(initiated_peaks,inputmatrix,cell_metadata$celltype,ref_genome = 'hg19',non_standard_clock = F,clock_gr_list = clock_gr_list)  
epitrace_obj_age_estimated <- RunEpiTraceAge(epitrace_obj)  
phylotree_res <- RunEpiTracePhylogeny(epitrace_obj_age_estimated)

To plot a phylogenetic tree

phylotree_res_myeloid <- RunEpiTracePhylogeny(subset(epitrace_obj_age_estimated,celltype %in% c('HSC','MPP','CMP','GMP','Monocyte')))   
mitosis_tree <- phylotree_res_myeloid[['MitosisClock']][[2]]  
mitosis_tree <- ape::root(mitosis_tree,outgroup='HSC')  
plot(mitosis_tree)

To compute age-peak association

associated_res_HSC <- AssociationOfPeaksToAge(subset(epitrace_obj_age_estimated,celltype %in% 'HSC'))

Iterative update of age estimation

library(EpiTrace)  
data("clock_gr_list")  
data("hematopoiesis.2018")   
initiated_peaks <- Init_Peakset(inputpeak)  
inputmatrix <- Init_Matrix(cell_metadata$celltype,initiated_peaks,inputmatrix)
epitrace_obj <- EpiTrace_prepare_object(initiated_peaks,inputmatrix,cell_metadata$celltype,ref_genome = 'hg19',non_standard_clock = F,clock_gr_list = clock_gr_list)  
epitrace_obj_age_conv_estimated <- EpiTraceAge_Convergence(peakSet = initiated_peaks,matrix=inputmatrix,ref_genome='hg19',iterative_time = 10,min.cutoff = 0,non_standard_clock = T,qualnum = 10,ncore_lim = 48,mean_error_limit = 0.01)

Using non-human reference dataset

Example: GSE178526

library(EpiTrace)  
mm285_design_clock347_gr <- readRDS(system.file("data", "mouse_clock_mm285_design_clock347_mm10.rds", package = "EpiTrace")) 
dat_mat_mtx <- readRDS(file='Save/Mouse.Cyclic.Macrophage.GSE178526.mtx.rds')
row_gr <- readRDS(file='Save/Mouse.Cyclic.Macrophage.GSE178526.row_gr.rds')
meta_df <- readRDS(file='Save/Mouse.Cyclic.Macrophage.GSE178526.meta_df.rds')
row_df <- as.data.frame(row_gr)
EpiTrace::Init_Peakset(row_gr) -> init_gr
init_gr$peakName <- paste0(row_df$seqnames,'-',row_df$start,'-',row_df$end)
meta_df$cell <- rownames(meta_df)
EpiTrace::Init_Matrix(peakname = init_gr$peakName,cellname = meta_df$cell,matrix = dat_mat_mtx) -> init_mm
EpiTrace::EpiTraceAge_Convergence(peakSet = init_gr,matrix=init_mm,ref_genome='mm10',clock_gr=mm285_design_clock347_gr,iterative_time = 10,min.cutoff = 0,non_standard_clock = T,qualnum = 3,ncore_lim = 40,mean_error_limit = 0.075,Z_cutoff = 2.5) -> epitrace_obj_age_conv_estimated

Creating non-human reference dataset on non-human vertebrates

Example: Human to mouse / zebrafish

easyLift::easyLiftOver(plyranges::reduce_ranges(c(clock_gr_list[[1]],clock_gr_list[[2]])),map = '/gpfs/genomedb/chains/hg19ToMm10.over.chain') -> lifted_human_to_mouse_clock_gr
easyLift::easyLiftOver(plyranges::reduce_ranges(c(clock_gr_list[[1]],clock_gr_list[[2]])),map = '/gpfs/genomedb/chains/hg19ToDanRer10.over.chain')  -> lifted_human_to_danRer10_clock_gr

Creating non-human reference dataset on non-human remote species (e.g. invertebrates)

Example: Human to Drosophila

library(dplyr)
library(tidyr)
library(readr)
library(GenomicRanges)
library(reshape2)
library(openxlsx)
library(ggplot2)
library(Matrix)
library(EpiTrace)
library(Seurat)
library(SeuratObject)
library(ggtree)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg19)
library(BSgenome.Dmelanogaster.UCSC.dm6)
library(patchwork)
library(ArchR)
library(parallel)
library(ChIPseeker)
library(TxDb.Dmelanogaster.UCSC.dm6.ensGene)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(org.Dm.eg.db)

data("clock_gr_list")
setwd('//gpfs/output/ECS_Research/data/scATAC/Fly_scATAC_embryonic_development_Science2022/')
datadir = '///gpfs/output/ECS_Research/data/scATAC/Fly_scATAC_embryonic_development_Science2022/'
fly_dat <- readRDS('fly.all.downsampled_seurat_filtered_processed.rds')

# map human clock in the promoter/TSS region to respective fly homolog
human_clock_hg19 <- plyranges::reduce_ranges(c(clock_gr_list[[1]],clock_gr_list[[2]]))
human_clock_hg19_anno <- ChIPseeker::annotatePeak(human_clock_hg19,TxDb=TxDb.Hsapiens.UCSC.hg19.knownGene,assignGenomicAnnotation = T,annoDb = 'org.Hs.eg.db',tssRegion = c(3000,1000),level='gene')
# annotate fly ranges
fly_dat_ranges <- data.frame(peaks=rownames(fly_dat)) %>% separate(col=1,into=c('chr','start','end'),remove=F,convert=T) %>% makeGRangesFromDataFrame(keep.extra.columns = T)
fly_dat_ranges_anno <- ChIPseeker::annotatePeak(fly_dat_ranges,TxDb=TxDb.Dmelanogaster.UCSC.dm6.ensGene,assignGenomicAnnotation = T,annoDb = 'org.Dm.eg.db',tssRegion = c(3000,1000),level='gene')
# read fly ortholog table
dmel_mapped_to_human <- readr::read_tsv('dmel_human_orthologs_disease_fb_2022_05.tsv.gz',comment = "## ")
colnames(dmel_mapped_to_human) <- gsub('#','',colnames(dmel_mapped_to_human))
# extract human annotation
human_clock_hg19_anno_df <- human_clock_hg19_anno@anno %>% as.data.frame
human_clock_hg19_anno_tss_df <- human_clock_hg19_anno_df[abs(human_clock_hg19_anno_df$distanceToTSS)< 100,]
human_clock_hg19_anno_tss_df$SYMBOL -> human_clock_hg19_anno_tss_df$Human_gene_symbol
# find mappable (have ortholog) human annotation
human_clock_hg19_anno_tss_df <- left_join(human_clock_hg19_anno_tss_df,dmel_mapped_to_human)
human_clock_hg19_anno_tss_mappable_df <- human_clock_hg19_anno_tss_df[!is.na(human_clock_hg19_anno_tss_df$Dmel_gene_ID),]
# extract fly annotation
fly_dat_ranges_anno_df <- fly_dat_ranges_anno@anno %>% as.data.frame() 
# define putative clockAcc regions for fly
fly_dat_ranges_anno_df$clock_hit <- ifelse(abs(fly_dat_ranges_anno_df$distanceToTSS)<100 & fly_dat_ranges_anno_df$geneId %in% human_clock_hg19_anno_tss_mappable_df$Dmel_gene_ID, 'clock','non_clock')
fly_dat_ranges_putative_clock <- fly_dat_ranges[fly_dat_ranges_anno_df$clock_hit %in% 'clock',]

MagpiePKU / EpiTrace