Hematopoietic Stem Cells (HSC)

To prove that SCRIP can infer TR activities in a complex system and could be potentially used to track cell differentiation, we applied SCRIP on a human hematopoietic stem cell (HSC) differentiation scATAC-seq dataset (Jason et al., Cell, 2018). Data was downloaded from GEO with accession GSE96769.

SCRIP enrich -i example/HSC/data/HSC_peak_count.h5 -s hs -p example/HSC/SCRIP -t 32

library(Seurat)
library(destiny)
library(dplyr)
library(ggplot2)

enri<-read.table("example/HSC/SCRIP/enrichment/SCRIP_enrichment.txt",header=T)
meta<-read.table("example/HSC/data/metadata.txt")
meta[which(meta$assign.celltype=="MCP"),7]<-"pDC"
enri<-enri[rownames(enri)%in%rownames(meta),]
enri_b<-t(enri)

seurat <- CreateSeuratObject(counts = enri_b, project = "hsc")
seurat@assays$RNA@scale.data<-as.matrix(seurat@assays$RNA@counts)
seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 2000)
seurat <- RunPCA(seurat, features = VariableFeatures(object = seurat))

seurat <- FindNeighbors(seurat, dims = 1:25)
seurat <- FindClusters(seurat, resolution = 0.7)
seurat <- RunUMAP(seurat, dims = 1:50)

logcounts <- GetAssayData(seurat, "data")

# Choose the top 600 most variable TFs to do the trajectory analysis.
feature<-sort(apply(logcounts,1,var),decreasing=TRUE)[1:600]
feature<-rownames(as.data.frame(feature))
input_matrix <- t(as.matrix(logcounts[feature, ]))

dm <- DiffusionMap(as.matrix(input_matrix),k=4)
cell_type<-as.data.frame(seurat@active.ident)
colnames(cell_type)<-"cell_type"
seurat@meta.data$cell_type<-cell_type$cell_type

options(repr.plot.width = 10, repr.plot.height = 10)
set.seed(2020)
dpt <- DPT(dm,tips=1)
c=plot(dpt, 1:2)
c+theme_classic()
HSC diffusionmap 
tmp <- data.frame(DC1 = dm$DC1,
                 DC2 = dm$DC2,
                 timepoint = cell_type,
                 dpt = dpt$DPT1)
set.seed(2020)
options(repr.plot.width = 10, repr.plot.height = 10)
p <- ggplot(tmp, aes(x = DC1, y = DC2, colour = cell_type)) +
    geom_point(size=2) +
    xlab("Diffusion component 1") +
    ylab("Diffusion component 2") +
    theme_classic()
figure1 <- p + scale_color_manual(values=c(HSC="#2D6A4F",MPP="#85BC07",LMPP="#74c69d",
                                           CLP="#95A0FF",pDC="#4cc9f0",CMP="#F0ED4A",
                                           GMP="#F9C1F8",MEP="#FAA300",mono="#EE85F6",UNK="#e9ecef")) +
                theme(axis.title=element_text(size=25),axis.text=element_text(size=25),
                legend.text=element_text(size=20),legend.title=element_text(size=25),text = element_text(family="myfont")) +
                guides(colour = guide_legend(override.aes = list(size=5)))
figure1
HSC celltype