Delete Seurat_analysis directory

library(Seurat)

library(GenomicRanges)

library(ggplot2)

library(dplyr) 

##################### Seurat analysis on merged cpm from replicates #####################

atac.m.matrix <- read.delim("ATAC_consensus_summit2kb_adj_cpm_merge_transpose.txt", row.names = 1, head=TRUE)

rownames(atac.m.matrix) = c("WT.H3K4me1","Npm1c.H3K4me1","Flt3-ITD.H3K4me1","DM.H3K4me1",

"WT.H3K4me3","Npm1c.H3K4me3","Flt3-ITD.H3K4me3","DM.H3K4me3",

"WT.H3K27ac","Npm1c.H3K27ac","Flt3-ITD.H3K27ac","DM.H3K27ac",

"WT.ATAC-seq","Npm1c.ATAC-seq","Flt3-ITD.ATAC-seq","DM.ATAC-seq")

atac.m.object <- CreateSeuratObject(counts = atac.m.matrix, project = "atac.m.summit", min.cells = 0, min.features = 0)

atac.m.object <- NormalizeData(atac.m.object, normalization.method = "RC", scale.factor = 1000000)

atac.m.object <- FindVariableFeatures(atac.m.object, selection.method = "vst")

m.id <- rownames(atac.m.object)

atac.m.matrix.scale <- ScaleData(atac.m.object, features = m.id)

atac.m.matrix.pca <- RunPCA(atac.m.matrix.scale,features = m.id,npcs = 50)

png("ATAC_consensus_summit2kb_adj_cpm_merg_pca_cutoff.png", width = 6, height = 4, units = "in", res=300)

ElbowPlot(atac.m.matrix.pca, ndims = 50)

dev.off()

## cutoff was set at 8 based on the graph

m.tmp <- FindNeighbors(atac.m.matrix.pca, reduction = "pca", dims = 1:8, nn.eps = 0.5)

m.tmp <- FindClusters(m.tmp, resolution = 0.5)

## Number of communities: 10

m.tmp3 <- RunTSNE(m.tmp, dims = 1:10)

##################### change the order of the clusters #####################

# first: to save old identity classes (the cluster labels) for reference.

m.tmp3[["old.ident"]] <- Idents(object = m.tmp3)

# Rename classes

m.tmp3 <- RenameIdents(object = m.tmp3, "0"="1","3"="2","7"="3","4"="4","9"="5","6"="6","5"="7","2"="8","1"="9","8"="10")

p1 <- DimPlot(m.tmp3, reduction = "tsne", pt.size = 0.1, label=TRUE, raster=FALSE,

order =  c("10","9","8","7","6","5","4","3","2","1")) +

ggtitle(label="Accessible chromatin regions") +

theme_bw() + theme_classic(base_size = 12, base_family = "") +

theme(axis.text = element_text(color="black", size=12, face="bold"),

axis.ticks = element_line(size = 1), axis.ticks.length = unit(.25, "cm"),

axis.title = element_text(color="black", size=14, face="bold"),

plot.title = element_text(color="black", size=14, face="bold",,hjust=0.5),

legend.title = element_text(color="black", size=14, face="bold",hjust=0.5),

legend.text = element_text(size=12, face="bold"))

ggsave("ATAC_consensus_summit2kb_adj_cpm_merg_tsne.2021.pdf", width = 5, height = 4.5)

pdf("ATAC_consensus_summit2kb_adj_cpm_merg_heatmap.pdf", width = 9.5, height = 5)

DoHeatmap(m.tmp3, features = m.id)

dev.off()

# Multiomics_integrative_analysis

A multi-layered multiomic approach to integrate chromatin analysis (Ma et al) at all accessible regions across wildtype and mutant HSPC was applied. 

Multiomics_Seurat_analysis.R

Ma, Q. et al. Super-Enhancer Redistribution as a Mechanism of Broad Gene Dysregulation in Repeatedly Drug-Treated Cancer Cells. Cell Rep. 31, 107532 (2020).