Contents

1 Introduction

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and cell states. The main functions of this package are to construct gene regulatory networks and infer transcription factor (TF) activity at the single cell level by integrating scATAC-seq and scRNA-seq data and incorporating of public bulk TF ChIP-seq data.

There are three related packages: epiregulon, epiregulon.extra and epiregulon.archr, the two of which are available through Bioconductor and the last of which is only available through github. The basic epiregulon package takes in gene expression and chromatin accessibility matrices in the form of SingleCellExperiment objects, constructs gene regulatory networks (“regulons”) and outputs the activity of transcription factors at the single cell level. The epiregulon.extra package provides a suite of tools for enrichment analysis of target genes, visualization of target genes and transcription factor activity, and network analysis which can be run on the epiregulon output. If the users would like to start from ArchR projects instead of SingleCellExperiment objects, they may choose to use epiregulon.archr package, which allows for seamless integration with the ArchR package, and continue with the rest of the workflow offered in epiregulon.extra.

For full documentation of the epiregulon package, please refer to the epiregulon book.

2 Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
 
BiocManager::install("epiregulon")

Alternatively, you could install from github

devtools::install_github(repo ='xiaosaiyao/epiregulon')

Load package

library(epiregulon)
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
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3 Data preparation

Prior to using epiregulon, single cell preprocessing needs to performed by user’s favorite methods. The following components are required:
1. Peak matrix from scATAC-seq containing the chromatin accessibility information
2. Gene expression matrix from either paired or unpaired scRNA-seq. RNA-seq integration needs to be performed for unpaired dataset.
3. Dimensionality reduction matrix from either single modality dataset or joint scRNA-seq and scATAC-seq

This tutorial demonstrates the basic functions of epiregulon, using the reprogram-seq dataset which can be downloaded from the scMultiome package. In this example, prostate cancer cells (LNCaP) were infected in separate wells with viruses encoding 4 transcription factors (NKX2-1, GATA6, FOXA1 and FOXA2) and a positive control (mNeonGreen) before pooling. The identity of the infected transcription factors was tracked through cell hashing (available in the field hash_assignment of the colData) and serves as the ground truth.

# load the MAE object
library(scMultiome)
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mae <- scMultiome::reprogramSeq()
#> see ?scMultiome and browseVignettes('scMultiome') for documentation
#> loading from cache

# extract peak matrix
PeakMatrix <- mae[["PeakMatrix"]]

# extract expression matrix
GeneExpressionMatrix <- mae[["GeneExpressionMatrix"]]
rownames(GeneExpressionMatrix) <- rowData(GeneExpressionMatrix)$name

# define the order of hash_assigment
GeneExpressionMatrix$hash_assignment <- 
  factor(as.character(GeneExpressionMatrix$hash_assignment),
         levels = c("HTO10_GATA6_UTR", "HTO2_GATA6_v2", "HTO8_NKX2.1_UTR", "HTO3_NKX2.1_v2",
                    "HTO1_FOXA2_v2", "HTO4_mFOXA1_v2", "HTO6_hFOXA1_UTR", "HTO5_NeonG_v2"))
# extract dimensional reduction matrix
reducedDimMatrix <- reducedDim(mae[['TileMatrix500']], "LSI_ATAC")

# transfer UMAP_combined from TileMatrix to GeneExpressionMatrix
reducedDim(GeneExpressionMatrix, "UMAP_Combined") <- 
  reducedDim(mae[['TileMatrix500']], "UMAP_Combined")

Visualize singleCellExperiment by UMAP


scater::plotReducedDim(GeneExpressionMatrix, 
                       dimred = "UMAP_Combined", 
                       text_by = "Clusters", 
                       colour_by = "Clusters")