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Inference of transcription factor motif activity from single cell RNA-seq data.

## Project description

Please note: at the moment this package is being actively developed and might not always be stable.

# SCEPIA - Single Cell Epigenome-based Inference of Activity

SCEPIA predicts transcription factor motif activity from single cell RNA-seq data. It uses computationally inferred epigenomes of single cells to identify transcription factors that determine cellular states. The regulatory inference is based on a two-step process:

1. Single cells are matched to a combination of (bulk) reference H3K27ac ChIP-seq or ATAC-seq profiles.
2. Using the H3K27ac ChIP-seq or ATAC-seq signal in enhancers associated with hypervariable genes the TF motif activity is inferred.

Currently five different references are available, three for human and two for mouse. Different data sets may give different results, based on a) the type of data (H3K27ac ChIP-seq or ATAC-seq) and b) the different cell types being represented. While SCEPIA does not require exact matching cell types to give good results, it does work best when relatively similar cell types are in the reference.

The following references can be used:

• ENCODE.H3K27ac.human - All H3K27ac experiments from ENCODE. Includes cell lines, tissues
• BLUEPRINT.H3K27ac.human - All H3K27ac cell types from BLUEPRINT (mostly hematopoietic cell types)
• Domcke.ATAC.fetal.human - Fetal single cell-based ATAC-seq clusters from 15 different organs (Domcke et al 2020).
• Cusanovich.ATAC.mouse - ATAC-seq data of single cell-based clusters from 13 adult mouse tissues (Cusanovich et al, 2018).
• ENCODE.H3K27ac.mouse - All H3K27ac experiments from mouse ENCODE.

So sorry, but only human and mouse are supported for now. However, if you have data from other species you can try it if gene names tend to match. Make sure you use gene names as identifiers, and scepia will run fine. In our (very limited) experience this can yield good results, but there are a lot of assumptions on conservation of regulatory interactions. If you have a large collection of ATAC-seq or ChIP-seq reference experiments available you can also create your own reference with ScepiaDataset.create(). This is not well-documented at the moment, let us know if you need help to do so.

## Requirements and installation

You will need conda using the bioconda channel.

Make sure you have conda installed. If you have not used bioconda before, first set up the necessary channels (in this order!). You only have to do this once.

$conda config --add channels defaults$ conda config --add channels bioconda
$conda config --add channels conda-forge  Now you can create an environment for scepia: conda create -n scepia scepia>=0.5.0 # Note: if you want to use scepia in a Jupyter notebook, you also have to install the following packages: ipywidgets nb_conda. conda activate scepia  ## Usage ### Before using SCEPIA You have to install genomes that scepia uses through genomepy. The genomes that are used include hg38, hg19, mm10 and mm9, depending on the reference. For example, to install hg38: $ conda activate scepia
\$ genomepy install hg38


You only need to do this once for each genome.

Note: this is independent of which genome / annotation you used for your single cell RNA-seq!

### Command line

Remember to activate the environment before using it

conda activate scepia


The command line script scepia infer_motifs works on any file that is supported by scanpy.read(). We recommend to process your data, including QC, filtering, normalization and clustering, using scanpy. If you save the results to an .h5ad file, scepia can continue from your analysis to infer motif activity. However, the command line tool also works on formats such as CSV files or tab-separated files. In that case, scepia will run some basic pre-processing steps. To run scepia:

scepia infer_motifs <input_file> <output_dir>


### Jupyter notebook tutorial

A tutorial on how to use scepia interactively in Jupyter can be found here.

Single cell data should be loaded in an AnnData object. Make sure of the following:

• Gene names are used in adata.var_names, not Ensembl identifiers or any other gene identifiers.
• adata.raw stores the raw, log-transformed single cell expression data.
• The main adata object is filtered to contain only hypervariable genes.
• Louvain or Leiden clustering has been run.

Once these preprocessing steps are met, infer_motifs() can be run to infer the TF motif activity. The first time the reference data will be downloaded, so this will take somewhat longer.

from scepia.sc import infer_motifs

# load and preprocess single-cell data using scanpy

adata = infer_motifs(adata, dataset="ENCODE.H3K27ac.human")


The resulting AnnData object can be saved with the .write() method to a h5ad file. However, due to some difficulties with storing the motif annotation in the correct format, the file cannot be loaded with the scanpy load() method. Instead, use the read() method from the scepia package:

from scepia.sc import read
adata = read("my_saved_data.h5ad")


The resulting object can now be treated as a normal AnnData object.

### Determine enhancer-based regulatory potential

The approach to determine the enhancer-based regulatory potential (ERP) score per gene is based on the approach developed by Wang et al., 2016. There is one difference, in this approach the score is calculates based only on H3K27ac signal in enhancers. We use log-transformed, z-score normalized H3K27ac read counts in 2kb windows centered at enhancer locations. The ERP score is used to match single cell RNA-seq data to the reference H3K27ac profiles.

To use, an H3K27ac BAM file is needed (mapped to hg38). The -N argument specifies the number of threads to use.

scepia area27 <bamfile> <outfile> -N 12


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