# scPECA
## Introduction
This is a python version of PECA2 gene regulatory
network construction software designed for single-cell data.
It has a faster running speed and a lower memory footprint.
## Installation
```commandline
pip install scPECA==2.0
```
## Run scPECA
### Input
scPECA requires to input the paired (sc)RNA-seq and (sc)ATAC-seq data,
and it provides data pre-processing in some formats.
(Pre-processing can also be done manually by the user)
#### Format 1
Paired bulk RNA-seq and ATAC-seq count data
sample_name_RNA.txt
<table>
<tr>
<td>gene1</td>
<td>10</td>
</tr>
<tr>
<td>gene2</td>
<td>3</td>
</tr>
</table>
sample_name_ATAC.txt
<table>
<tr>
<td>chr1_10000_10100</td>
<td>1</td>
</tr>
<tr>
<td>chr1_20000_20100 </td>
<td>0</td>
</tr>
</table>
#### Format 2
scRNA-seq count data and scATAC-seq count data within the same cluster
without meta information
sample_name_scRNA.csv
| | barcode1 | barcode2 |
|--------|----------|----------|
| gene1 | 1 | 19 |
| gene2 | 3 | 6 |
sample_name_scATAC.csv
| | barcode1 | barcode2 |
|------------------|----------|----------|
| chr1_10000_10100 | 1 | 0 |
| chr1_20000_20100 | 0 | 1 |
#### Format 3
scRNA-seq count data and scATAC-seq count data
with meta information
sample_name_scRNA.csv
| | barcode1 | barcode2 |
|--------|----------|----------|
| gene1 | 1 | 19 |
| gene2 | 3 | 6 |
sample_name_scRNA_meta.csv
<table>
<tr>
<td>barcode1</td>
<td>celltype1</td>
</tr>
<tr>
<td>barcode2</td>
<td>celltype2</td>
</tr>
</table>
sample_name_scATAC.csv
| | barcode1 | barcode2 |
|------------------|----------|----------|
| chr1_10000_10100 | 1 | 0 |
| chr1_20000_20100 | 0 | 1 |
sample_name_scATAC_meta.csv
<table>
<tr>
<td>barcode1</td>
<td>celltype1</td>
</tr>
<tr>
<td>barcode2</td>
<td>celltype2</td>
</tr>
</table>
#### Format 4
h5ad scRNA file with cell label
### Prior files
If you are the first time to run scPECA, please download the prior files as follows,
```
from scPECA.prior_install import figshare_download
import os
import scPECA
figshare_download(os.path.join(os.path.dirname(scPECA.__file__),'Prior.tar.gz'))
```
### Run example
```commandline
from scPECA.scPECA_main import scPECAclass
import scPECA
import os
# example 1
pkg_path = os.path.dirname(scPECA.__file__)
# demo data path
data_path = os.path.join(os.path.dirname(scPECA.__file__), 'Cones')
demo = scPECAclass(data_path, 'Cones', 'hg38', pkg_path) # Create a scPECA class
demo.RNA_process(2) # Format 2 RNA data processing
demo.ATAC_process(2) # Format 2 ATAC data processing
demo.network('Cones', data_path) # PECA2 GRN construction
# example 2
data_path = os.path.join(os.path.dirname(scPECA.__file__), '4cellline')
demo = scPECAclass(data_path, '4cellline', 'hg38', pkg_path)
demo.RNA_process(3)
demo.ATAC_process(3)
# example 3
data_path = os.path.join(os.path.dirname(scPECA.__file__), 'paul15')
demo = scPECAclass(data_path, 'paul15', 'mm10', pkg_path)
demo.RNA_process(4, 'paul15_clusters')
```
The details of other optional parameters can be viewed in python. All sample data has been downloaded with the software package.
### Output
sample_name_network.txt
TFTG_regulationScore.txt
### GRN Analysis Tools (example cell line: K562)
1. TFTG co-module analysis:
```
demo.co_module(celltype, num_clusters)
```
Result: TFmodule_result.txt, TGmodule_result.txt, co_module.png
2. TF layering
```
demo.tf_layer(celltype)
demo.top_tf_layer_plot(celltype)
```
Result: TF_layer.txt, top_TF_layer_graph.png
## System & Software Requirements
System: linux
Linux software: Homer
Python package: pybedtools, ismember, scipy, numpy_groupies,
## Considerations
1. RNA data preprocessing currently supports only hg38, hg19, mm10, mm9 genomes.
2. pybedtools may not support the latest version of
python and will require the creation of a new environment.
Raw data
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