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https://github.com/nalomran/pyreqtl

A collection of Python modules equivalent to R ReQTL Toolkit aims to identify the association between expressed SNVs with their gene expression using RNA-sequencing data.
https://github.com/nalomran/pyreqtl

bioinformatics bioinformatics-analysis bioinformatics-tool data-science gene-expression matrixeqtl numpy pandas python python3 r rna-seq rna-seq-analysis rpy2 scipy snvs

Last synced: 3 months ago
JSON representation

A collection of Python modules equivalent to R ReQTL Toolkit aims to identify the association between expressed SNVs with their gene expression using RNA-sequencing data.

Host: GitHub
URL: https://github.com/nalomran/pyreqtl
Owner: nalomran
License: mit
Created: 2020-09-24T10:45:18.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2022-01-20T21:02:50.000Z (almost 3 years ago)
Last Synced: 2024-04-23T01:20:21.107Z (8 months ago)
Topics: bioinformatics, bioinformatics-analysis, bioinformatics-tool, data-science, gene-expression, matrixeqtl, numpy, pandas, python, python3, r, rna-seq, rna-seq-analysis, rpy2, scipy, snvs
Language: Python
Homepage:
Size: 3.1 MB
Stars: 10
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# PyReQTL

![GitHub](https://img.shields.io/github/license/nalomran/PyReQTL)
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## Introduction
PyReQTL is a python package/library that consist of collection of python modules developed for individuals or research
group who prefer to perform ReQTL analysis using python instead of R programming language. Hence, this library are
replica of the R toolkit **"ReQTL"** but implemented in Python (please see the section References for direct link of the
ReQTL R toolkit GitHub repo page). The original R toolkit requires a number of R packages such as
MatrixEQTL and among others Python lacks equivalent libraries. Furthermore, despite that many of the code-base in this
particular project were smoothly translated into python, there were needs to interface with R and to its
environment which includes importing R packages to utilize their functions from within Python and thus
the Python library "rpy2" and R are requirements to these modules.

Briefly, PyReQTL does as for the ReQTL identify the correlation between expressed SNVs and their gene expression using
RNA-sequencing data and each module transforms the sequencing files into ReQTL input files which will eventually be used
as inputs for the R package MatrixEQTL to find the significant variation-expression relationships.

## Features
- Faster than the R ReQTL version
- For efficiency and performance, it uses both Pandas DataFrames and numpy arrays
- rpy2 is more efficient and better integrated than using subprocess to interface with R

## Asking for Help

If you have an issue (error or bug) when executing the library or any of the modules or have any other enquires please
feel free to send me an [email](mailto:[email protected])

## Getting Started

Please follow the instructions in the sections that follow. You need to have a terminal to be able to execute the
modules via the command line interface (cli). In case you are getting an error or bug please refer to the section
**[Asking for Help](#asking-for-help)**.

## Operating System Requirement

PyReQTL modules were developed on *macOS Catalina v.10.15.2* and should work also on *Linux OS*.

## Python Version
Both Python version *3.7* and *3.8* were successfully tested on PyReQTL. You need Python version >=3.5.

**Note:**

- I would recommend checking your default python version which it may be python v2.7 so running ```python```
may start version *2.6* or *2.7* and therefore you need to run ```python3``` to start python 3 version
or you may set your python v3 as your default python version.
- Note that PyReQTL were not tested on Windows and it may or may not work.

## Prerequisites

* The following are the core Python libraries (dependencies) required for PyReQTL modules:

```
pandas
numpy
scipy
rpy2
```

* Optional Python Library:
```
mypy
```

* You will also need to install the latest version of *R (4.0.2)* from the the official language site
at: https://cloud.r-project.org

* #### **Important notes:**
1. Python will install the required R packages for you but you may be asked to choose a secure mirror for installing
those R packages.

2. You may also be asked when executing the modules that require R packages to upgrade some R dependencies, you may
ignore it by typing```no```.

3. Some bioconductor packages will also be required for the bioinformatics related analysis.

## Installation

1. Install the package with pip:

```bash
pip install pyreqtl
```

2. You could also clone the project with the command git clone:
```bash
git clone https://github.com/nalomran/PyReQTL.git
```

- Once cloned, you may now try to install the dependencies by running the command:
```bash
sudo pip install -r requirements.txt
```

- Then, issue the ```ls ``` command to list the files that end with .py (i.e. the PyReQTL modules) (OPTIONAL)
```bash
ls -1 *.py
```

## Quick example

```python
from PyReQTL import gene_matrix
gene_exp, gene_loc = gene_matrix.build_gene_matrix(gene_dir='data', prefx_out='ReQTL_test')

# file is saved for gene expression in output/ReQTL_test_gene-exp_matrix.txt
# file is saved for gene location in output/ReQTL_test_gene-exp-loc_matrix.txt

gene_exp.head(3)
# sample gene_id sample_1 sample_10 sample_11 sample_12 sample_13 sample_14 sample_15 ... sample_25 sample_3 sample_4 sample_5 sample_6 sample_7 sample_8 sample_9
# 0 ENSG00000000457 0.502402 -0.502402 -0.395725 -1.020076 -0.736316 -1.768825 -1.426077 ... 1.020076 1.198380 -0.194028 1.426077 -1.198380 -0.293381 1.768825 0.096559
# 1 ENSG00000000460 0.502402 0.395725 0.000000 -1.426077 -0.502402 -0.736316 -0.869424 ... 1.020076 1.198380 -1.198380 1.426077 -1.020076 -1.768825 1.768825 0.096559
# 2 ENSG00000000938 -0.096559 1.426077 -0.293381 0.293381 -0.736316 -1.198380 -0.395725 ... 1.020076 -0.194028 1.198380 0.736316 0.194028 -1.768825 0.502402 -1.426077
# [3 rows x 26 columns]

gene_loc.head(3)
# GeneID Reference Start End
# 0 ENSG00000237613 chr1 34554 36081
# 1 ENSG00000238009 chr1 89295 133723
# 2 ENSG00000239945 chr1 89551 91105
# [3 rows x 4 columns])
```
```bash
python -m PyReQTL.gene_matrix -i data -o ReQTL_test -c True
```

## Running the Modules

If you choose to clone the repo from Github, then you are given two options to run the modules:
1. Running the prepared shell script *"run_all.sh"* found at the parent's directory of *PyReQTL*. This script is
prepared to automatically run all modules sequentially with just a single command.

2. Running each individual python module separately.

### - Option One (run all the modules sequentially)

Run the whole analysis at once:

1. type ```cd ..``` to go back to the main directory in which the shell script lives

- You can modify the script based on your needs

2. run the following command:

```bash
sh run_all.sh
```

### - Option Two (run each module separately)

#### 1. build_gen_exp_matrix module

##### **Note**:

1. please refer to the link in the **[References](#references)** section for the description of each module
2. make sure you are in the ```PyReQTL``` directory in order to run each individual python module

- Run the following command to go the PyReQTL directory:
```bash
cd PyReQTL/
```

##### Inputs + Options

+ -i: a directory contain all the raw gene expression files from Stringtie

+ -o: the prefix for the gene expression and gene location files

##### Output
* the gene expression values to be fed into MatrixEQTL

* the gene location for MatrixEQTL

#### Command Example
```bash
python build_gen_exp_matrix.py -i ../data -o ReQTL_test
```

***

#### 2. build_VAF_matrix module

##### Inputs + Options

+ -r: a directory containing the .csv files from the output of readCounts
module

+ -o: the prefix for the SNV matrix and SNV location files

##### Output

+ the SNV locations for MatrixEQTL

+ the SNV variant allele fraction matrix for MatrixEQTL

##### Command Example
```bash
python build_VAF_matrix.py -r ../data -o ReQTL_test
```

***

#### 3. harmonize_matrices module

##### Input

+ -v: the path to the variant matrix generated from the build_VAF_matrix.py

+ -g: the path to the gene expression matrix generated from the build_gen_exp_matrix.py

+ -c: the path to the covariate matrix located in the data folder

##### Output
* three output files consists of the three input files that have mutual samples

##### Command Example
```bash
python harmonize_matrices.py \
-v output/ReQTL_test_VAF_matrix.txt \
-g output/ReQTL_test_gene-exp_matrix.txt \
-c ../data/covariates_matrix.txt
```
##### Note:
1. the covariates matrix file is supplied in the "data" folder
2. this is an optional module but it is recommended to avoid possible error or bug

***

#### 4. run_matrix_ReQTL module

This module requires the R package MatrixEQTL. Please refer to the section References for more
information about this package

##### Input

+ -s: the SNV or variant matrix file created from harmonize_matrices

+ -sl: the SNV location matrix file created from build_VAF_matrix

+ -ge: the gene expression file matrix created from build_gene-exp_matrix

+ -gl: the gene locations file created from build_gene-exp_matrix

+ -c: the covariates matrix file created from "harmonize_matrices.
[OPTIONAL]! you can access the file under data directory

+ -o: the prefix for the path to the output files

+ -ct: logical (T or F) specifying whether to split the output into cis
or trans

+ -pcis: p-value thresholds for the cis output files

+ -ptran: p-value thresholds for the trans output files

+ -p: p-value thresholds for the unified output file

##### Output

+ one output could be cis ReQTLs and trans ReQTLs or one with all of the
unified ReQTLs. This depends on what you choose for the value of parameter
"-ct"

+ QQ plot of p-values

##### Commands Example

* splitting *cis* and *trans*
```bash
python run_matrix_ReQTL.py \
-s output/ReQTL_test_VAF_matrix_harmonized.txt \
-sl output/ReQTL_test_VAF_loc_matrix.txt \
-ge output/ReQTL_test_gene-exp_matrix_harmonized.txt \
-gl output/ReQTL_test_gene-exp-loc_matrix.txt \
-c output/covariates_matrix_harmonized.txt \
-ct T \
-o "ReQTL_test" \
-pcis 0.001 \
-ptra 0.00001
```

* unified *cis* and *trans*
```bash
python run_matrix_ReQTL.py \
-s output/ReQTL_test_VAF_matrix_harmonized.txt \
-sl output/ReQTL_test_VAF_loc_matrix.txt \
-ge output/ReQTL_test_gene-exp_matrix_harmonized.txt \
-gl output/ReQTL_test_gene-exp-loc_matrix.txt \
-c output/covariates_matrix_harmonized.txt \
-ct F \
-o "ReQTL_test" \
-p 0.001
```
***

#### 5. annotate_cis_trans module

#### Input

+ -r: the path to the ReQTL analysis result file

+ -ga: the path to the file gene location annotations

+ -o: the prefix for the output annotated result

#### Output

* the ReQTLs annotated as cis or trans

##### Commands Example
```bash
python annotate_cis_trans.py \
-r output/ReQTL_test_all_ReQTLs.txt \
-ga ../data/gene_locations_hg38.txt \
-o ReQTL_test
```
***

## Authors & Acknowledgment for the ReQTL R Toolkit

- Liam Spurr, **Nawaf Alomran**, Pavlos Bousounis, Dacian Reece-Stremtan, Prashant N M, Hongyu Liu, Piotr Słowiński, Muzi Li, Qianqian Zhang, Justin Sein, Gabriel Asher, Keith A. Crandall, Krasimira Tsaneva-Atanasova, and Anelia Horvath

- We would like to thank the Matrix EQTL team (Shabalin, et al. 2012) for their sample code and R package
upon which *run\_matrix_ReQTL.R* is based.

## Paper
- https://academic.oup.com/bioinformatics/article/36/5/1351/5582649

## References

1. The ReQTL R toolkit github repository: https://github.com/HorvathLab/ReQTL

2. MatrixEQTL R package: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

3. readCounts module: https://github.com/HorvathLab/NGS/tree/master/readCounts