1. Installation

There are many ways to fetch the package presented and reproduce the results reported. Below we describe the different ways to install everything properly. We also provide some code and snippets to download and preprocess the presented datasets from their original sources so that each dataset can be easiliy and reliably retrieved.

Warning

Given that our project was developed using DeepLearning techniques, it requires a specific set of libraries to run. We provide the list of dependencies, but beware that installing such dependencies can download some big packages (e.g. torch bundled with a cudaSDK).

Warning

The dependencies listed require a python version >= 3.7

1.1. Getting started

Here are reported 3 ways in which you can have access to our code

1.1.1. Install via PyPy repository

We deployed our package to the PyPy repository; however to avoid specifying a fixed list of dependencies together with the packages, it is deployed standalone and requirements are provided separately in a file in our repository, so they have to be installed separately

Note that the warning above applies here: running pip install -r dependencies.txt will download some heavy packages.

$ pip install biopy
$ git clone https://github.com/BioPyTeam/biopy
$ cd biopy
$ pip install -r dependencies.txt

And that’s it!

1.1.2. Install via setup file

You can also simply clone the repo and install the package with the provided script. It will install biopy in the current python environment.

> git clone https://github.com/BioPyTeam/biopy
> cd biopy
> install.bat

1.1.3. Download the source code only

If you do not want to install any package and just download the code, you can do so. In this case, if you want to execute any of the provided snippets, you need to have src/biopy in your sys.path folder, or to execute commands from a sibling directory of src/biopy.

To download the source code, you can run this command:

$ git clone https://github.com/BioPyTeam/biopy

Note

You still need to get the third-party packages. See the provided dependencies.txt file

1.2. Downloading the datasets

As also stated elsewhere, the presented methods have been applied on three distinct datasets, two of which have been already presented and preprocessed in [YBV+21].

1.2.1. A549 Dataset

A549 Dataset is a paired multiomics (ATAC-seq and RNA-seq) single-cells dataset comprising A549 cells of tumoral lung tissue explanted from a 58-year-old caucasian male. One notable characteristic of this dataset is the fact that it is a paired dataset, which means that for a given sequencing in a given omic, there is also the corresponding sequencing in the other omic of the same cell.

ATAC-seq and RNA-seq have been preprocessed also in works other than [YBV+21]. So in order to download the preprocessed data from the all different original sources, we suggest to run this script:

$ python3 biopy/utils/download_dataset_nature.py --dataset_dir="dataset_a549"

1.2.2. CD4+ Dataset

This dataset contains two very different omics:

  • Preprocessed RNA single-cell sequencing of naive CD4+ T cells, which have been clustered into two groups: quiescent and poised cells

  • Grayscale 64x64 chromatin images of poised and quiescent single cells

This is the main dataset presented in [YBV+21] and it has been published by the authors on Dropbox.

$ wget --content-disposition https://www.dropbox.com/sh/hjt57go4dyahgq7/AAAhAE8bHNn5Sq-D0jGkO_gAa?dl=1
$ unzip MultiDomainTranslationNatureComm2020.zip

1.2.3. GDC Dataset

We also applied the proposed methods on a preprocessed dataset retrieved from The NCI’s Genomic Data Commons (GDC)

The multiomics dataset contains three omics (mRNA, miRNA and methilation) obtained from multicell sequencing of breast tissue.

If you want to recreate the dataset from the GDC portal you can simply run from bash the following command which will download the data leveraging the GDC API:

$ ./download_dataset.sh

If you want to run the provided script step by step, for each action (download_omic, …), there are additional options for customizing directories’ locations and other relevant parameters. See details with python3 biopy/utils/download_dataset_gdc.py {action} -h or python3 biopy/utils/download_dataset_gdc.py -h to get the list of available actions.

Warning

Even though the final preprocessed and splitted dataset weighs only a few gigabyte, the overall data that needs to be downloaded is around 100GB, and at least 350GB after decompression. Furthermore, during file downloads, network connections may get terminated, and so the provided bash script may error out. However, it can be safely run again after every failure until all files have been downloaded. In some cases, additional instruction may be presented to the user on screen