# MOVE (Multi-Omics Variational autoEncoder)

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The code in this repository can be used to run our Multi-Omics Variational

autoEncoder (MOVE) framework for integration of omics and clinical variabels

spanning both categorial and continuous data. Our approach includes training

ensemble VAE models and using *in silico* perturbation experiments to identify

cross omics associations. The manuscript has been published in Nature

Biotechnology:

> Allesøe, R.L., Lundgaard, A.T., Hernández Medina, R. *et al*. Discovery of

> drug–omics associations in type 2 diabetes with generative deep-learning

> models. *Nat Biotechnol* (2023). https://doi.org/10.1038/s41587-022-01520-x

We developed the method based on a Type 2 Diabetes cohort from the IMI DIRECT

project containing 789 newly diagnosed T2D patients. The cohort and data

creation is described in

[Koivula et al.](https://dx.doi.org/10.1007%2Fs00125-019-4906-1) and

[Wesolowska-Andersen et al.](https://doi.org/10.1016/j.xcrm.2021.100477). For

the analysis we included the following data:

Multi-omics data sets:

```

Genomics

Transcriptomics

Proteomics

Metabolomics

Metagenomics

```

Other data sets:

```

Clinical data (blood measurements, imaging data, ...)

Questionnaire data (diet etc)

Accelerometer data

Medication data

```

# Installation

## Installing MOVE package

MOVE is written in Python and can be installed using `pip`:

```bash

>>> pip install move-dl

```

## Requirements

MOVE should run on any environmnet where Python is available. The variational

autoencoder architecture is implemented in PyTorch.

The training of the VAEs can be done using CPUs only or GPU acceleration. If

you do not have powerful GPUs available, it is possible to run using only CPUs.

For instance, the tutorial data set consisting of simulated drug, metabolomics

and proteomics data for 500 individuals runs fine on a standard macbook.

> Note: The pip installation of `move-dl` does not setup your local GPU automatically

# The MOVE pipeline

MOVE has five-six steps:

```

01. Encode the data into a format that can be read by MOVE

02. Finding the right architecture of the network focusing on reconstruction accuracy

03. Finding the right architecture of the network focusing on stability of the model

04. Use model, determined from steps 02-03, to create and analyze the latent space

05. Identify associations between a categorical and continuous datasets

05a. Using an ensemble of VAEs with the t-test approach

05b. Using an ensemble of VAEs with the Bayesian decision theory approach

06. If both 5a and 5b were run select the overlap between them

```

## How to run MOVE

Please refer to our [**documentation**](https://move-dl.readthedocs.io/) for

examples and [tutorials](https://move-dl.readthedocs.io/tutorial/index.html)

on how to run MOVE.

Additionally, you can copy

[this notebook](https://colab.research.google.com/drive/1RFWNsuGymCmppPsElBvDuA9zRbGskKmi?usp=sharing)

and follow its instructions to get familiar with our pipeline.

# Data sets

## DIRECT data set

The data used in notebooks are not available for testing due to the informed

consent given by study participants, the various national ethical approvals for

the study, and the European General Data Protection Regulation (GDPR).

Therefore, individual-level clinical and omics data cannot be transferred from

the centralized IMI-DIRECT repository. Requests for access to summary statistics

IMI-DIRECT data, including those presented here, can be made to

DIRECTdataaccess@Dundee.ac.uk. Requesters will be informed on how summary-level

data can be accessed via the DIRECT secure analysis platform following

submission of appropriate application. The IMI-DIRECT data access policy is

available [here](https://directdiabetes.org).

## Simulated and publicaly available data sets

We have therefore provided two datasets to test the workflow: a simulated

dataset and a publicly-available maize rhizosphere microbiome data set.

# Citation

To cite MOVE, use the following information:

Allesøe, R.L., Lundgaard, A.T., Hernández Medina, R. *et al*. Discovery of

drug–omics associations in type 2 diabetes with generative deep-learning models.

*Nat Biotechnol* (2023). https://doi.org/10.1038/s41587-022-01520-x