ML in Omics

Project Topic: Machine Learning in Omics: Integration of Metagenomics and Metabolomics.

Source code and documentation for Life Science Technologies Project Course B, Aalto University, Finland.

How to Run

Environment Setup

(Optional) First, create a virtual environment:

python -m venv mlomics         # or `virtualenv mlomics`
source mlomics/bin/activate    # to activate the env

Next, install the requirements:

pip install -r requirements.txt

Analysis setup

First, the configuration for the study must be available in the configuration file. The config for each study looks something like this:

'mars_ibs_lr': {
    'dir': 'MARS_IBS_2020',
    'preprocessor': PC,
    'preproc_kwargs': {'var': 0.9},
    'classifier': LogisticRegression,
    'classifier_param_grid': {
        'penalty': ['l1', 'l2'],
        'C': [0.001, 0.01, 0.1, 1, 10, 100],
        'solver': ['liblinear', 'saga'],
        'max_iter': [100, 200, 300],
        'tol': [0.0001, 0.001, 0.01],
        'class_weight': [None, 'balanced'],
        'random_state': [42]
    },
},

Then, you can run the analysis using the following command and arguments:

python main.py --path "path/to/data" --study "mars_ibs_lr" --output "../output" --debug True

Analysis Description

Goals

Integration of Metabolomics and Metagenomics data for more accurate biomarker identification
Comparison of Metabolomics and Metagenomics in disease status prediction

Summary

Aspect	Value
Model	Logisticregression, RandomForest, ML-based classifiers (future)
Feature Engineering	PCA, LDA, $\chi^2$
Biomarker detection	Graph Convolutional Networks, model-based (LR)

Data

The datasets were obtained from this repository.

Authors

Alexandra Gorbonos

Alireza Tajmirriahi

Mohammad Sherafati

Salma Rachidi

AlirezaT99/ML-in-Omics