Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data

This is a scikit-learn compatible Python implementation of Stabl, coupled with useful functions and example notebooks to rerun the analyses on the different use cases located in the sample data folder

![DOI]

Overview

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models.

Requirements

• scikit-learn >= 1.1.2
• knockpy >= 1.2
• pandas >= 1.4.2
• numpy >= 1.23.1
• joblib >= 1.1.0
• tqdm >= 4.64.0
• seaborn >= 0.12.0
• matplotlib >= 3.5.2

Installation

Install Directly from github:

pip install git+https://github.com/gregbellan/Stabl.git

or

---

Download Stabl:

git clone https://github.com/gregbellan/Stabl.git

Install requirements and Stabl:

pip install .

The general installation time is less than 10 seconds, and have been tested on mac OS and linux system.

Input data

When using your own data, you have to provide

• The preprocessed input data matrix (preferably a pandas DataFrame having column names)
• The outcomes (preferably a pandas Series having a names)
• (Input Data and outcomes should have the same indices)

Sample Data

The "Sample Data" folder contains data for the following use cases:

Onset of Labor

Training

• Outcome: Days before Labor, 150 samples — 53 patients
• Proteomics: 150 samples — 1317 biomarkers
• CyTOF: 150 samples — 1502 biomarkers
• Metabolomics: 150 samples — 3529 biomarkers

Validation

• Outcome: Days before Labor, 27 samples — 10 patients
• Proteomics: 21 samples — 1317 biomarkers
• CyTOF: 27 samples — 1502 biomarkers

COVID-19

Training

• Outcome: Mild/Moderate (43) Vs. Severe (25)
• Proteomics: 68 samples — 1463 biomarkers

Validation

• Outcome: Mild/Moderate (125) Vs. Severe (659)
• Proteomics: 784 samples — 1420 biomarkers

CFRNA Preeclampsia

Training

• Outcome: Control (63) Vs. Preeclampsia (96) — 48 patients
• CFRNA: 159 samples — 37184 biomarkers

Surgical Site Infections (SSI)

Training

• Outcome: Control (77) Vs. SSI (16)
• CyTOF: 93 samples — 1125 biomarkers
• Proteomics: 91 samples — 721 biomarkers

Benchmarks

• Tutorial Notebooks.ipynb: Tutorial on how to use the library
• * Benchmarks.ipynb: Jupyter Notebook rerunning all the benchmarks

Cite

Julien Hedou, Ivana Maric, Grégoire Bellan et al. Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data, 27 February 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2609859/v1]