ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Single Cell Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1636240
This article is part of the Research TopicAI in Single-Cell BiologyView all articles
Comprehensive Analysis of Multi-Omics Vaccine Response Data Using MOFA and Stabl Algorithms
Provisionally accepted
- Stanford University, Stanford, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
FluPRINT is a multi-omics dataset that measures donors' protein expression and cell counts across various assays. Donors were also assigned a binary value (0 or 1), being labeled as high responders (1) if they had a fold change ≥ 4 of the antibody titer for hemagglutinin inhibition (HAI) from day 0 to day 28, and low responders otherwise (0). In this project, we used the MOFA and Stabl algorithms to analyze FluPRINT, estimate the population structure from the data, and identify the most important features for predicting response to the vaccine. The preprocessing of the dataset included removing repeat features, scaling by assay, and removing outliers. Since Stabl does not directly address missing values, features with high amounts of missing values were removed and the remaining were ignored. MOFA identified the top feature in structure extraction as IL neg 2 CD4 pos CD45RA neg pSTAT5. MOFA explains well the variance of the data while also choosing features that have good significance, as illustrated by their significant p-values (p < 0.05). Stabl found the top feature for explaining the outcome to be CD33-CD3+ CD4+ CD25hiCD127low CD161+ CD45RA+ Tregs, which matched the top result of previously published analysis. MOFA's features achieved an AUROC of 0.616 (95% CI of 0.426-0.806), and Stabl's achieved an AUROC of 0.634 (95% CI of 0.432-0.823). Our research addresses a key knowledge gap: understanding how these fundamentally different analytical approaches perform when analyzing the same complex dataset. Our exploration evaluates their respective strengths, limitations, and biological insights and provides guidance on using MOFA and Stabl to find the best predictive cell subsets and features for understanding large immunological multi-omics data. The code for this project can be found at https://github.com/aanya21gupta/fluprint.
Keywords: influenza, FluPRINT, MOFA, Stabl, multi-omics, Vaccine, flu vaccine
Received: 27 May 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Gupta, Abe and Maecker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Holden Maecker, maecker@stanford.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.