Innate immune responsiveness predicts both enhanced cellular immunity and symptomatic disease after controlled human influenza infection

This dataset contains processed multi-omics data and trained models used for integrative analysis of the H3N2 influenza challenge study using the MEFISTO framework. MEFISTO (Multi-omics Factor Analysis with Interpolation for Spatial and Temporal Outcomes) is a Gaussian process factor analysis method designed to capture structured variation across time and modalities. The dataset includes:- Preprocessed data- Trained MEFISTO models- Derived outputs used for downstream analysis and figure generation The accompanying notebooks (available in the linked GitHub repository) reproduce the full analysis pipeline, including preprocessing, model training, and interpretation of latent factors. This resource enables reproducibility of the results presented in the associated manuscript. Related publication for MEFISTO:https://www.nature.com/articles/s41592-021-01343-9 [Link to manuscript will be added upon publication] Data is available upon request: Please contact: l[dot]papargyris[at]imperial[dot]ac[dot]uk本数据集包含经预处理的多组学数据与训练完成的模型，用于基于MEFISTO框架开展H3N2流感攻毒试验的整合分析。 MEFISTO（Multi-omics Factor Analysis with Interpolation for Spatial and Temporal Outcomes）是一种高斯过程因子分析方法，旨在捕捉时间与多组学模态维度下的结构化变异。 本数据集涵盖以下内容：- 预处理完成的多组学数据- 训练好的MEFISTO模型- 用于下游分析与图表生成的衍生输出结果 配套的Jupyter笔记本（可在关联的GitHub仓库中获取）可复现完整分析流程，涵盖数据预处理、模型训练与隐因子解读环节。 本数据集可支持关联论文中所呈现结果的可复现性验证。 MEFISTO相关研究论文：https://www.nature.com/articles/s41592-021-01343-9 [论文正式发表后将补充关联链接] 数据集可通过申请获取：请联系：l[dot]papargyris[at]imperial[dot]ac[dot]uk