Leveraging baseline transcriptional features and information from single-cell data to power the prediction of influenza vaccine response.

ABSTRACT:

Introduction

Vaccination is still the primary means for preventing influenza virus infection, but the protective effects vary greatly among individuals. Identifying individuals at risk of low response to influenza vaccination is important. This study aimed to explore improved strategies for constructing predictive models of influenza vaccine response using gene expression data.

Methods

We first used gene expression and immune response data from the Immune Signatures Data Resource (IS2) to define influenza vaccine response-related transcriptional expression and alteration features at different time points across vaccination via differential expression analysis. Then, we mapped these features to single-cell resolution using additional published single-cell data to investigate

SUBMITTER: Ye X 

PROVIDER: S-EPMC10879619 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature