Single-Cell Proteomic Data for Estimating Influenza Infection Timing

Klodiana Rizzo Nervo published a dataset on figshare in March 2026. It contains proteomic data from a controlled human challenge study with influenza A/California/2009 (H1N1). The data was used to train random forest models for estimating days post-infection and distinguishing virus shedders.

Use Cases

• Training predictive models for infection timing based on immune population dynamics.
• Validating biomarkers for time since exposure to respiratory pathogens.
• Analyzing longitudinal immune cell subset profiles from mass cytometry panels.
• Developing models to distinguish virus shedders from non-shedders.
• Studying host immune response structure during influenza infection.

Strengths

• Data originates from a controlled human challenge study, providing a structured experimental context.
• Models were independently validated using data from a separate challenge study with the same virus.
• The dataset is openly available under a CC-BY-4.0 license.

Limitations

• The dataset is only 1.5 KB, indicating a very limited scope or summary-level data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Data from a controlled human influenza challenge study, profiled using 42-marker mass cytometry panels.

Freshness

Last updated 2026-03-19 08:43:39.