• Overview
• Project Structure
• Data Requirements
• Analysis Workflow
• File Organization
• Helper Functions
• External Datasets
• License
• Support

This project analyzes immune system changes during aging and vaccination responses using:

• Multi-omics data: scRNA-seq, flow cytometry, Olink proteomics, total IgG, HAI assay
• Multiple cohorts: BRI s with longitudinal sampling and SF4 cohort
• Comprehensive cell typing: 71 distinct immune cell types
• External validation: OneK1K, Terekhova, AIFI RA, and Tissue Aging datasets
• Time series analysis: Multiple timepoints (Y1D0, Y1D7, Y2D0, etc.)

├── Celltype_Mapping/           # Cell type classification hierarchy
├── Color_hex_codes/            # Standardized color schemes
├── Conda_Environment/          # Python and R environment files
├── Dataset/                    # Data storage and download scripts
├── Demographics_table/         # Cohort demographic analysis
├── Figure1-5/                  # Main manuscript figures
├── Extended_Figure1-9/         # Extended analysis figures
├── Supplementary_Figure1-2/    # Supplementary figures
├── Supplementary_Table1/       # Supplementary tables
├── helper_function/            # Reusable analysis functions
├── README.md                   # This file
└── LICENSE.txt                 # Allen Institute Software License

• BRI Cohort: Single-cell RNA-seq, flow cytometry, Olink proteomics, HAI, total IgG assay
• SF4 Cohort: Single-cell RNA-seq, flow cytometry, Olink proteomics

• OneK1K: Population-scale scRNA-seq data
• Terekhova Dataset: Independent aging study
• RA Dataset: Rheumatoid arthritis cohort in AIFI
• Tissue Aging Dataset: Tissue aging dataset

• scRNA-seq: H5AD files with AnnData format
• Flow cytometry: FCS files processed through standard gating
• Proteomics: Olink NPX values
• Metadata: CSV files with sample annotations
• Total IgG/HAI Assay: CSV files

Python:

• scanpy, pandas, numpy, matplotlib, seaborn
• scikit-learn, scipy, statsmodels
• celltypist, milopy, harmonypy

R:

• DESeq2, limma, edgeR
• ggplot2, dplyr, tidyr

Dataset/
├── scRNA/
│   ├── BRI/                    # BRI cohort scRNA-seq data
│   └── SF4/                    # SF4 cohort scRNA-seq data
├── FlowCyto/                   # Flow cytometry data
├── Olink/                      # Proteomics data
├── HAI/                        # Hemagglutination inhibition data
└── MSD/                        # Flu specific total IgG

• hex_to_rgb(): Convert hex colors to RGB
• create_cmap_from_hex(): Create custom colormaps
• plot_nhood_graph(): Plot neighborhood graphs for spatial analysis
• grouped_obs_sum_raw(): Aggregate raw counts by group
• grouped_obs_mean(): Calculate mean expression by group

• read_pseudobulk_expression(): Parallel data reading
• filter_genes_and_celltype(): Data filtering utilities
• deseq2_analysis(): DESeq2 differential expression wrapper
• clr_transform(): Centered log-ratio transformation

Standardized color palettes are defined in Color_hex_codes/:

• Cell types: 71 distinct colors for immune cell types
• Demographics: Age groups, sex, CMV status
• Consistent theming: Across all figures and analyses

The AIFI_Reference.json file defines a hierarchical cell type classification:

• Major categories: B cell, T cell, NK cell, Monocyte, DC, etc.
• Subcategories: Memory, Naive, Effector subtypes
• Detailed annotations: 71 specific cell type labels

• Composite aging scores
• Cell type frequency changes
• Gene expression alterations
• Cross-cohort validation

• Pre/post vaccination comparisons
• Vaccine-specific effects
• Time course analysis

• scRNA-seq + flow cytometry
• scRNA-seq + proteomics
• Cross-platform validation
• Pathway analysis

This project is licensed under the Allen Institute Software License - a 2-clause BSD license with additional restrictions on commercial use. See LICENSE.txt for details.

Key restrictions:

• Commercial use requires written permission from Allen Institute
• Redistribution must include copyright notice
• No warranty provided

This code is released AS IS without active support. While we welcome issues and questions, please understand that active responses are not guaranteed.

If you use this code/dataset in your research, please cite our manuscript.

Gong, Q., Sharma, M., Glass, M.C. et al. Multi-omic profiling reveals age-related immune dynamics in healthy adults. Nature (2025). https://doi.org/10.1038/s41586-025-09686-5

Last updated: Oct 2025
Maintainer: Qiuyu Gong