Datasets:

xuejun72
/

HR-VILAGE-3K3M

license: mit
configs:
  - config_name: study_meta
    data_files:
      - split: study_meta
        path: study_meta.csv
    default: true
  - config_name: meta
    data_files:
      - split: RNA_seq
        path:
          - meta/E_MTAB_12993_nose_meta.csv
          - meta/E_MTAB_12993_blood_meta.csv
      - split: microarray
        path:
          - meta/GSE101709_meta.csv
          - meta/GSE101710_meta.csv

HR-VILAGE-3K3M: Human Respiratory Viral Immunization Longitudinal Gene Expression

This repository provides the HR-VILAGE-3K3M dataset, a curated collection of human longitudinal gene expression profiles, antibody measurements, and aligned metadata from respiratory viral immunization and infection studies. The dataset includes baseline transcriptomic profiles and covers diverse exposure types (vaccination, inoculation, and mixed exposure). HR-VILAGE-3K3M is designed as a benchmark resource to support the development and evaluation of deep learning models for longitudinal gene expression analysis and to facilitate research into the temporal dynamics of immune responses.

Fig: Overview of HV-RIGEL-3K. (a) HV-RIGEL-3K construction workflow. (b) Distribution of sample timepoints for vaccine and inoculation studies. (c) Composition of the dataset by platform, tissue type, study type, and pathogen.

Dataset Description:

This repo contains 66 studies (59 bulk and 7 single cell studies), comprising 3178 subjects, 14,136 observations, and 2,557,942 single cells.
We provide preprocessed and normalized gene expression data, raw gene expression data, metadata and antibody data.

Data Structure:

HR-VILAGE-3K3M/
├── README.md
├── study_meta.csv
├── bulk_gene_expr/
│    └── <study_ID>_gene_expr.csv
├── single_cell_gene_expr/
│    └── <study_ID>_processed.h5ad
├── meta/
│    └── <study_ID>_meta.csv
├── bulk_gene_expr_raw/
│    └── <study_ID>_raw.csv
└── antibody/
     └── <study_ID>_antibody.csv

study_meta.csv: Contains study-level metadata (e.g., platform, tissue type, study type) and serves as an overview of the repository. Users can use this file to filter and select a subset of studies based on custom criteria.
bulk_gene_expr/: Processed gene expression matrices for each study (sample-by-gene). All files share the same 41,667 gene columns, with 9,004 genes non-missing across all studies.
single_cell_gene_expr/: Contains processed .h5ad files for each single-cell RNA-seq study. Raw count matrices are stored in the .X attribute, and cell-level metadata is stored in the .obs dataframe.
meta/: Study-specific metadata files (.csv), aligned by row names with the corresponding expression data. All metadata files share the same column structure; missing values are marked as NA.
bulk_gene_expr_raw/: Raw probe-level expression matrices (probe-by-sample), provided when available to support custom preprocessing workflows.
antibody/: Raw antibody measurements with sample IDs matching those in the metadata, enabling integration with gene expression data at the subject level.

How to start:

Users could directly download all files and read files locally. Alternatively, the following provides (partially) loading the dataset into Python using dataset package.

repo_id = "xuejun72/HR-VILAGE-3K3M"
import pandas as pd
from datasets import load_dataset

Bulk gene expression data

Bulk gene expression data can be loaded and combined using two alternative approaches.

Use our predefined configuration name, and pass to name argument in load_dataset(). trust_remote_code=True is required.

Example 1, to download study_meta:

study_meta = load_dataset(repo_id, name="study_meta", trust_remote_code=True)["train"].to_pandas()

Example 2, to download and combine all meta datasets:

meta_dict = load_dataset(repo_id, name = "meta", trust_remote_code=True)
meta = meta_dict["train"].to_pandas()

Example 3, to download and combine all gene expression datasets. However, this is highly NOT recommended since their size are too large and the execution time will be long.

# Not recommended!
gene_expr_dict = load_dataset(repo_id, name = "gene_expr", trust_remote_code=True)
gene_expr = gene_expr_dict["train"].to_pandas()

In addition, we provide a study filter function before downloading and loading, which works for meta and bulk_gene_expr datasets. split_filter argument is designed for this filter, which is optional. By default, split_filter=None will download all datasets as shown before. split_filter is a dict Python object where key is filter factors taking values from ['study_type','platform','tissue','pathogen','vaccine'] and value is a list of categories for each key. value should be exact same as that in study_meta.csv. Some examples of a valid split_filter:

split_filter = {"study_type": ["inoculation","inoculation"]}
split_filter = {"study_type": ["vaccine"], "vaccine": ["Influenza TIV"]}
split_filter = {"study_type": ["vaccine"], "platform": ["RNA-seq"], "tissue": ["PBMC","nasal swab"], "pathogen": []}

Example 4, to download and combine a customized filtered meta dataset:

split_filter = {"study_type": ["vaccine"], "platform": ["RNA-seq"], "tissue": ["PBMC","nasal swab"], "pathogen": []}
meta_filtered_dict = load_dataset(repo_id, name = "meta", trust_remote_code=True, split_filter=split_filter)
for _, value in meta_filtered_dict.items(): 
  meta_filtered = value.to_pandas().set_index("row_name")
meta_filtered

Example 5, to download and combine a customized filtered gene expression dataset:

split_filter = {"study_type": ["vaccine"], "platform": ["RNA-seq"], "tissue": ["PBMC","nasal swab"], "pathogen": []}
gene_expr_filtered_dict = load_dataset(repo_id, name = "gene_expr", trust_remote_code=True, split_filter=split_filter)
for _, value in gene_expr_filtered_dict.items(): 
  gene_expr_filtered = value.to_pandas().set_index("row_name")
gene_expr_filtered

Use the exact path of one csv file, and pass to data_files argument in load_dataset().

Example 1, to download study_meta:
```
study_meta = load_dataset(repo_id, data_files = "study_meta.csv")["train"].to_pandas()
```
Example 2, to download antibody dataset for GSE101709:
```
antibody = load_dataset(repo_id, data_files = "antibody/GSE101709_antibody.csv")["train"].to_pandas()
```
Example 3, to download raw gene expression dataset for GSE117580:
```
raw = load_dataset(repo_id, data_files = "gene_expr_raw/GSE117580_raw.csv")["train"].to_pandas()
```
Note: for antibody and raw gene expression datasets, since different study has different columns which cannot be simply combined, loading them must using data_files argument and be one-by-one.

Single cell gene expression data

Single-cell gene expression data can be downloaded and accessed using the anndata package.

  import anndata as ad
  # Load the GSE195673 dataset
  GSE195673 = ad.read_h5ad("./GSE195673_processed.h5ad")
  # View cell-level metadata
  GSE195673.obs

A merged dataset containing all 7 studies is also provided, comprising 2,557,942 cells and 13,589 common genes:

  import anndata as ad
  # Load the combined dataset
  combined = ad.read_h5ad("./combined.h5ad")
  # View cell-level metadata
  combined.obs

The combined object includes detailed cell-level metadata such as sample_id, cell_type, sex, donor_id, time_point_day, dataset, covid_status, age, tissue, and study_type. It also contains dimensionality reductions (X_pca, X_umap) and graph-based neighbor information in obsp for downstream analysis.

Example code:

Single Cell Visualization

import scanpy as sc
# Visualize UMAP colored by dataset
sc.pl.umap(combined, color='dataset', save='_combined_by_dataset.png', show=True)

Data Relations:

The following duplicate studies (right nodes) are not included in HR-VILAGE-3K3M, while their source studies (left leaves) are included in HR-VILAGE-3K3M.

GSE73072_H3N2_DEE2 ──┐
                      ├── GSE52428
GSE73072_H1N1_DEE4 ──┘

GSE73072_H3N2_DEE2    ├── GSE30550

GSE73072_HRV_UVA   ──┐
GSE73072_RSV_DEE1  ── ├── GSE17156
GSE73072_H3N2_DEE2 ──┘

The studies within the following groups are from the same study group with less batch effect.

Group 1

E_MTAB_12993_blood E_MTAB_12993_nose
Group 2

GSE61754 GSE90732
Group 3

GSE73072_H1N1_DEE4 GSE73072_H1N1_DEE3 GSE73072_H3N2_DEE2 GSE73072_H3N2_DEE5 GSE73072_HRV_DUKE GSE73072_HRV_UVA GSE73072_RSV_DEE1
Group 4

GSE74811 GSE74813 GSE74815 GSE74816
Group 5

GSE48023 GSE48018
Group 6

GSE59635 GSE59654
Group 7

GSE59743 GSE101709 GSE101710
Group 8

GSE190001_PRIME GSE190001_BOOST
Group 9

SDY311 SDY112 SDY315
Group 10

GSE201533 GSE190747 GSE201642
Group 11

GSE246525 GSE276544 GSE247401
Group 12

GSE169159 GSE102012
Group 13

GSE29614 GSE29615 GSE29617
Group 14

GSE52005 GSE48762