Data Downloads

Access Complete Transcriptomic Datasets

Data Availability

All processed transcriptomic data from this project are freely available for download. The datasets are provided in both JSON and CSV formats for easy integration into your analysis pipelines.

Please cite this resource if you use the data in your research. Citation Information →

Bulk RNA-seq Dataset

Differential Expression Analysis: Ga30 vs Ga15

Laser capture microdissection (LCM) based bulk RNA-seq comparing seizure-onset (P30) to pre-seizure (P15) stages.

Dataset Details

  • Total Genes: 593 differentially expressed genes
  • Upregulated: 560 genes (94.4%)
  • Downregulated: 33 genes (5.6%)
  • Criteria: padj < 0.1, |log2FC| > 0.5
  • Analysis: DESeq2

File Information

  • Format: JSON
  • Fields: symbol, name, ensembl_id, log2fc, padj, regulation
  • Size: ~150 KB
Download Bulk RNA-seq DEGs (JSON) Search & Export Custom Dataset →

Spatial Transcriptomics Datasets

10X Genomics Visium Spatial Gene Expression

Spatially-resolved transcriptomics from thalamocortical tissue at two developmental timepoints.

Spatial Transcriptomics - P15 (Pre-seizure)

Dataset Details
  • Total DEGs: 1,079 genes
  • Upregulated: 509 genes (47.2%)
  • Downregulated: 570 genes (52.8%)
  • Samples: 4 (GA1, GA2, WH1, WH3)
File Information
  • Format: JSON
  • Fields: symbol, name, ensembl_id, log2fc, adj_p_val, regulation
  • Size: ~300 KB
Download Spatial P15 DEGs (JSON)

Spatial Transcriptomics - P30 (Seizure-onset)

Dataset Details
  • Total DEGs: 1,079 genes
  • Upregulated: 565 genes (52.4%)
  • Downregulated: 514 genes (47.6%)
  • Samples: 4 (GA1, GA2, WH1, WH2)
File Information
  • Format: JSON
  • Fields: symbol, name, ensembl_id, log2fc, adj_p_val, regulation
  • Size: ~300 KB
Download Spatial P30 DEGs (JSON)

Spatial Transcriptomics CSV Summaries

Quick-access CSV files for top differentially expressed genes

Pre-generated CSV summary files containing the top 25 upregulated and top 25 downregulated genes by absolute log2 fold change for each spatial transcriptomics timepoint.

P15 Top DEGs

Dataset Details
  • Genes: 50 (25 up, 25 down)
  • Timepoint: Postnatal day 15
  • Status: Pre-seizure
  • Size: 4.2 KB
Columns

Symbol, Gene_Name, logFC, Average_Expression, t_Statistic, P_Value, Adjusted_P_Value, Regulation

Download P15 CSV

P30 Top DEGs

Dataset Details
  • Genes: 50 (25 up, 25 down)
  • Timepoint: Postnatal day 30
  • Status: Seizure-onset
  • Size: 4.5 KB
Columns

Symbol, Gene_Name, logFC, Average_Expression, t_Statistic, P_Value, Adjusted_P_Value, Regulation

Download P30 CSV

Summary Statistics

Dataset Details
  • Comparisons: P15 & P30
  • Metrics: DEG counts, percentages
  • Purpose: Quick overview
  • Size: 0.2 KB
Columns

Timepoint, Total_DEGs, Upregulated, Downregulated, Percent_Up, Percent_Down, Mean_logFC

Download Summary CSV

CSV Format Notes

  • Delimiter: Comma (,) - standard CSV format
  • Headers: First row contains column names
  • logFC: Log2 fold change (positive = upregulated, negative = downregulated)
  • Regulation: "Up" or "Down" for easy filtering
  • Sorted by: Absolute log2 fold change (highest to lowest)

Need the complete datasets?

These CSV files contain only the top 50 genes per timepoint. For complete datasets with all 1,079 genes:

  • Download the full JSON files above (P15 & P30 sections)
  • Use the Gene Search Tool to export custom filtered datasets

10X Genomics Loupe Browser Files

Interactive spatial transcriptomics analysis platform

What's Included

Download .cloupe files for advanced interactive analysis in 10X Genomics Loupe Browser desktop application:

  • Full spatial transcriptome data (~20,000 genes)
  • H&E tissue images with spatial coordinates
  • Gene expression matrices
  • Clustering and annotation data
  • Interactive visualization capabilities

Requirements

  • Software: 10X Genomics Loupe Browser (free download)
  • OS: Windows, macOS, or Linux
  • RAM: 8 GB minimum, 16 GB recommended
  • Disk Space: ~1 GB for both files

Installation Guide →

P15 Pre-Seizure - Loupe Browser File

Sample Metadata
  • Sample ID: ga2p15_2
  • Phenotype: Pre-seizure baseline
  • Timepoint: Postnatal day 15 (P15)
  • Description: Before seizure onset
File Information
  • Filename: GAERS_P15_PreSeizure.cloupe
  • Size: 275 MB
  • Platform: 10X Genomics Visium
  • Spots: ~5,000 spatial barcodes
  • Genes: ~20,000 detected
Download P15 Loupe File (275 MB)
MD5 Checksum for file verification 75165a5804a3ccfffa72af6f1232de4f

P30 Seizure-Onset - Loupe Browser File

Sample Metadata
  • Sample ID: ga1p30_2
  • Phenotype: Seizure-onset
  • Timepoint: Postnatal day 30 (P30)
  • Description: At onset of absence seizures
File Information
  • Filename: GAERS_P30_SeizureOnset.cloupe
  • Size: 328 MB
  • Platform: 10X Genomics Visium
  • Spots: ~5,000 spatial barcodes
  • Genes: ~20,000 detected
Download P30 Loupe File (328 MB)
MD5 Checksum for file verification 011b9ca08f1b38f4e8bc3b9de6193f03

Getting Started

  1. Download Loupe Browser from 10X Genomics website (free, Windows/macOS/Linux)
  2. Download .cloupe files using the buttons above
  3. Open in Loupe Browser and start exploring spatial gene expression
  4. Analyze interactively: Query any gene, draw custom regions, perform differential expression

Complete Installation Guide & Tutorials →

Pathway Enrichment Analysis

GO & Reactome Enrichment Results

Functional enrichment analysis of differentially expressed genes using Gene Ontology and Reactome pathways.

Dataset Details

  • Total Terms: 43 enriched pathways
  • Categories: Biological Process, Molecular Function, Reactome
  • Criteria: p.adjust < 0.05
  • Analysis: clusterProfiler

File Information

  • Format: JSON
  • Fields: description, gene_ratio, p_adjust, gene_count, category
  • Size: ~50 KB
Download Enrichment Results (JSON)

Gene Master Index

Comprehensive Gene Database Across All Datasets

Integrated database containing all 3,611 unique genes from bulk RNA-seq, spatial transcriptomics, and seizure gene annotations.

Database Details

  • Total Genes: 3,611 unique genes
  • Bulk RNA-seq: 593 genes
  • Spatial P15: 1,079 genes
  • Spatial P30: 1,079 genes
  • Seizure Genes: 2,007 genes

File Information

  • Format: JSON
  • Fields: symbol, name, ensembl_id, chromosome, datasets, expression data
  • Size: ~1 MB
Note: This master index integrates data from all sources. Each gene entry contains differential expression data from all datasets where the gene was detected.
Download Gene Master Index (JSON) Interactive Gene Search →

Data Format Documentation

JSON File Structure

All JSON files follow a consistent structure for easy parsing and integration.

Differential Expression Files (DEG)

{
  "genes": [
    {
      "symbol": "Cacna1g",
      "name": "calcium voltage-gated channel subunit alpha1 G",
      "ensembl_id": "ENSRNOG00000018056",
      "log2fc": 1.234,
      "padj": 0.001,
      "regulation": "up",
      "significant": true
    },
    ...
  ]
}

Enrichment Analysis Files

{
  "enrichment": {
    "biological_process": [
      {
        "description": "postsynaptic density organization",
        "gene_ratio": "25/560",
        "p_adjust": 0.00012,
        "gene_count": 25,
        "genes": "Gene1/Gene2/Gene3/..."
      },
      ...
    ]
  }
}

Gene Master Index

{
  "genes": [
    {
      "symbol": "Cacna1g",
      "name": "calcium voltage-gated channel subunit alpha1 G",
      "ensembl_id": "ENSRNOG00000018056",
      "chromosome": "1",
      "seizure_gene": true,
      "bulk_rnaseq": {
        "log2fc": 1.234,
        "adj_p_val": 0.001,
        "regulation": "up"
      },
      "spatial_p15": {
        "log2fc": 0.987,
        "adj_p_val": 0.005,
        "regulation": "up"
      },
      "spatial_p30": null
    },
    ...
  ]
}

Working with the Data

Python Example

import json
import pandas as pd

# Load JSON data
with open('rnaseq-degs.json') as f:
    data = json.load(f)

# Convert to DataFrame
df = pd.DataFrame(data['genes'])

# Filter upregulated genes
up_genes = df[df['regulation'] == 'up']

R Example

library(jsonlite)

# Load JSON data
data <- fromJSON("rnaseq-degs.json")

# Extract genes
genes_df <- data$genes

# Filter upregulated genes
up_genes <- genes_df[genes_df$regulation == "up", ]

Custom Data Export

For customized datasets tailored to your research needs, use our interactive gene search tool.

Features:

  • Search & Filter: Find genes by symbol, name, or Ensembl ID
  • Advanced Filtering: Filter by regulation, p-value, fold change, datasets
  • CSV Export: Download filtered results as CSV with all metadata
  • Interactive: Sort, paginate, and explore data before downloading
Example Use Cases:
  • Export only upregulated genes with |log2FC| > 2
  • Get seizure-related genes present in bulk RNA-seq
  • Download genes enriched in specific GO pathways
  • Create custom gene lists for pathway analysis
Open Gene Search Tool →

Data Usage & Citation

Please Cite This Resource

If you use data from gaers.bio in your research, please cite:

Özdemir, Ö. (2025). gaers.bio: Spatiotemporal Transcriptomic Analysis of Epileptogenesis in the GAERS Model. TÜBİTAK Project 122S431. Available at: https://gaers.bio

Full Citation Information →

Terms of Use

  • Data are freely available for academic and research purposes
  • Proper attribution is required when using the data
  • For commercial use, please contact the PI
  • Redistribution is permitted with proper attribution