GEO: Finding Public Gene Expression Data
GEO (Gene Expression Omnibus) is an NCBI repository where researchers publish the gene expression data and experimental descriptions used in papers. Just as you find a project in a code repository, in GEO you find data and metadata through study-level accessions.
First step in GEO: confirm that the research question and sample composition fit your analysis, then choose the file that matches the processing stage you need.
An accession is the address of the data
Section titled “An accession is the address of the data”Each GEO page has a stable identifier such as GSE251845. This is called an accession. Because it points to a dataset more precisely than a paper title, record the accession in searches, code, and analysis notes.
| Prefix | Unit | In a structure familiar to developers |
|---|---|---|
GSE | A study grouping multiple samples, called a Series | Project |
GSM | One sample | Data object inside a project |
GPL | Platform used for measurement | Build environment or equipment specification |
One GSE contains multiple GSMs. Read the overall study design on the GSE page, then inspect the tissue, treatment conditions, and files for a specific sample on its GSM page.
Four things to check first on the page
Section titled “Four things to check first on the page”- Check the research question and comparison groups under Summary and Overall design.
- Under Samples, check the sample count, tissue, conditions, and whether samples are matched by patient.
- Under Supplementary file, check for processed files such as a count matrix.
- Follow the SRA link to see whether the FASTQ files can be downloaded again.
Public availability alone does not make data comparable. You must also determine whether samples are tumour and adjacent normal tissue, tissues from different people, and whether they used the same sequencing protocol and genome build.
Should you download the count matrix or FASTQ?
Section titled “Should you download the count matrix or FASTQ?”If the learning goal is differential-expression statistics, starting from a researcher-provided raw count matrix is usually best. The download is small and you can skip alignment and quantification.
To reproduce the QC, alignment, and quantification pipeline, start with FASTQ from SRA. This requires much more storage and computation, and you must apply the same pipeline to every sample. The workflow after FASTQ continues in Checking FASTQ Quality and Trimming and Aligning RNA Reads to the Genome.
What to record for reproducibility
Section titled “What to record for reproducibility”- GSE and GSM accessions
- File names and download date
- Sample inclusion and exclusion criteria
- Rules used to construct condition and patient IDs
- Whether values are raw counts or normalized
- Genome build and quantification method reported in the paper and GEO page