# RNA-seq quantifications from GEO

A major barrier to reusing public RNA-seq data has been that every submitter processes raw reads differently — different aligners, references, and counting rules — so counts from two studies are rarely directly comparable. To address this, NCBI computes a uniform set of RNA-seq quantifications for many GEO Series using a single consistent pipeline. This article explains what those are and how GEOquery retrieves them.

    library(GEOquery)

## NCBI-computed quantifications vs. submitter files

There are two distinct things you might mean by “the counts” for a GEO RNA-seq study:

1. Submitter-provided processed files — whatever the authors uploaded as supplementary files. Formats and gene models vary by study; GEOquery lists and downloads them (`getGEOSuppFiles()`) but does not interpret them.
2. NCBI-computed quantifications — a raw-count matrix (and accompanying annotation) that NCBI generated uniformly from the study’s SRA runs. These are comparable across studies and are what the GEOquery RNA-seq functions target.

Not every Series has NCBI quantifications; you can check first.

## Checking availability and loading

    # Does this Series have NCBI-computed quantifications?
    hasRNASeqQuantifications("GSE164073")
    #> [1] TRUE

    # Load them as a SummarizedExperiment
    se <- getRNASeqData("GSE164073")
    se
    #> class: SummarizedExperiment
    #> dim: 39376 18
    #> metadata(5): experimentData annotation protocolData genomeInfo
    #>   created_at
    #> assays(1): counts
    #> rownames(39376): 100287102 653635 ... 4576 4571
    #> rowData names(18): GeneID Symbol ... GOProcess GOComponent
    #> colnames(18): GSM4996084 GSM4996085 ... GSM4996100 GSM4996101
    #> colData names(45): title geo_accession ... time.point.ch1 tissue.ch1

The result is a `SummarizedExperiment`: `assay(se)` is the raw count matrix, `rowData(se)` carries gene annotation, and `colData(se)` the sample metadata. Because it is a `SummarizedExperiment`, it drops straight into the differential expression workflows described in the downstream analysis article.

    assay(se)[1:5, 1:3]
    #>           GSM4996084 GSM4996085 GSM4996086
    #> 100287102          2          5          3
    #> 653635           244        236        337
    #> 102466751         25         17         34
    #> 107985730          1          1          1
    #> 100302278          0          0          0

## Why raw counts (and what to do with them)

The NCBI quantifications are deliberately raw counts, not normalized values, because the correct normalization depends on your downstream method. Count-based differential expression tools expect raw counts and model the counting noise themselves:

- DESeq2
- edgeR
- limma with `voom`

See downstream analysis for a worked sketch.

## When you need the submitter’s files instead

If a study has no NCBI quantifications, or you specifically need the authors’ processed output (e.g. TPM, or a custom gene model), fall back to the supplementary files and read them yourself:

    getGEOSuppFiles("GSE164073", fetch_files = FALSE)   # inspect first
    #>                               fname
    #> 1 GSE164073_Eye_count_matrix.csv.gz
    #>                                                                                                   url
    #> 1 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE164nnn/GSE164073/suppl/GSE164073_Eye_count_matrix.csv.gz

## Where to go next

- Finding and downloading data — search for RNA-seq studies and cache large downloads.
- From GEO to downstream analysis — DESeq2 / edgeR / limma-voom from a count `SummarizedExperiment`.