Single-cell studies are now a large fraction of GEO submissions, but they do not fit the classic GEO mental model. This article explains why single-cell data is awkward to retrieve, the file formats you will meet, and how GEOquery’s single-cell functions turn them into `SingleCellExperiment` objects ready for the Bioconductor single-cell ecosystem.

    library(GEOquery)

## Why single-cell data is different

For a microarray or bulk RNA-seq study, the processed matrix lives in the GEO Series Matrix file, and `getGEO("GSE...")` hands you a `SummarizedExperiment` directly. Single-cell data almost never works this way. The Series Matrix for a single-cell `GSE` is usually empty or contains only sample-level metadata, because a per-cell matrix with tens of thousands of columns does not belong in GEO’s sample-by-feature table.

Instead, the actual data lives in supplementary files (see Understanding GEO data formats). So a single-cell workflow is fundamentally supplementary-files-first: you must look at what is attached, decide what is loadable, and then load it — which is exactly the shape of the GEOquery single-cell API.

## The file formats you will meet

GEO single-cell submissions are heterogeneous. The common formats:

- 10x Matrix Market triplet — three files per sample: a sparse `matrix.mtx[.gz]`, `barcodes.tsv[.gz]` (cell IDs), and `features.tsv[.gz]`/`genes.tsv[.gz]` (gene IDs). The three must be read together; a missing file makes the sample unreadable.
- 10x HDF5 (`.h5`) — the CellRanger filtered/raw feature-barcode matrix in a single HDF5 file.
- AnnData (`.h5ad`) — the scverse/Python standard; increasingly common on recent GEO submissions.
- Seurat `.rds` — a saved Seurat object. GEOquery reads these (detected by class) and coerces them to a `SingleCellExperiment`; see Working with Seurat below.
- loom — less common, and intentionally not handled by GEOquery (read it with `LoomExperiment` directly).

A further wrinkle: files are frequently bundled inside a single `GSE_RAW.tar` archive, and naming conventions vary wildly between submitters. This is why the first step is always inspection, not download.

## Step 1 — inspect: the manifest

`geoSingleCellManifest()` lists the supplementary files and classifies each by format and role, extracting the GSM sample id and platform — without downloading anything. It lets you see, often many gigabytes ahead of time, what a study actually contains. GSE132771 is a two-platform study (mouse and human) where each sample contributes a 10x Matrix Market triplet:

    m <- geoSingleCellManifest("GSE132771")
    nrow(m)      # every supplementary file across the study
    #> [1] 72
    # three files per sample: matrix + barcodes + features (a `url` column, omitted
    # here for width, gives each file's download location)
    head(m[, c("fname", "sample", "format", "role", "platform")])
    #>                                   fname     sample  format     role platform
    #> 1 GSM3891612_Bleo1_GFPp_barcodes.tsv.gz GSM3891612 10x_mtx barcodes GPL21103
    #> 2    GSM3891612_Bleo1_GFPp_genes.tsv.gz GSM3891612 10x_mtx features GPL21103
    #> 3   GSM3891612_Bleo1_GFPp_matrix.mtx.gz GSM3891612 10x_mtx   matrix GPL21103
    #> 4 GSM3891613_Bleo2_GFPp_barcodes.tsv.gz GSM3891613 10x_mtx barcodes GPL21103
    #> 5    GSM3891613_Bleo2_GFPp_genes.tsv.gz GSM3891613 10x_mtx features GPL21103
    #> 6   GSM3891613_Bleo2_GFPp_matrix.mtx.gz GSM3891613 10x_mtx   matrix GPL21103

## Step 2 — decide: loadable units

`geoSingleCellUnits()` collapses that file list into loadable units — one per sample and format — and reports whether each is complete and which platform it belongs to. A 10x triplet is only loadable if all three files (`matrix`/`barcodes`/`features`) are present; the `status` and `loadable` columns tell you, so incomplete samples never fail mid-download:

    u <- geoSingleCellUnits(m)
    nrow(u)      # one unit per sample
    #> [1] 24
    head(u)
    #>             unit     sample platform  format n_files   status loadable
    #> 1 mtx|GSM3891612 GSM3891612 GPL21103 10x_mtx       3 complete     TRUE
    #> 2 mtx|GSM3891613 GSM3891613 GPL21103 10x_mtx       3 complete     TRUE
    #> 3 mtx|GSM3891614 GSM3891614 GPL21103 10x_mtx       3 complete     TRUE
    #> 4 mtx|GSM3891615 GSM3891615 GPL21103 10x_mtx       3 complete     TRUE
    #> 5 mtx|GSM3891616 GSM3891616 GPL21103 10x_mtx       3 complete     TRUE
    #> 6 mtx|GSM3891617 GSM3891617 GPL21103 10x_mtx       3 complete     TRUE

This is where you make decisions: which samples, which platform, and which incomplete units (`loadable = FALSE`) to skip.

## Step 3 — load

For the common, well-structured cases, `getGEOSingleCell()` does the whole thing — manifest → units → download → read — and returns a named list of `SingleCellExperiment`, one per sample. It tells you what it loads and what it skips, so nothing disappears silently:

    sces <- getGEOSingleCell("GSE132771")
    #> Loading GSM3891612 (10x_mtx)...
    #> Loading GSM3891613 (10x_mtx)...
    #> ... (one per sample)

    length(sces)      # one SingleCellExperiment per sample
    sces[[1]]

Any unit reported `loadable = FALSE` (an incomplete 10x triplet, say) is skipped with a message rather than failing the whole call, so nothing disappears silently.

By default it returns a list rather than a single combined object: per-sample matrices often use different references or feature sets, and silently reconciling them would be misleading. Because GSE132771 spans two platforms, combining is meaningful only within a platform — `by = "platform"` combines the mouse and human samples separately and refuses to merge across them:

    per_platform <- getGEOSingleCell("GSE132771", by = "platform")

### Sample metadata travels with the cells

The single-cell matrices GEO stores as supplementary files carry no phenotype information — the sample’s age, sex, genotype, tissue, treatment, and so on live in the GEO sample record, not in the matrix. By default `getGEOSingleCell()` fetches that per-sample metadata and attaches it to each object’s `colData`, broadcast across the sample’s cells, so it is there when you need it (and it survives combining with `by = "platform"` / `by = "all"`):

    sces <- getGEOSingleCell("GSE132771")
    cd <- SummarizedExperiment::colData(sces[[1]])
    cd[, grep("^sample\\.", colnames(cd))]
    #>              sample.title sample.source_name_ch1 sample.genotype.ch1 ...
    #> AAACCTGAG..  Normal lung  Normal human lung      wild type           ...
    #> AAACCTGAGT.. Normal lung  Normal human lung      wild type           ...

Added columns are prefixed `sample.` so they never clash with the importer’s own `colData`. The metadata comes from the Series Matrix (one small extra download); pass `addSampleMeta = FALSE` to skip it. Whole-study files that carry no GSM get no sample columns (there is no single sample to attribute them to).

### Full control

`getGEOSingleCell()` deliberately handles only common layouts. For anything unusual — bespoke naming, a single combined matrix for many samples, files inside a `_RAW.tar` — use the manifest to find what you want, download with `getGEOSuppFiles()`, and read one unit at a time with `readGEOSingleCell()`:

    readGEOSingleCell(path_to_dir_or_file)            # format auto-detected
    readGEOSingleCell(triplet_files, format = "10x_mtx")

## The reader dependencies

Reading uses focused Bioconductor importers, kept as optional dependencies so a basic GEOquery install stays light:

- 10x mtx / 10x h5 → TENxIO
- h5ad → anndataR, which reads AnnData natively in R with no Python dependency.

GEOquery will prompt you to install the relevant one if it is missing.

## Working with Seurat

GEOquery speaks `SingleCellExperiment` internally, but Seurat is one coercion away and supported at both edges (Seurat is an optional dependency).

Get Seurat objects directly:

    seurat_list <- getGEOSingleCell("GSE161228", as = "Seurat")
    # or one object:
    seu <- readGEOSingleCell(path, as = "Seurat")

Read a Seurat object that a submitter saved as a `.rds` supplementary file (its contents are detected by class and coerced to a `SingleCellExperiment`):

    sce <- readGEOSingleCell(path_to_rds)        # Seurat .rds -> SingleCellExperiment

Or coerce by hand, in either direction:

    seurat_obj <- Seurat::as.Seurat(sce)
    sce <- Seurat::as.SingleCellExperiment(seurat_obj)

## Downstream: the single-cell ecosystem

Once you have a `SingleCellExperiment`, you are in the heart of Bioconductor’s single-cell stack. Natural next steps:

- Quality control, normalization, and feature selection with scater and scran.
- Dimensionality reduction, clustering, and annotation following the Orchestrating Single-Cell Analysis with Bioconductor (OSCA) book.
- On-disk / out-of-memory handling of large matrices with HDF5Array and DelayedArray.

## What is intentionally out of scope

GEOquery’s single-cell support targets the discovery-and-load problem, not everything. It does not handle loom files, files packaged inside `_RAW.tar`, or idiosyncratic combined-matrix layouts. The manifest plus `readGEOSingleCell()` is the escape hatch for those, and the design notes are in the project’s `adr/0004-single-cell-architecture.md`.

## Where to go next

- Understanding GEO data formats — why supplementary files exist and what GEO does and doesn’t parse.
- From GEO to downstream analysis — the OSCA stack from a `SingleCellExperiment`.