singlet
Find, load, and analyze re-processed public single-cell RNA-seq studies with one line of Python.
Singlet re-processes public GEO single-cell RNA-seq studies through one
uniform pipeline and publishes each study as a .singlet file. You work with
two simple things: GEO accession strings (GSE… / GSM…) and .singlet
files. Everything loads as AnnData, ready
for scanpy or PyTorch. Browse the catalog at singlet.bio.
- Data: CC0 (public domain) — Code: MIT.
- Downloads need no login and no API key.
Install
pip install singlet
Wheels for Linux (x86_64, aarch64), macOS (Intel, Apple silicon) and Windows include the compiled .1pz / .s1
codec, so no compiler is needed. The development version installs from GitHub:
pip install "singlet @ git+https://github.com/Singlet-Bio/singlet"
Building from source compiles the package's .1pz codec, so you need a C++17 compiler
and zstd (headers + library): apt install build-essential libzstd-dev
on Debian/Ubuntu, brew install zstd on macOS, or
conda install -c conda-forge zstd. Linux, macOS and Windows are
supported (Windows needs Visual Studio Build Tools and zstd to build from source; wheels are built in CI).
Optional extras (same URL, extra in brackets):
pip install "singlet[analysis] @ git+https://github.com/Singlet-Bio/singlet" # matplotlib, scanpy, statsmodels, igraph, leidenalg
pip install "singlet[torch] @ git+https://github.com/Singlet-Bio/singlet" # PyTorch DataLoaders
pip install "singlet[zarr] @ git+https://github.com/Singlet-Bio/singlet" # Zarr export/import
pip install "singlet[mcp] @ git+https://github.com/Singlet-Bio/singlet" # local MCP server (singlet-mcp)
Quick start
import singlet
# Find studies in plain English → GSE accessions (live search)
studies = singlet.find("human PBMC 10x")
# Load a study → AnnData (free download, cached under ~/.singlet/cache)
adata = singlet.load("GSE138867")
# Load one sample: the parent study is downloaded, then filtered to it
pbmc1 = singlet.load("GSM4120733")
# Load and concatenate several studies into ONE AnnData
adata = singlet.load(["GSE138867", "GSE146974"])
# Per-cell labels come with every per-sample study (docs/cell_annotation.md section 8)
adata.obs[
["cell_type", "cell_type_ontology_term_id", "sex_chrom", "ancestry_hancestro", "called_ed"]
]
# ... and can select cells before anything else is downloaded
heart = singlet.cells("GSE286650", "cell_type == 'cardiac muscle cell' and sex_chrom == 'male'")
# Load a local .singlet file
adata = singlet.load("path/to/GSE138867.singlet")
# Search and load the top three matching studies in one step
adata = singlet.find_load("human pancreas islet cells")
load() accepts a single accession, a single .singlet path, or a list/tuple
mixing both. Lists are concatenated into one AnnData; obs["source"] and
obs["dataset"] record where each cell came from.
What you get back:
X: gene-level UMI counts (cells × genes, sparse), withlayers["spliced"]andlayers["unspliced"]when both are available.var_names: Ensembl gene ids;var["gene_name"]: gene symbols.obs:gsm_id, organism, protocol and other per-sample metadata.uns["study_meta"],uns["manifest"]: study metadata and the file's manifest (stored so thatwrite_h5ad/write_zarrwork).uns["skipped_samples"]: present only when some samples had an empty count matrix; those are skipped with a warning. A study with no usable sample raises an error pointing to its page on singlet.bio.
Pick genes by symbol or by Ensembl id:
adata = singlet.load("GSE138867", genes=["CD3E", "MS4A1", "ENSG00000105374"])
Symbols are matched exactly first, then case-insensitively; anything unmatched is reported in a warning.
Natural-language search
import singlet
singlet.find("exhausted T cells in melanoma") # study (GSE) accessions
singlet.find("mouse brain 10x", level="gsm", limit=20) # sample (GSM) accessions
# find + load in one call (top 3 studies by default; raise limit deliberately)
adata = singlet.find_load("human kidney organoids", limit=5)
Search runs against the hosted endpoint (https://singlet.bio/api; override
with $SINGLET_API_BASE). It is rate-limited per client; for heavier use,
create a key at https://singlet.bio/account and call singlet.set_api_key()
(or set $SINGLET_API_KEY).
Browse the catalog snapshot (offline)
import singlet
singlet.summary() # snapshot overview
singlet.samples(organism="Homo sapiens", status="SUCCESS")
singlet.tissues() # tissue breakdown
singlet.info("GSE138867") # metadata
These functions read a catalog snapshot bundled with the package, so they
work offline but lag the live catalog. singlet.info() falls back to the
live API (/api/gse/<GSE>, /api/gsm/<GSM>) for accessions the snapshot
does not know, and python -m singlet prints live totals from
https://singlet.bio/api/stats. Set SINGLET_OFFLINE=1 to disable those
live lookups.
Export
singlet.to_h5ad(adata, "study.h5ad")
singlet.to_zarr(adata, "study.zarr") # needs singlet[zarr]
singlet.to_mtx(adata, "study_mtx/") # 10x-style matrix.mtx.gz + barcodes + features
Loom export is not supported from Python.
Standard analysis (scanpy-compatible)
import scanpy as sc
adata = singlet.load("GSE138867")
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
The package also has built-in equivalents (singlet.normalize,
singlet.pca, singlet.neighbors, singlet.leiden, singlet.umap, ...)
that do not need scanpy.
PyTorch training
from singlet.torch import SingletDataset, DataLoader
# An array of .singlet files, concatenated into one training set
loader = DataLoader(
["a.singlet", "b.singlet", "c.singlet"],
batch_size=512,
device="cuda",
normalize=True,
)
# A single dataset / accession / AnnData also works
ds = SingletDataset("GSE138867", normalize=True)
MCP servers
- Hosted:
https://singlet.bio/mcpserves the live catalog to MCP clients; nothing to install. - Local:
singlet-mcp(installsinglet[mcp]) runs 12 tools over the offline catalog snapshot plus live search, over stdio. The old command namesingletstill works but is deprecated.
Configuration
| Variable | Meaning |
|---|---|
SINGLET_DATA_BASE |
Bundle host: files live at <base>/data/<GSE>/<GSE>.singlet (default https://data.singlet.bio; a trailing /data is accepted, so the R client's value works too) |
SINGLET_SAMPLE_MIRROR |
Self-hosted mirror of per-sample directories for singlet.fetch() (no public default) |
SINGLET_API_BASE |
REST API base (default https://singlet.bio/api) |
SINGLET_CACHE_DIR |
Download cache (default ~/.singlet/cache) |
SINGLET_API_KEY |
Optional key for natural-language search |
SINGLET_OFFLINE |
1 disables the live lookups in info() and python -m singlet |
Links
- Homepage: https://singlet.bio
- Documentation: https://singlet.bio/docs
- Source: https://github.com/Singlet-Bio/singlet
License
Code: MIT. Catalog data: CC0 (public domain).
1 maintainer
Metadata
1 maintainer
Release files for singlet 1.3.3
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