Pancreas (inDrop)

Human pancreatic islet scRNA-seq data from 6 datasets across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, and SMARTER-seq). Here we just use the inDrop1 batch, which includes1937 cells × 15502 genes.

reference Luecken et al., 2021

homo sapiens Organism

16,382 × 18,771 Dimensions

1.26 GiB Size

log_cp10k Normalization

Source

Description

Preview

An AnnData object with n_obs × n_vars = 16,382 × 18,771 with slots:

obs: size_factors , cell_type , batch

var: feature_name , hvg , hvg_score

obsp: knn_connectivities , knn_distances

obsm: X_pca

varm: pca_loadings

layers: counts , normalized

uns: dataset_description , dataset_id , dataset_name , dataset_organism , dataset_reference , dataset_summary , dataset_url , knn , normalization_id , pca_variance

Data structure

Name	Description	Type	Data type	Size
obs
`size_factors`	The size factors created by the normalisation method, if any.	vector	float32	16382
`cell_type`	Classification of the cell type based on its characteristics and function within the tissue or organism.	vector	category	16382
`batch`	A batch identifier. This label is very context-dependent and may be a combination of the tissue, assay, donor, etc.	vector	category	16382
var
`feature_name`	A human-readable name for the feature, usually a gene symbol.	vector	object	18771
`hvg`	Whether or not the feature is considered to be a 'highly variable gene'	vector	bool	18771
`hvg_score`	A ranking of the features by hvg.	vector	float64	18771
obsp
`knn_connectivities`	K nearest neighbors connectivities matrix.	sparsematrix	float32	16382 × 16382
`knn_distances`	K nearest neighbors distance matrix.	sparsematrix	float64	16382 × 16382
obsm
`X_pca`	The resulting PCA embedding.	densematrix	float32	16382 × 50
varm
`pca_loadings`	The PCA loadings matrix.	densematrix	float64	18771 × 50
layers
`counts`	Raw counts	sparsematrix	float32	16382 × 18771
`normalized`	Normalised expression values	sparsematrix	float32	16382 × 18771
uns
`dataset_description`	Long description of the dataset.	atomic	str	1
`dataset_id`	A unique identifier for the dataset. This is different from the `obs.dataset_id` field, which is the identifier for the dataset from which the cell data is derived.	atomic	str	1
`dataset_name`	A human-readable name for the dataset.	atomic	str	1
`dataset_organism`	The organism of the sample in the dataset.	atomic	str	1
`dataset_reference`	Bibtex reference of the paper in which the dataset was published.	atomic	str	1
`dataset_summary`	Short description of the dataset.	atomic	str	1
`dataset_url`	Link to the original source of the dataset.	atomic	str	1
`knn`	Supplementary K nearest neighbors data.	dict		3
`normalization_id`	Which normalization was used	atomic	str	1
`pca_variance`	The PCA variance objects.	dict		2

Download & explore

The processed dataset lives on the OpenProblems public S3 bucket, which is world-readable. Download the file directly, or copy its S3 URI to fetch it with your tool of choice.

dataset.h5ad 1.26 GiB

Download

s3://openproblems-data/resources/datasets/openproblems_v1/pancreas/log_cp10k/dataset.h5ad

Used in

Denoising

References

Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., & Theis, F. J. (2021). Benchmarking atlas-level data integration in single-cell genomics. Nature Methods, 19(1), 41–50. 10.1038/s41592-021-01336-8 ↗