Mouse myeloid lineage differentiation

Name	Description	Type	Data type	Size
obs
`cell_type`	Classification of the cell type based on its characteristics and function within the tissue or organism.	vector	category	660
`size_factors`	The size factors created by the normalisation method, if any.	vector	float32	660
var
`feature_name`	A human-readable name for the feature, usually a gene symbol.	vector	object	116918
`hvg`	Whether or not the feature is considered to be a 'highly variable gene'	vector	bool	116918
`hvg_score`	A ranking of the features by hvg.	vector	float64	116918
obsp
`knn_connectivities`	K nearest neighbors connectivities matrix.	sparsematrix	float32	660 × 660
`knn_distances`	K nearest neighbors distance matrix.	sparsematrix	float64	660 × 660
obsm
`X_pca`	The resulting PCA embedding.	densematrix	float32	660 × 50
varm
`pca_loadings`	The PCA loadings matrix.	densematrix	float64	116918 × 50
layers
`counts`	Raw counts	sparsematrix	float32	660 × 116918
`normalized`	Normalised expression values	sparsematrix	float32	660 × 116918
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 117.4 MiB

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

Used in

Dimensionality Reduction for Visualisation

References

Olsson, A., Venkatasubramanian, M., Chaudhri, V. K., Aronow, B. J., Salomonis, N., Singh, H., & Grimes, H. L. (2016). Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature, 537(7622), 698–702. 10.1038/nature19348 ↗