Mouse HSPC

Info

openproblems_v1/mouse_hspc_nestorowa2016
Nestorowa et al. (2016)
377.81 MiB
02-02-2024
1920 cells × 43258 genes

Quick links

Source

Used in

• Dimensionality reduction for visualisation
  

DATASET ID

openproblems_v1/mouse_hspc_nestorowa2016

REFERENCE

Nestorowa et al. (2016)

SIZE

377.81 MiB

CREATED

02-02-2024

DIMENSIONS

1920 × 43258

Description

1656 hematopoietic stem and progenitor cells from mouse bone marrow. Sequenced by Smart-seq2.

Preview

dataset is an AnnData object with n_obs × n_vars = 1920 × 43258 with slots:

• obs: cell_type, size_factors
  
• 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

Reference

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	1920
size_factors	The size factors created by the normalisation method, if any.	vector	float32	1920
var
feature_name	A human-readable name for the feature, usually a gene symbol.	vector	object	43258
hvg	Whether or not the feature is considered to be a ‘highly variable gene’	vector	bool	43258
hvg_score	A ranking of the features by hvg.	vector	float64	43258
obsp
knn_connectivities	K nearest neighbors connectivities matrix.	sparsematrix	float32	1920 × 1920
knn_distances	K nearest neighbors distance matrix.	sparsematrix	float64	1920 × 1920
obsm
X_pca	The resulting PCA embedding.	densematrix	float32	1920 × 50
varm
pca_loadings	The PCA loadings matrix.	densematrix	float64	43258 × 50
layers
counts	Raw counts	sparsematrix	float32	1920 × 43258
normalized	Normalised expression values	sparsematrix	float32	1920 × 43258
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

Slot crossref data

dataset.layers['counts']

In R: dataset$layers[["counts"]]

Type: sparsematrix, data type: float32, shape: 1920 × 43258

Raw counts

dataset.layers['normalized']

In R: dataset$layers[["normalized"]]

Type: sparsematrix, data type: float32, shape: 1920 × 43258

Normalised expression values

dataset.obs['cell_type']

In R: dataset$obs[["cell_type"]]

Type: vector, data type: category, shape: 1920

Classification of the cell type based on its characteristics and function within the tissue or organism.

dataset.obs['size_factors']

In R: dataset$obs[["size_factors"]]

Type: vector, data type: float32, shape: 1920

The size factors created by the normalisation method, if any.

dataset.obsm['X_pca']

In R: dataset$obsm[["X_pca"]]

Type: densematrix, data type: float32, shape: 1920 × 50

The resulting PCA embedding.

dataset.obsp['knn_connectivities']

In R: dataset$obsp[["knn_connectivities"]]

Type: sparsematrix, data type: float32, shape: 1920 × 1920

K nearest neighbors connectivities matrix.

dataset.obsp['knn_distances']

In R: dataset$obsp[["knn_distances"]]

Type: sparsematrix, data type: float64, shape: 1920 × 1920

K nearest neighbors distance matrix.

dataset.uns['dataset_description']

In R: dataset$uns[["dataset_description"]]

Type: atomic, data type: str, shape: 1

Long description of the dataset.

dataset.uns['dataset_id']

In R: dataset$uns[["dataset_id"]]

Type: atomic, data type: str, shape: 1

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.

dataset.uns['dataset_name']

In R: dataset$uns[["dataset_name"]]

Type: atomic, data type: str, shape: 1

A human-readable name for the dataset.

dataset.uns['dataset_organism']

In R: dataset$uns[["dataset_organism"]]

Type: atomic, data type: str, shape: 1

The organism of the sample in the dataset.

dataset.uns['dataset_reference']

In R: dataset$uns[["dataset_reference"]]

Type: atomic, data type: str, shape: 1

Bibtex reference of the paper in which the dataset was published.

dataset.uns['dataset_summary']

In R: dataset$uns[["dataset_summary"]]

Type: atomic, data type: str, shape: 1

Short description of the dataset.

dataset.uns['dataset_url']

In R: dataset$uns[["dataset_url"]]

Type: atomic, data type: str, shape: 1

Link to the original source of the dataset.

dataset.uns['knn']

In R: dataset$uns[["knn"]]

Type: dict, data type: ``, shape: 3

Supplementary K nearest neighbors data.

dataset.uns['normalization_id']

In R: dataset$uns[["normalization_id"]]

Type: atomic, data type: str, shape: 1

Which normalization was used

dataset.uns['pca_variance']

In R: dataset$uns[["pca_variance"]]

Type: dict, data type: ``, shape: 2

The PCA variance objects.

dataset.var['feature_name']

In R: dataset$var[["feature_name"]]

Type: vector, data type: object, shape: 43258

A human-readable name for the feature, usually a gene symbol.

dataset.var['hvg']

In R: dataset$var[["hvg"]]

Type: vector, data type: bool, shape: 43258

Whether or not the feature is considered to be a ‘highly variable gene’

dataset.var['hvg_score']

In R: dataset$var[["hvg_score"]]

Type: vector, data type: float64, shape: 43258

A ranking of the features by hvg.

dataset.varm['pca_loadings']

In R: dataset$varm[["pca_loadings"]]

Type: densematrix, data type: float64, shape: 43258 × 50

The PCA loadings matrix.

References

Nestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold Göttgens. 2016. “A Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.” Blood 128 (8): e20–31. https://doi.org/10.1182/blood-2016-05-716480.