Mouse HSPC – Open Problems in Single Cell Analysis

Info

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

Quick links

Source

Used in

Dimensionality reduction for visualisation

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.