5k PBMCs

Info

openproblems_v1/tenx_5k_pbmc
10x Genomics (2019)
156.82 MiB
02-02-2024
5247 × 20822

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Source

Used in

• Dimensionality reduction for visualisation
  

DATASET ID

openproblems_v1/tenx_5k_pbmc

REFERENCE

10x Genomics (2019)

SIZE

156.82 MiB

CREATED

02-02-2024

DIMENSIONS

5247 × 20822

Description

5k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. Sequenced on 10X v3 chemistry in July 2019 by 10X Genomics.

Preview

dataset is an AnnData object with n_obs × n_vars = 5247 × 20822 with slots:

• obs: 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
size_factors	The size factors created by the normalisation method, if any.	vector	float32	5247
var
feature_name	A human-readable name for the feature, usually a gene symbol.	vector	object	20822
hvg	Whether or not the feature is considered to be a ‘highly variable gene’	vector	bool	20822
hvg_score	A ranking of the features by hvg.	vector	float64	20822
obsp
knn_connectivities	K nearest neighbors connectivities matrix.	sparsematrix	float32	5247 × 5247
knn_distances	K nearest neighbors distance matrix.	sparsematrix	float64	5247 × 5247
obsm
X_pca	The resulting PCA embedding.	densematrix	float32	5247 × 50
varm
pca_loadings	The PCA loadings matrix.	densematrix	float64	20822 × 50
layers
counts	Raw counts	sparsematrix	float32	5247 × 20822
normalized	Normalised expression values	sparsematrix	float32	5247 × 20822
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: 5247 × 20822

Raw counts

dataset.layers['normalized']

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

Type: sparsematrix, data type: float32, shape: 5247 × 20822

Normalised expression values

dataset.obs['size_factors']

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

Type: vector, data type: float32, shape: 5247

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: 5247 × 50

The resulting PCA embedding.

dataset.obsp['knn_connectivities']

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

Type: sparsematrix, data type: float32, shape: 5247 × 5247

K nearest neighbors connectivities matrix.

dataset.obsp['knn_distances']

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

Type: sparsematrix, data type: float64, shape: 5247 × 5247

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: 20822

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: 20822

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: 20822

A ranking of the features by hvg.

dataset.varm['pca_loadings']

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

Type: densematrix, data type: float64, shape: 20822 × 50

The PCA loadings matrix.

References

10x Genomics. 2019. “5k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor with a Panel of TotalSeq-b Antibodies (V3 Chemistry).” https://www.10xgenomics.com/resources/datasets/5-k-peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-with-cell-surface-proteins-v-3-chemistry-3-1-standard-3-1-0.