Slide-seqV2 - Mouse Cortex

Info

zenodo_spatial/slideseqv2/mouse_cortex
Stickels et al. (2020)
22.42 MiB
23-09-2024
17797 cells × 127 genes

Quick links

Source

Used in

• Spatially Variable Genes
  

DATASET ID

zenodo_spatial/slideseqv2/mouse_cortex

REFERENCE

Stickels et al. (2020)

SIZE

22.42 MiB

CREATED

23-09-2024

DIMENSIONS

17797 × 127

Description

Gene expression library of Mouse cortex profiled using Slide-seq V2.

Preview

dataset is an AnnData object with n_obs × n_vars = 17797 × 127 with slots:

• var: feature_name
  
• layers: counts
  
• uns: dataset_description, dataset_id, dataset_name, dataset_organism, dataset_reference, dataset_summary, dataset_url

Reference

Name	Description	Type	Data type	Size
var
feature_name	A human-readable name for the feature, usually a gene symbol.	vector	object	127
layers
counts	Raw counts	sparsematrix	int64	17797 × 127
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

Slot crossref data

dataset.layers['counts']

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

Type: sparsematrix, data type: int64, shape: 17797 × 127

Raw counts

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.var['feature_name']

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

Type: vector, data type: object, shape: 127

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

References

Stickels, Robert R., Evan Murray, Pawan Kumar, Jilong Li, Jamie L. Marshall, Daniela J. Di Bella, Paola Arlotta, Evan Z. Macosko, and Fei Chen. 2020. “Highly Sensitive Spatial Transcriptomics at Near-Cellular Resolution with Slide-seqV2.” Nature Biotechnology 39 (3): 313–19. https://doi.org/10.1038/s41587-020-0739-1.