DBiT-seq - Mouse Eye (E10)

Info

zenodo_spatial/dbitseq/mouse_e10_eye
Liu et al. (2020)
22.62 MiB
23-09-2024
2500 cells × 12130 genes

Quick links

Source

Used in

• Spatially Variable Genes
  

DATASET ID

zenodo_spatial/dbitseq/mouse_e10_eye

REFERENCE

Liu et al. (2020)

SIZE

22.62 MiB

CREATED

23-09-2024

DIMENSIONS

2500 × 12130

Description

Gene expression library of an E10 whole mouse embryo tissue (eye in early-stage organogenesis) profiled using DBiT-seq.

Preview

dataset is an AnnData object with n_obs × n_vars = 2500 × 12130 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	12130
layers
counts	Raw counts	densematrix	float32	2500 × 12130
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: densematrix, data type: float32, shape: 2500 × 12130

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: 12130

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

References

Liu, Yang, Mingyu Yang, Yanxiang Deng, Graham Su, Archibald Enninful, Cindy C. Guo, Toma Tebaldi, et al. 2020. “High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue.” Cell 183 (6): 1665–1681.e18. https://doi.org/10.1016/j.cell.2020.10.026.