Info
openproblems_
Cao et al. (2018)
205.41 MiB
02-02-2024
4739 × 60550
Quick links
Used in
No related benchmarks found.
sci-CAR profiles of 5k cell line cells (HEK293T, NIH/3T3, A549) across three treatment conditions (DEX 0h, 1h and 3h)
CREATED
02-02-2024
DIMENSIONS
4739 × 60550
Single cell RNA-seq and ATAC-seq co-profiling for HEK293T cells, NIH/3T3 cells, A549 cells across three treatment conditions (DEX 0 hour, 1 hour and 3 hour treatment).
dataset_mod1
is an AnnData object with n_obs × n_vars = 4739 × 60550 with slots:
cell_type
, size_factors
feature_name
, hvg
, hvg_score
X_svd
counts
, normalized
dataset_description
, dataset_id
, dataset_name
, dataset_organism
, dataset_reference
, dataset_summary
, dataset_url
, normalization_id
dataset_mod2
is an AnnData object with n_obs × n_vars = 4739 × 146713 with slots:
cell_type
, size_factors
feature_name
, hvg
, hvg_score
X_svd
counts
, normalized
dataset_description
, dataset_id
, dataset_name
, dataset_organism
, dataset_reference
, dataset_summary
, dataset_url
, normalization_id
Name | Description | Type | Data type | Size |
---|---|---|---|---|
obs | ||||
cell_
|
Classification of the cell type based on its characteristics and function within the tissue or organism. |
vector
|
category
|
4739 |
size_
|
The size factors created by the normalisation method, if any. |
vector
|
float32
|
4739 |
var | ||||
feature_
|
A human-readable name for the feature, usually a gene symbol. |
vector
|
object
|
60550 |
hvg
|
Whether or not the feature is considered to be a ‘highly variable gene’ |
vector
|
bool
|
60550 |
hvg_
|
A ranking of the features by hvg. |
vector
|
float64
|
60550 |
obsm | ||||
X_
|
The resulting SVD embedding. |
densematrix
|
float32
|
4739 × 100 |
layers | ||||
counts
|
Raw counts |
sparsematrix
|
float32
|
4739 × 60550 |
normalized
|
Normalised expression values |
sparsematrix
|
float32
|
4739 × 60550 |
uns | ||||
dataset_
|
Long description of the dataset. |
atomic
|
str
|
1 |
dataset_
|
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_
|
A human-readable name for the dataset. |
atomic
|
str
|
1 |
dataset_
|
The organism of the sample in the dataset. |
atomic
|
str
|
1 |
dataset_
|
Bibtex reference of the paper in which the dataset was published. |
atomic
|
str
|
1 |
dataset_
|
Short description of the dataset. |
atomic
|
str
|
1 |
dataset_
|
Link to the original source of the dataset. |
atomic
|
str
|
1 |
normalization_
|
Which normalization was used |
atomic
|
str
|
1 |
Name | Description | Type | Data type | Size |
---|---|---|---|---|
obs | ||||
cell_
|
Classification of the cell type based on its characteristics and function within the tissue or organism. |
vector
|
category
|
4739 |
size_
|
The size factors created by the normalisation method, if any. |
vector
|
float64
|
4739 |
var | ||||
feature_
|
A human-readable name for the feature, usually a gene symbol. |
vector
|
object
|
146713 |
hvg
|
Whether or not the feature is considered to be a ‘highly variable gene’ |
vector
|
bool
|
146713 |
hvg_
|
A ranking of the features by hvg. |
vector
|
float64
|
146713 |
obsm | ||||
X_
|
The resulting SVD embedding. |
densematrix
|
float64
|
4739 × 100 |
layers | ||||
counts
|
Raw counts |
sparsematrix
|
float64
|
4739 × 146713 |
normalized
|
Normalised expression values |
sparsematrix
|
float64
|
4739 × 146713 |
uns | ||||
dataset_
|
Long description of the dataset. |
atomic
|
str
|
1 |
dataset_
|
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_
|
A human-readable name for the dataset. |
atomic
|
str
|
1 |
dataset_
|
The organism of the sample in the dataset. |
atomic
|
str
|
1 |
dataset_
|
Bibtex reference of the paper in which the dataset was published. |
atomic
|
str
|
1 |
dataset_
|
Short description of the dataset. |
atomic
|
str
|
1 |
dataset_
|
Link to the original source of the dataset. |
atomic
|
str
|
1 |
normalization_
|
Which normalization was used |
atomic
|
str
|
1 |
dataset_mod1.layers['counts']
In R: dataset_mod1$layers[["counts"]]
Type: sparsematrix
, data type: float32
, shape: 4739 × 60550
Raw counts
dataset_mod1.layers['normalized']
In R: dataset_mod1$layers[["normalized"]]
Type: sparsematrix
, data type: float32
, shape: 4739 × 60550
Normalised expression values
dataset_mod1.obs['cell_type']
In R: dataset_mod1$obs[["cell_type"]]
Type: vector
, data type: category
, shape: 4739
Classification of the cell type based on its characteristics and function within the tissue or organism.
dataset_mod1.obs['size_factors']
In R: dataset_mod1$obs[["size_factors"]]
Type: vector
, data type: float32
, shape: 4739
The size factors created by the normalisation method, if any.
dataset_mod1.obsm['X_svd']
In R: dataset_mod1$obsm[["X_svd"]]
Type: densematrix
, data type: float32
, shape: 4739 × 100
The resulting SVD embedding.
dataset_mod1.uns['dataset_description']
In R: dataset_mod1$uns[["dataset_description"]]
Type: atomic
, data type: str
, shape: 1
Long description of the dataset.
dataset_mod1.uns['dataset_id']
In R: dataset_mod1$uns[["dataset_id"]]
Type: atomic
, data type: str
, shape: 1
A unique identifier for the dataset. This is different from the obs.dataset_id
field, which is the identifier for the dataset from which the cell data is derived.
dataset_mod1.uns['dataset_name']
In R: dataset_mod1$uns[["dataset_name"]]
Type: atomic
, data type: str
, shape: 1
A human-readable name for the dataset.
dataset_mod1.uns['dataset_organism']
In R: dataset_mod1$uns[["dataset_organism"]]
Type: atomic
, data type: str
, shape: 1
The organism of the sample in the dataset.
dataset_mod1.uns['dataset_reference']
In R: dataset_mod1$uns[["dataset_reference"]]
Type: atomic
, data type: str
, shape: 1
Bibtex reference of the paper in which the dataset was published.
dataset_mod1.uns['dataset_summary']
In R: dataset_mod1$uns[["dataset_summary"]]
Type: atomic
, data type: str
, shape: 1
Short description of the dataset.
dataset_mod1.uns['dataset_url']
In R: dataset_mod1$uns[["dataset_url"]]
Type: atomic
, data type: str
, shape: 1
Link to the original source of the dataset.
dataset_mod1.uns['normalization_id']
In R: dataset_mod1$uns[["normalization_id"]]
Type: atomic
, data type: str
, shape: 1
Which normalization was used
dataset_mod1.var['feature_name']
In R: dataset_mod1$var[["feature_name"]]
Type: vector
, data type: object
, shape: 60550
A human-readable name for the feature, usually a gene symbol.
dataset_mod1.var['hvg']
In R: dataset_mod1$var[["hvg"]]
Type: vector
, data type: bool
, shape: 60550
Whether or not the feature is considered to be a ‘highly variable gene’
dataset_mod1.var['hvg_score']
In R: dataset_mod1$var[["hvg_score"]]
Type: vector
, data type: float64
, shape: 60550
A ranking of the features by hvg.
dataset_mod2.layers['counts']
In R: dataset_mod2$layers[["counts"]]
Type: sparsematrix
, data type: float64
, shape: 4739 × 146713
Raw counts
dataset_mod2.layers['normalized']
In R: dataset_mod2$layers[["normalized"]]
Type: sparsematrix
, data type: float64
, shape: 4739 × 146713
Normalised expression values
dataset_mod2.obs['cell_type']
In R: dataset_mod2$obs[["cell_type"]]
Type: vector
, data type: category
, shape: 4739
Classification of the cell type based on its characteristics and function within the tissue or organism.
dataset_mod2.obs['size_factors']
In R: dataset_mod2$obs[["size_factors"]]
Type: vector
, data type: float64
, shape: 4739
The size factors created by the normalisation method, if any.
dataset_mod2.obsm['X_svd']
In R: dataset_mod2$obsm[["X_svd"]]
Type: densematrix
, data type: float64
, shape: 4739 × 100
The resulting SVD embedding.
dataset_mod2.uns['dataset_description']
In R: dataset_mod2$uns[["dataset_description"]]
Type: atomic
, data type: str
, shape: 1
Long description of the dataset.
dataset_mod2.uns['dataset_id']
In R: dataset_mod2$uns[["dataset_id"]]
Type: atomic
, data type: str
, shape: 1
A unique identifier for the dataset. This is different from the obs.dataset_id
field, which is the identifier for the dataset from which the cell data is derived.
dataset_mod2.uns['dataset_name']
In R: dataset_mod2$uns[["dataset_name"]]
Type: atomic
, data type: str
, shape: 1
A human-readable name for the dataset.
dataset_mod2.uns['dataset_organism']
In R: dataset_mod2$uns[["dataset_organism"]]
Type: atomic
, data type: str
, shape: 1
The organism of the sample in the dataset.
dataset_mod2.uns['dataset_reference']
In R: dataset_mod2$uns[["dataset_reference"]]
Type: atomic
, data type: str
, shape: 1
Bibtex reference of the paper in which the dataset was published.
dataset_mod2.uns['dataset_summary']
In R: dataset_mod2$uns[["dataset_summary"]]
Type: atomic
, data type: str
, shape: 1
Short description of the dataset.
dataset_mod2.uns['dataset_url']
In R: dataset_mod2$uns[["dataset_url"]]
Type: atomic
, data type: str
, shape: 1
Link to the original source of the dataset.
dataset_mod2.uns['normalization_id']
In R: dataset_mod2$uns[["normalization_id"]]
Type: atomic
, data type: str
, shape: 1
Which normalization was used
dataset_mod2.var['feature_name']
In R: dataset_mod2$var[["feature_name"]]
Type: vector
, data type: object
, shape: 146713
A human-readable name for the feature, usually a gene symbol.
dataset_mod2.var['hvg']
In R: dataset_mod2$var[["hvg"]]
Type: vector
, data type: bool
, shape: 146713
Whether or not the feature is considered to be a ‘highly variable gene’
dataset_mod2.var['hvg_score']
In R: dataset_mod2$var[["hvg_score"]]
Type: vector
, data type: float64
, shape: 146713
A ranking of the features by hvg.