Info
openproblems_
Olsson et al. (2016)
117.4 MiB
02-02-2024
660 × 116918
Myeloid lineage differentiation from mouse blood
openproblems_
Olsson et al. (2016)
117.4 MiB
02-02-2024
660 × 116918
DATASET ID
openproblems_v1/mouse_blood_olsson_labelled
REFERENCE
Olsson et al. (2016)
SIZE
117.4 MiB
CREATED
02-02-2024
DIMENSIONS
660 × 116918
660 FACS-isolated myeloid cells from 9 experiments sequenced using C1 Fluidigm and SMARTseq in 2016 by Olsson et al.
dataset
is an AnnData object with n_obs × n_vars = 660 × 116918 with slots:
cell_type
, size_factors
feature_name
, hvg
, hvg_score
knn_connectivities
, knn_distances
X_pca
pca_loadings
counts
, normalized
dataset_description
, dataset_id
, dataset_name
, dataset_organism
, dataset_reference
, dataset_summary
, dataset_url
, knn
, normalization_id
, pca_variance
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
|
660 |
size_
|
The size factors created by the normalisation method, if any. |
vector
|
float32
|
660 |
var | ||||
feature_
|
A human-readable name for the feature, usually a gene symbol. |
vector
|
object
|
116918 |
hvg
|
Whether or not the feature is considered to be a ‘highly variable gene’ |
vector
|
bool
|
116918 |
hvg_
|
A ranking of the features by hvg. |
vector
|
float64
|
116918 |
obsp | ||||
knn_
|
K nearest neighbors connectivities matrix. |
sparsematrix
|
float32
|
660 × 660 |
knn_
|
K nearest neighbors distance matrix. |
sparsematrix
|
float64
|
660 × 660 |
obsm | ||||
X_
|
The resulting PCA embedding. |
densematrix
|
float32
|
660 × 50 |
varm | ||||
pca_
|
The PCA loadings matrix. |
densematrix
|
float64
|
116918 × 50 |
layers | ||||
counts
|
Raw counts |
sparsematrix
|
float32
|
660 × 116918 |
normalized
|
Normalised expression values |
sparsematrix
|
float32
|
660 × 116918 |
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 |
knn
|
Supplementary K nearest neighbors data. |
dict
|
3 | |
normalization_
|
Which normalization was used |
atomic
|
str
|
1 |
pca_
|
The PCA variance objects. |
dict
|
2 |
dataset.layers['counts']
In R: dataset$layers[["counts"]]
Type: sparsematrix
, data type: float32
, shape: 660 × 116918
Raw counts
dataset.layers['normalized']
In R: dataset$layers[["normalized"]]
Type: sparsematrix
, data type: float32
, shape: 660 × 116918
Normalised expression values
dataset.obs['cell_type']
In R: dataset$obs[["cell_type"]]
Type: vector
, data type: category
, shape: 660
Classification of the cell type based on its characteristics and function within the tissue or organism.
dataset.obs['size_factors']
In R: dataset$obs[["size_factors"]]
Type: vector
, data type: float32
, shape: 660
The size factors created by the normalisation method, if any.
dataset.obsm['X_pca']
In R: dataset$obsm[["X_pca"]]
Type: densematrix
, data type: float32
, shape: 660 × 50
The resulting PCA embedding.
dataset.obsp['knn_connectivities']
In R: dataset$obsp[["knn_connectivities"]]
Type: sparsematrix
, data type: float32
, shape: 660 × 660
K nearest neighbors connectivities matrix.
dataset.obsp['knn_distances']
In R: dataset$obsp[["knn_distances"]]
Type: sparsematrix
, data type: float64
, shape: 660 × 660
K nearest neighbors distance matrix.
dataset.uns['dataset_description']
In R: dataset$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: 116918
A human-readable name for the feature, usually a gene symbol.
dataset.var['hvg']
In R: dataset$var[["hvg"]]
Type: vector
, data type: bool
, shape: 116918
Whether or not the feature is considered to be a ‘highly variable gene’
dataset.var['hvg_score']
In R: dataset$var[["hvg_score"]]
Type: vector
, data type: float64
, shape: 116918
A ranking of the features by hvg.
dataset.varm['pca_loadings']
In R: dataset$varm[["pca_loadings"]]
Type: densematrix
, data type: float64
, shape: 116918 × 50
The PCA loadings matrix.