torch-adata 0.0.1

torch-adata

torch-adata

Create pytorch Datasets from AnnData

Example use of the base class

The base class, AnnDataset is a subclass of the widely-used torch.utils.data.Dataset. The outputs of all AnnDataset classes and subclasses are designed to be directly compatible with the torch.utils.data.DataLoader module.

import anndata as a
import torch_adata as ta

adata = a.read_h5ad("/path/to/data.h5ad")
dataset = ta.AnnDataset(adata)

Returns data (X as a torch.Tensor) and the pandas.DataFrame; adata.obs.

# create a dummy index
idx = np.random.choice(range(dataset.__len__()), 5)
X, obs = dataset.__getitem__(idx)

Specialized classes

GroupedAnnDataset

A subclass of the base class, AnnDataset.

import anndata as a
import torch_adata as ta

adata = a.read_h5ad("/path/to/data.h5ad")
dataset = ta.GroupedAnnDataset(adata, groupby="batch")

Returns data as a dictionary of data with values as torch.Tensor and keys as each groupby category and the sampled adata.obs is again returned as a pandas.DataFrame.

# create a dummy index
idx = np.random.choice(range(dataset.__len__()), 5)
X_dict, obs = dataset.__getitem__(idx)

TimeResolvedAnnDataset

A subclass of the class, GroupedAnnDataset.

import anndata as a
import torch_adata as ta

adata = a.read_h5ad("/path/to/data.h5ad")
dataset = ta.TimeResolvedAnnDataset(adata, time_key="Time point")

Returns the initial datapoint, X0 as a torch.Tensor, the entire sample of the dataset as a dictionary of data with values as torch.Tensor and keys as each timepoint indicated by the time_key. Sampled adata.obs is again returned as a pandas.DataFrame.

# create a dummy index
idx = np.random.choice(range(dataset.__len__()), 5)
X0, X_dict, t, obs = dataset.__getitem__(idx)

Installation

Install from PYPI:

pip install torch-adata

Install the developer version:

git clone https://github.com/mvinyard/torch-adata.git; cd torch-adata;
pip install -e .