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import os
import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class BrcaTcga(InMemoryDataset):
r"""The breast cancer (BRCA TCGA Pan-Cancer Atlas) dataset consisting of
patients with survival information and gene expression data from
`cBioPortal <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160307/>`_
and a network of biological interactions between those nodes from
`Pathway Commons <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145667/>`_.
The dataset contains the gene features of 1,082 patients, and the overall
survival time (in months) of each patient as label.
Pre-processing and example model codes on how to use this dataset can be
found `here <https://github.com/cannin/pyg_pathway_commons_cbioportal>`_.
Args:
root (str): Root directory where the dataset should be saved.
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
* - 1,082
- 9,288
- 271,771
- 1,082
"""
url = 'https://zenodo.org/record/8251328/files/brca_tcga.zip?download=1'
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return ['graph_idx.csv', 'graph_labels.csv', 'edge_index.pt']
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.unlink(path)
fs.rm(self.raw_dir)
os.rename(osp.join(self.root, 'brca_tcga'), self.raw_dir)
def process(self) -> None:
import pandas as pd
graph_feat = pd.read_csv(self.raw_paths[0], index_col=0).values
graph_feat = torch.from_numpy(graph_feat).to(torch.float)
graph_labels = np.loadtxt(self.raw_paths[1], delimiter=',')
graph_label = torch.from_numpy(graph_labels).to(torch.float)
edge_index = fs.torch_load(self.raw_paths[2])
data_list = []
for x, y in zip(graph_feat, graph_label):
data = Data(x=x.view(-1, 1), edge_index=edge_index, y=y)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
self.save(data_list, self.processed_paths[0])
|
