Breast cancer IMC dataset for InterSTELLAR training

<p>&nbsp;<strong>graph_data.npy</strong>: preprocessed&nbsp;Breast cancer IMC dataset for InterSTELLAR training, saved as a<strong> .npy</strong> file.</p> <ol> <li>Read this file with python and get a list named <strong>all_graph</strong>.&nbsp; This list includes <strong>368&nbsp;</strong>elements. The first&nbsp;<strong>366 </strong>elements are the information from each tissue, the last two elements are the <strong>mean </strong>and<strong> standard deviation</strong> of the cell feature data&nbsp;after log-transformation with shape <strong>30x1</strong>.</li> <li>Each sub-element&nbsp;<strong>all_graph[i]</strong> is alone a new list with three elements, including <strong>cell feature matrix all_graph[i][0]</strong>,<strong> cell locations&nbsp;all_graph[i][1],</strong> <strong>tissue labels&nbsp;all_graph[i][2] </strong>and <strong>cell phenotypes</strong> <strong>all_graph[i][3]</strong>.</li> <li><strong>all_graph[i][0]</strong> is a <strong>Nx30</strong> matrix, corresponding to<strong> 30 cell markers</strong>;&nbsp; <strong>all_graph[i][1]</strong> is a<strong> Nx2</strong> matrix, corresponding to <strong>x </strong>and <strong>y </strong>locations of a single cell;&nbsp;<strong> all_graph[i][2]</strong> is a list with<strong> 5</strong> elements, corresponding to <strong>tissue phenotypes</strong> (0: healthy, 1: TNBC, 2: Non-TNBC cancers), <strong>tissue area</strong>&nbsp;(um²),<strong> tumor grade</strong>, <strong>overall survival time </strong>(month) and&nbsp;&nbsp;<strong>patient status</strong> (alive or death);&nbsp; <strong>all_graph[i][3] </strong>is a list with <strong>N </strong>elements corresponding to the <strong>cell phenotypes</strong>.</li> </ol> <p><strong>cell_maskes.npy</strong>: the cell segmentation maskes corresponding to the 366 tissues.</p>