Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data: supplementary MSI data and analysis implementation of paper

Datacite citation style:
Lelieveldt, Boudewijn (2016): Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data: supplementary MSI data and analysis implementation of paper. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:827a63b1-0c33-464a-a61e-ba236f0302c4
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Mass spectrometry imaging provides untargeted spatiomolecular information necessary to uncover molecular intratumor heterogeneity. The challenge has been to identify those tumor subpopulations that drive patient outcomes within the highly complex datasets (hyperdimensional data, intratumor heterogeneity, and patient variation). Here we report an automatic, unbiased pipeline to nonlinearly map the hyperdimensional data into a 3D space, and identify molecularly distinct, clinically relevant tumor subpopulations. We demonstrate this pipeline’s ability to uncover subpopulations statistically associated with patient survival in primary tumors of gastric cancer and with metastasis in primary tumors of breast cancer.
history
  • 2016-10-03 first online, published, posted
publisher
TU Delft
format
media types: application/octet-stream, application/zip, image/png, text/html, text/plain
organizations
Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), PRB Group: Pattern Recognition and Bioinformatics;
Leiden University Medical Center, Department of Radiology, Division of Image Processing (LKEB)

DATA

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