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

?titleData-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data: supplementary MSI data and analysis implementation of paper
?creatororcidLelieveldt, B.P.F. (Boudewijn)
?contributorDelft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), PRB Group: Pattern Recognition and Bioinformatics
?contributorLeiden University Medical Center, Department of Radiology, Division of Image Processing (LKEB)
?date accepted2016-10-03
?date created2016-09-27
?date published2016
?description
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.
?languageen
?publisherTU Delft
?subjectbiomarker ● cancer ● Intratumor heterogeneity ● mass spectrometry imaging ● t-SNE
? ▲ in collection
?related publicationnew windowhttps://doi.org/10.1073/pnas.1510227113
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