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
datasetposted on 03.10.2016 by Boudewijn Lelieveldt
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.