CSCD-SOCR Demonstration of Visualization of High-dimensional Diabetes Data using TensorBoard

CSCD Team | CSCD Methods and Analytics Core

This CSCD Resource demonstrates:
  1. t-distributed stochastic neighbor embedding (t-SNE) statistical method for manifold dimension reduction,
  2. The TensorBoard machine learning platform, and
  3. Hands-on Big Data Analytics using the SOCR Diabetes Case-Study,
  4. Interactive Visual Analytics of user-provided data.
Before you begin, review the SOCR hands-on high-dimensional t-SNE Data Analytics Learning Module and the DSPA Dimensionality Reduction Book Chapter. This video provides additional motivation and intuition in using this CSCD Resource for high-dimensional visual data analytics.

Similar to the analysis of the UK Biobank study and the Country Ranking study, you can provide your own dataset and generate a dynamic visualization.
This will require you to provide a pair of ASCII text files that can be loaded from your computer.

The first file contains tab-delimited (TSV) data including the predictor vectors (row=case * column=features).
The second file is an optional TSV file including metadata like labels for each case (row), if any.
Examples of the two data formats that can be loaded from your computer are included below. Once the data is loaded in the app, you can run the analysis on your own data much like we show here using the CSCD Diabetes Case-Study for dimensionality reduction.

System Description



This resource is developed with partial support from the NIH National Institute of Nursing Research (Grant P20-NR015331). Substantial contributions were provided by investigators, students and faculty in the Center for Complexity and Self-Management in Chronic Disease (CSCD) and the Statistics Online Computational Resource (SOCR) made this possible.