1. Load Data

2. Data Summary

All input data, with size of:

Numeric data matrix for visualization, with size of:


3. Customization

Data preprocessing:

In the PCA plot:
PCA: Principal Component Analysis

Figure legend preview

t-SNE: t-Distributed Stochastic Neighbor Embedding

MDS: Multidimensional Scaling

Dimensions of the grid

Correlation of observations
Correlation of features

Heatmap of the whole dataset

Save as png Save as pdf Save as Table

Data for drawing
Note: graph will be colored according the color customization set on the left panel

© 2017. Author: Dijun Chen (chendijun2012 at gmail.com). All right reserved. This web application was built with the Shiny framework.