Visualization of 3D random point generation and Delaunay triangulation of the resulting point set. The columns visualize the outcome of the algorithm after 5, 6, 25, and 50 steps, respectively. The last row shows the result of several addition operations (the formula syntax is ``command result operands''.):
AddCell 4_1 3_2 3_1 2_2 2_1;
AddCell 4_2 3_3 3_2 2_3 2_2;
AddCell 4_3 3_4 3_3 2_4 2_3;
AddCell 4_4 3_4 3_3 3_2 3_1;

Spreadsheet for Visualization framework development process

A screen snapshot of visualizing sequence similarity reports after performing three operations. (Step 1) Initially, we loaded each column with a slightly different, but related, dataset (A1=B1=C1=D1, A2=B2=C2=D2, A3=B3=C3=D3). (Step 2) We selected Row B, and then subtracted cell A3 from it (B1=B1-A3, B2=B2-A3, B3=B3-A3). Cell B3 contains the empty set as expected. (Step 3) We changed Row C and D to show different views of Row A. The views show different sets of variables using a different representation, thus increasing our ability to see other dimensions of the multivariate datasets simultaneously.

Visualization of time-series matrices. The screen snapshot shows visualizations of protein residue substitution probability matrices of various evolutionary distances. The first, second, and third rows visualize matrix 40, 120, and 250 from the PAM matrix series. The fourth row visualizes matrix 62 from the BLOSUM matrix series. The first column uses a cube representation that maps positive matrix values to the volume, height, and color attributes of the cubes. The second column uses a carpet plot that maps values to the height and color of a 3D surface. The third column uses a bar representation that maps values to the length, height, and color attributes of the bars. The fourth column shows various representations in different rotational configurations.