The nearest neighbors of each selected/target gene in the Expression Matrix are selected with correlation or distance measure independently, then all target genes and their nearest neighbors are hierarchically clustered and visualized in an interactive matrix heatmap, where target genes are labeled by black lines. The interactive features include 1) mouse over a cell to see row/column labels and cell value, and 2) draw a rectangle to zoom in and double click to zoom out.

Measure: Use "correlation" (Pearson correlation coefficent, PCC) or "distance" (Euclidean distance) to select nearest neighbors.
Cor.absolute: Yes-only absolute PCCs are used; No-negative PCCs are maintained.
Select by: Genes with most similar expression profiles with each target gene are selected as nearest neighbors. The are three selection ways. The "proportion" means top proportion of most similar genes, "number" denotes a number of top most similar genes, and "value" means most similar genes with similarity above a specific value of Cutoff.
Scale by: Scale the matrix heatmap by column, by row, or no scale. The scaled values are only used to strengthen values difference in the heatmap, not for downstream network analysis.
Update: Every time when selected genes changed in the Data Matrix, the matrix heatmap is not updated automatically. Otherwise the internal computation would slow down the app, especially when many genes are selected in a short time. Once the final target genes are selected, simply click this button and the latest matrix heatmap will be shown.