Page Comparison

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1. Compute correlation coefficient matrix.
2. Determine optimal value for the scale free exponent, beta, and collect regression statistics.
3. Compute the topological overlap matrix (TOM).
4. Perform hierarchical clustering of genes, based on TOM.
5. Detect and label modules in TOM, using "Dynamic Tree Cutting".
6. Merge modules based on hierarchical clustering of representative genes.
7. Cluster samples hierarchically.
8. Compute intra/inter-module network statistics, per gene.
9. Produce diagnostic plots (dendrograms, heat maps, statistical scatter plots).
10. Produce tabular output of module membership, network statistics, and scale-free regression statistics.

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• leverage the UCLA-WGCNA package for the "common" steps, 1->5, gaining significant performance
• provide the user a parameter choice at step 5, to do "tree cutting" in the manner of the Sage algorithm, or in that of the UCLA-WGCNA algorithm
• provide two algorithms for step 6 (module merging), allowing a user to choose the Sage or UCLA-WGCNA algorithm
• leverage the UCLA-WGCNA dendrogram/module plotting algorithm in step 9
• maintain the Sage algorithms for the Sage-specific post-processing, i.e. step 7, statistics in step 8 , the and the heat maps in step 9.

External dependencies

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module merging, by analyzing most-highly-connected genes in each module

fixed-cluster-number tree cutting for sample module definition

computation of within- and between- module per-gene connectivity statistics

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