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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. statistics in step 8 and the heat maps in step 9. Accelerate step 8 using compiled code.
External dependencies
WGCNA::cor -- the compiled/accelerated Pearson correlation computation
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computation of within- and between- module per-gene connectivity statistics (but using additional compiled code to dramatically speed up analysis of large modules)
heatmap generation for correlation and TOM matrices
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Correlation computation is faster, with identical results.
TOM computation is faster, with identical results.
Hierarchical clustering is faster, with identical results.
Scale-free exponent (beta) determination is similar, with very similar results and regression statistics.
"Dynamic tree cutting" algorithm is the same, with very similar results.
Intramodular statistics are faster, with identical results.
Diagnostic plot set is reduced from 12 to 8, omitting redundant plots.
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