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If an algorithm requires a matrix of size #probes x #probes (e.g. a correlation or TOM 'distance matrix' for clustering), then # probes <46340. Clearly this colon cancer data set, with 54,675 probes exceeds this limit.
The code does run to completion for the UCLA-WGCNA code when using a preprocessing option to break up the genes into 'blocks' using K-means clustering. Requesting a block size of 10,000 resulted in 6 blocks and 2h:47m of run time.
Other options to address large probe numbers include pre-filtering to select a subset of probes of interest (e.g. using just the probes with the greatest variation across samples).