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Table 2 Number of enriched biclusters found by each algorithm on each dataset

From: Identifying gene-specific subgroups: an alternative to biclustering

datasetCCAISAK-CPGCPlaidQUBICSpectralxMOTIFsCPGC
11 (5.0)7 (1.0)6 (2.0)1 (5.0)0 (7.5)0 (7.5)2 (3.0)1 (5.0)
20 (7.5)8 (1.0)6 (2.0)1 (5.0)1 (5.0)0 (7.5)2 (3.0)1 (5.0)
31 (6.0)8 (1.0)7 (2.0)1 (6.0)5 (3.0)0 (8.0)3 (4.0)1 (6.0)
41 (5.5)2 (2.5)1 (5.5)2 (2.5)0 (8.0)5 (1.0)1 (5.5)1 (5.5)
52 (4.0)6 (1.5)2 (4.0)1 (6.5)6 (1.5)0 (8.0)2 (4.0)1 (6.5)
61 (5.5)7 (1.0)5 (2.0)2 (4.0)0 (7.5)0 (7.5)3 (3.0)1 (5.5)
72 (5.0)3 (4.0)8 (1.0)1 (7.0)7 (2.5)7 (2.5)1 (7.0)1 (7.0)
83 (5.0)8 (1.5)8 (1.5)1 (7.5)6 (3.0)5 (4.0)2 (6.0)1 (7.5)
91 (7.0)2 (4.5)7 (1.0)1 (7.0)6 (2.5)6 (2.5)2 (4.5)1 (7.0)
101 (5.0)1 (5.0)5 (1.0)1 (5.0)0 (8.0)2 (2.0)1 (5.0)1 (5.0)
110 (7.5)1 (4.5)4 (1.5)0 (7.5)4 (1.5)1 (4.5)1 (4.5)1 (4.5)
122 (3.5)8 (1.0)7 (2.0)0 (7.5)2 (3.5)0 (7.5)1 (5.5)1 (5.5)
130 (7.0)3 (1.5)3 (1.5)0 (7.0)2 (3.5)0 (7.0)2 (3.5)1 (5.0)
142 (4.5)3 (2.5)1 (6.5)0 (8.0)2 (4.5)10 (1.0)3 (2.5)1 (6.5)
153 (2.0)3 (2.0)2 (4.5)0 (8.0)3 (2.0)2 (4.5)1 (6.5)1 (6.5)
161 (5.5)8 (1.5)8 (1.5)0 (7.5)4 (3.0)0 (7.5)2 (4.0)1 (5.5)
170 (7.0)3 (2.0)1 (4.5)0 (7.0)3 (2.0)0 (7.0)3 (2.0)1 (4.5)
182 (2.0)1 (4.0)3 (1.0)0 (7.0)1 (4.0)0 (7.0)0 (7.0)1 (4.0)
198 (2.5)0 (7.0)9 (1.0)6 (4.0)8 (2.5)0 (7.0)0 (7.0)1 (5.0)
206 (2.0)3 (4.5)10 (1.0)3 (4.5)4 (3.0)0 (8.0)2 (6.0)1 (7.0)
212 (4.0)1 (6.5)8 (1.0)4 (2.0)2 (4.0)0 (8.0)2 (4.0)1 (6.5)
226 (2.0)1 (6.5)8 (1.0)0 (8.0)3 (4.5)5 (3.0)3 (4.5)1 (6.5)
232 (2.5)0 (7.0)4 (1.0)0 (7.0)2 (2.5)0 (7.0)1 (4.5)1 (4.5)
244 (1.5)0 (6.5)4 (1.5)0 (6.5)0 (6.5)0 (6.5)1 (3.5)1 (3.5)
255 (1.5)0 (7.0)5 (1.5)3 (3.0)0 (7.0)0 (7.0)1 (4.5)1 (4.5)
264 (1.5)0 (7.5)4 (1.5)2 (4.5)2 (4.5)0 (7.5)3 (3.0)1 (6.0)
274 (1.0)1 (6.5)3 (2.0)2 (4.0)2 (4.0)0 (8.0)2 (4.0)1 (6.5)
285 (1.0)0 (7.0)4 (2.0)2 (3.5)2 (3.5)0 (7.0)0 (7.0)1 (5.0)
293 (3.5)1 (6.0)6 (1.0)3 (3.5)4 (2.0)0 (8.0)1 (6.0)1 (6.0)
305 (1.0)0 (7.5)2 (2.5)1 (5.0)1 (5.0)0 (7.5)2 (2.5)1 (5.0)
314 (2.5)1 (5.5)5 (1.0)4 (2.5)1 (5.5)0 (8.0)1 (5.5)1 (5.5)
328 (1.5)0 (7.5)5 (4.5)7 (3.0)8 (1.5)0 (7.5)5 (4.5)1 (6.0)
336 (3.0)0 (8.0)7 (2.0)3 (4.0)9 (1.0)1 (6.0)1 (6.0)1 (6.0)
346 (2.0)0 (7.5)4 (3.0)3 (4.5)7 (1.0)0 (7.5)3 (4.5)1 (6.0)
357 (1.0)0 (6.5)5 (2.0)2 (3.0)0 (6.5)0 (6.5)0 (6.5)1 (4.0)
avg. rank3.74.52.15.43.96.24.75.6
  1. Numbers in parentheses are the associated ranks. In case of ties, average ranks are assigned. The last row corresponds to the algorithm ranks averaged over the 35 datasets. Best performances are highlighted in bold. It is observed that all enriched biclusters have different GO enrichment. Note that CPGC is the original algorithm identifying a single submatrix of maximal sum per dataset