From: BicPAMS: software for biological data analysis with pattern-based biclustering
 | Parameter | Value | Notes |
---|---|---|---|
Major parameters | P3 Coherency assumption | Constant assumption | A default assumption considers a (possibly noise-tolerant) constant pattern on a subset of rows/columns/nodes, providing an adequate degree of flexibility (superior to biclusters with differential/dense values or constant values overall) well suited for initial analyzes. |
 | P4 Coherency strength | \(|\mathcal {L}|\)=5 or δ=\(\bar {A}\)/5 | Adequate sensitivity to different levels of expression ({-2,-1} {0} and {1,2} sets of symbols correspond to down-regulation, preserved and up-regulation) or association strength. Multiple symbols can be assigned to a single real-valued element to guarantee robustness to noise. |
 | P5 Quality | 80% | Guarantees an adequate tolerance to noise, allowing biclusters to have up to 20% of noisy values. |
 | P15 Pattern representation | Closed | Closed pattern representations enable the discovery of maximal biclusters (biclusters that cannot be extended without removing rows or columns). |
 | P16 Orientation | Patterns on rows | In accordance with Def.2. Considering expression data where rows correspond to genes, a bicluster with coherency across rows is defined by a group of genes with the same pattern along a subset of conditions. When rows correspond to conditions, a less-trivial bicluster is given by a group genes with preserved expression spanning a subset of conditions. |
Mapping options | P6 Normalization | Row | Normalization of values per biological entity or sample. |
 | P7 Discretization | Gaussian | Cut-off points of a learned Gaussian curve to minimize imbalanced distributions of items. |
 | P8 Noise handler | None | By default multi-item assignments are deactivated for an easy interpretation of results. Nevertheless, we suggest the selection of multi-item assignments to guarantee a heightened robustness to discretization drawbacks and noise. |
 | P9 Symmetries | Dynamic | Symmetries are dynamically selected if the inputted data has negative values. This option can be deactivated to force the biclustering task to not distinguish positive from negative values. |
 | P10 Missings handler | Remove | Remove is suggested since Quality P5 is already in place to accommodate missing values within biclusters. Nevertheless, Replace option is suggested for data with a considerable amount of missing values. |
 | P11 Remove uninformative elements | None | By default, no items are removed. Alternative options should be only selected in the presence of knowledge regarding uninformative elements, such as non-differential expression or loose interactions. |
Mining options | P12 Stopping criteria | 50 biclusters | A minimum number of 50 biclusters (before postpro cessing) is suggested by default since the combination of this option with the quality and dissimilarity criteria leads to a compact set of dissimilar biclusters. This number (as well as the number of iterations) can be increased to guarantee more complete solutions for complex or large datasets. |
 | P13 Min. ♯columns | 4 | Although maximal biclusters have at least 4 columns by default, this number should be increased for datasets where biclusters have a significantly higher number of columns. |
 | P14 ♯Iterations | 2 | Guarantees the removal of small and highly coherent regions in the dataset (after the 1st iteration) to enable the discovery of less-trivial biclusters. This number can be increased to promote a more even distribution of biclusters across the regions of the inputted data. |
 | P17 Pattern miner | Dynamic | From empirical evidence, CharmDiff is suggested for closed patterns, CharmMFI for maximal patterns, and F2G for simple patterns. When order-preserving coherency is inputted, IndexSpan is suggested by default. |
 | P18 Scalability | Dynamic | Option activated in the presence of very large datasets (>20 million elements under a constant assumption and >1 million elements for the remaining coherency assumptions). |
Closing | P19 Merging | Heuristic | Guarantees an efficient yet quasi-exact postprocessing. |
 | P20 Filtering | 40% dissimilar elements | Guarantees an adequate level of dissimilarity. Biclusters sharing more than 60% of their elements with a larger bicluster are removed. |