S-IGTD: supervised tabular-to-image topology learning via between-group correlation for multiclass classification of biological data

Motivation: Tabular-to-image methods allow convolutional neural network (CNN)-based classifiers to analyse high-dimensional biological tables by mapping features onto a two-dimensional grid. Existing layouts are usually driven by unsupervised global correlation, which can place class-discriminative features far apart when nuisance or housekeeping covariation dominates the total covariance structure. Results: We present the Supervised Image Generator for Tabular Data (S-IGTD), a supervised extension of IGTD that optimizes tabular-to-image topology by replacing total-correlation distance with one minus the absolute between-group correlation, computed from class-wise feature means, under the Within-And-Between-Analysis (WABA) decomposition. We prove entrywise consistency of the supervised distance matrix under standard moment conditions and identify balanced-class settings in which S-IGTD improves a Signal Dispersion Score (SDS)-related topology objective. In controlled simulations targeting between-group signal, S-IGTD outperformed Euclidean- and correlation-distance IGTD variants in SDS, accuracy and macro-F1 score. Across five biological benchmarks ranging from 4- to 91-class classification, S-IGTD produced compact class-supervised layouts, with 24/35 Holm-adjusted significant SDS wins against seven non-reference layout controls. As a secondary downstream diagnostic, a CNN with batch normalization showed higher mean accuracy than random layouts and correlation-distance IGTD on all real datasets, and higher mean accuracy than Euclidean-distance IGTD on four of five datasets, with the clearest gains on large multiclass cancer and methylation benchmarks. Availability and implementation: Source code, datasets, configuration files and reproducibility scripts are freely available at https://github.com/hanmingwu1103/S-IGTD.