Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data

Abstract

Background: Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, $L_0{L}_1$ penalisation and $L_0{L}_2$ penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.

Methods: Five large $\left(n4000\right)$ genomic datasets were extracted from Gene Expression Omnibus. 'Gold-standard' regression models were trained on subspaces of these datasets ( $n4000$ , $p=500$ ). Penalised regression models were trained on small samples from these subspaces ( $n\in \left\{25,75,150\right\},p=500$ ) and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty 'preselection' according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation.

Results: $L_1{L}_2$ -penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models. $L_0{L}_2$ -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions. $L_0{L}_2$ also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics.

Conclusions: This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of $L_0{L}_2$ penalisation for structural selection and $L_1{L}_2$ penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

Keywords: Artificial intelligence; computational biology; gene regulatory networks; genomics; models; statistical.