Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm

Big data methods prevail in the biomedical domain leading to effective and scalable data-driven approaches. Biomedical data are known for their ultra-high dimensionality, especially the ones coming from molecular biology experiments. This property is also included in the emerging technique of single-cell RNA-sequencing (scRNA-seq), where we obtain sequence information from individual cells. A reliable way to uncover their complexity is by using Machine Learning approaches, including dimensional reduction and feature selection methods. Although the first choice has had remarkable progress in scRNA-seq data, only the latter can offer deeper interpretability at the gene level since it highlights the dominant gene features in the given data. Towards tackling this challenge, we propose a feature selection framework that utilizes genetic optimization principles and identifies low-dimensional combinations of gene lists in order to enhance classification performance of any off-the-shelf classifier (e.g., LDA or SVM). Our intuition is that by identifying an optimal genes subset, we can enhance the prediction power of scRNA-seq data even if these genes are unrelated to each other. We showcase our proposed framework's effectiveness in two real scRNA-seq experiments with gene dimensions up to 36708. Our framework can identify very low-dimensional subsets of genes (less than 200) while boosting the classifiers' performance. Finally, we provide a biological interpretation of the selected genes, thus providing evidence of our method's utility towards explainable artificial intelligence.