  Background: Selecting feature genes to predict phenotypes is one of the
typical tasks in analyzing genomics data. Though many general-purpose
algorithms were developed for prediction, dealing with highly correlated genes
in the prediction model is still not well addressed. High correlation among
genes introduces technical problems, such as multi-collinearity issues, leading
to unreliable prediction models. Furthermore, when a causal gene (whose
variants have an actual biological effect on a phenotype) is highly correlated
with other genes, most algorithms select the feature gene from the correlated
group in a purely data-driven manner. Since the correlation structure among
genes could change substantially when condition changes, the prediction model
based on not correctly selected feature genes is unreliable. Therefore, we aim
to keep the causal biological signal in the prediction process and build a more
robust prediction model.
  Method: We propose a grouping algorithm, which treats highly correlated genes
as a group and uses their common pattern to represent the group's biological
signal in feature selection. Our novel grouping algorithm can be integrated
into existing prediction algorithms to enhance their prediction performance.
Our proposed grouping method has two advantages. First, using the gene group's
common patterns makes the prediction more robust and reliable under condition
change. Second, it reports whole correlated gene groups as discovered
biomarkers for prediction tasks, allowing researchers to conduct follow-up
studies to identify causal genes within the identified groups.
  Result: Using real benchmark scRNA-seq datasets with simulated cell
phenotypes, we demonstrate our novel method significantly outperforms standard
models in both (1) prediction of cell phenotypes and (2) feature gene
selection.
