DCAE: Selecting Discriminative Genes on Single-cell RNA-seq Data for Cell-type Quantification

Tumor-liltrating lymphocytes (TILs) are predictive for response to neoadjuvant treatment in tumors. Still, the abundance of tumor-liltrating cell types has not yet been produced in large quantities, hampering researchers exploring their characteristics. As the levels of genomics or transcriptomics could reflect changes in cell-type proportions, several computational tools have been developed to estimate cell-type abundances based on the reference gene expression proliles. Differential expression analysis is the most widely used to recognize marker genes. However, it ignores the correlation between genes. To this end, we propose a feature selection method, dubbed Discriminative Concrete Autoencoder (DCAE), to identify informative genes on single-cell RNA-seq data, which are then used to quantity cell-type proportions. To evaluate the performance of DCAE on selecting discriminative genes, we conduct experiments on our collected and processed single-cell RNA-seq dataset. First, we compare DCAE to the original Concrete Autoencoder by the cell-type classification accuracies resulting from their selected genes. Then we infer cell-type abundance by using deconvolution function with the chosen small cohort of genes. Next, we evaluate the deconvolution accuracy by the Pearson correlation coefficient between the estimated cell-type proportions and the true proportions, and the corresponding P-value. Finally, we compare the effects of the selected genes and the differential expression genes on the deconvolution accuracy. The results show that our selected genes by DCAE have higher discriminant power to distinguish cell types and effectively infer cell-type abundance. Thus, DCAE provides insights into acquiring candidate biomarkers for cell-type quantification.