InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniques

Abstract

We introduce InGene, the first of its kind, fast and scalable non-linear, unsupervised method for analyzing single-cell RNA sequencing data (scRNA-seq). While non-linear dimensionality reduction techniques such as t-SNE and UMAP are effective at visualizing cellular sub-populations in low-dimensional space, they do not identify the specific genes that influence the transformation. InGene addresses this issue by assigning an importance score to each expressed gene based on its contribution to the construction of the low-dimensional map. InGene can provide insight into the cellular heterogeneity of scRNA-seq data and accurately identify genes associated with cell-type populations or diseases, as demonstrated in our analysis of scRNA-seq datasets.