MarkerMap: nonlinear marker selection for single-cell studies
This work addresses the computational challenge of marker selection for interpretability in single-cell studies, though it appears incremental as it builds on existing methods with a new framework.
The researchers tackled the problem of selecting minimal gene sets that explain cell type variability in single-cell RNA-seq data, introducing MarkerMap, a generative model that achieves competitive performance on real datasets against existing approaches.
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.