MarkerMap: nonlinear marker selection for single-cell studies

07/28/2022

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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.

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MarkerMap: nonlinear marker selection for single-cell studies

Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data

A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data

Single-cell RNA-seq data imputation using Feature Propagation

Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data

Multiscale methods for signal selection in single-cell data

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Reply to "Issues arising from benchmarking single-cell RNA sequencing imputation methods"

MarkerMap: nonlinear marker selection for single-cell studies

Related Research

Active feature selection discovers minimal gene-sets for classifying cell-types and disease states in single-cell mRNA-seq data

A systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data

Single-cell RNA-seq data imputation using Feature Propagation

Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data

Multiscale methods for signal selection in single-cell data

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Reply to "Issues arising from benchmarking single-cell RNA sequencing imputation methods"