scGeno: a Hidden Markov Model approach to denoise chromosome-scale genotypes from single-cell data.

Motivation: Single-cell analysis of monoallelic expression and genomic imprinting requires accurate genotype determination at the cellular level. However, genotype inference from single-cell RNA sequencing data is challenging due to technical noise, allelic dropout, and sparse gene expression patterns, particularly in genetically heterogeneous populations. Results: Here, we present scGeno, a categorical Hidden Markov Model that infers chromosome-level genotype states in organisms with mixed genotypes by modeling sequential gene expression ratios from single-cell RNA sequencing data. Our method leverages the sequential continuity of the genotype states along chromosomes to overcome single-cell data limitations and generates chromosome-resolved, comprehensive genotype maps for individual samples. Our probabilistic framework accounts for technical noise while maintaining high accuracy in genotype assignment. Validation on experimental data demonstrates robust performance in determining clear genotypic states, thereby enabling systematic investigation of allele-specific expression patterns at single-cell resolution. Availability and implementation: scGeno is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from GitHub (https://github.com/RosariaTornisiello/Genotype_HMM.git).