Factorial state-space modelling for kinetic clustering and lineage inference
Claassen, M.; Gupta, R.
Show abstract
Single-cell RNA sequencing (scRNAseq) protocols measure the abundance of expressed transcripts for single cells. Gene expression profiles of cells (cell-states) represent the functional properties of the cell and are used to cluster cell-states that have a common functional identity (cell-type). Standard clustering methods for scRNAseq data perform hard clustering based on KNN graphs. This approach implicitly assumes that variation among cell-states within a cluster does not correspond to changes in functional properties. Differentiation is a directed process of transitions between cell-types via gradual changes in cell-states over the course of the process. We propose a latent state-space Markov model that utilises cell-state transitions derived from RNA velocity to model differentiation as a sequence of latent state transitions and to perform soft kinetic clustering of cell-states that accommodates the transitional nature of cells in a differentiation process. We applied this model to the differentiation of Radial-glia cells into mature neurons and demonstrate the utility of our method in discriminating between functional and transitional cell-states.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation 96%
- Fusion of single-cell transcriptome and DNA-binding data, for genomic network inference in cortical development 95%
- CDSeqR: fast complete deconvolution for gene expression data from bulk tissues 95%
Similar papers in this journal
Similar papers in this journal
- One model fits all: combining inference and simulation of gene regulatory networks 95%
- Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error 95%
- G2S3: a gene graph-based imputation method for single-cell RNA sequencing data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.