Single-cell RNA-seq data imputation using Feature Propagation

TL;DR

This paper introduces scFP, a novel graph-based method for imputing missing gene expression data in single-cell RNA sequencing, improving cell clustering accuracy by propagating features in raw gene expression space.

Contribution

scFP is the first to directly propagate gene expression features via a cell-cell graph, combining hard and soft feature propagation for improved imputation.

Findings

scFP outperforms recent imputation methods in clustering tasks.

The method effectively denoises gene expression data.

scFP demonstrates robustness across various datasets.

Abstract

While single-cell RNA sequencing provides an understanding of the transcriptome of individual cells, its high sparsity, often termed dropout, hampers the capture of significant cell-cell relationships. Here, we propose scFP (single-cell Feature Propagation), which directly propagates features, i.e., gene expression, especially in raw feature space, via cell-cell graph. Specifically, it first obtains a warmed-up cell-gene matrix via Hard Feature Propagation which fully utilizes known gene transcripts. Then, we refine the k-Nearest Neighbor(kNN) of the cell-cell graph with a warmed-up cell-gene matrix, followed by Soft Feature Propagation which now allows known gene transcripts to be further denoised through their neighbors. Through extensive experiments on imputation with cell clustering tasks, we demonstrate our proposed model, scFP, outperforms various recent imputation and clustering methods. The source code of scFP can be found at https://github.com/Junseok0207/scFP.