A posterior probability based Bayesian method for single-cell RNA-seq data imputation

Single-cell RNA-sequencing (scRNA-seq) data suffer from a lot of zeros. Such dropout events impede the downstream data analyses. We propose BayesImpute to infer and impute dropouts from the scRNA-seq data. Using the expression rate and coefficient of variation of the genes within the cell subpopulation, BayesImpute first determines likely dropouts, and then constructs the posterior distribution for each gene and uses the posterior mean to impute dropout values. Some simulated and real experiments show that BayesImpute can effectively identify dropout events and reduce the introduction of false positive signals. Additionally, BayesImpute successfully recovers the true expression levels of missing values, restores the gene-to-gene and cell-to cell correlation coefficient, and maintains the biological information in bulk RNA-seq data. Furthermore, BayesImpute boosts the clustering and visualization of cell subpopulations and improves the identification of differentially expressed genes. We further demonstrate that, in comparison to other statistical-based imputation methods, BayesImpute is scalable and fast with minimal memory usage.

Automated summary

scRNA-seq data often have many zeros, and these dropout events hinder downstream data analysis. We propose BayesImpute to infer and impute dropout values in scRNA-seq data. By utilizing the expression rate and coefficient of variation of genes, BayesImpute identifies likely dropouts and imputes missing values using posterior distributions. Simulated and real experiments show that BayesImpute effectively identifies dropout events, reduces false positive signals, and preserves biological information. Furthermore, BayesImpute improves cell subpopulation analysis, differential expression detection, and is scalable and fast with low memory usage compared to other statistical-based imputation methods.