ArticleBriefings in bioinformatics2026
Imputing missing values in single-cell RNA-sequencing data: a statistical and machine learning-based approach.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to capture gene expression patterns within individual cells. However, due to the limited RNA content within cells, dropout events occur, resulting in a substantial number of zero counts in the single-cell expression matrix. To address this issue, we propose a novel method called single-cell dropout detection and imputation (scDDI). This method identifies dropout events using a Poisson-negative binomial mixture model and subsequently imputes the missing values using a decision tree regression model. We evaluate the performance of scDDI on both simulated and real scRNA-seq datasets, demonstrating its superiority over established single-cell imputation techniques. Notably, scDDI significantly improves dropout detection, leading to enhanced performance in various downstream analysis tasks like gene expression recovery, cell clustering, and cell subpopulation identification.
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