scMTD

scMTD is a software tool developed to address challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. scRNA-seq is a powerful technique for capturing transcriptomes at single-cell resolution. However, dropout events, where certain genes are inadequately detected, can distort gene expression levels and biological signals, making downstream data analysis less accurate.
scMTD is a multidimensional imputation algorithm that leverages a statistical model-based approach. It identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells. This approach integrates information at the cell-level, gene-level, and transcriptome dynamics to recover scRNA-seq data.
In comparison to state-of-the-art imputation methods, scMTD effectively recovers biological signals within transcriptomes. It consistently outperforms other algorithms in various analytical experiments, including improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and the identification of cell types.
scMTD maintains gene expression characteristics, enhances the clustering of cell subpopulations, aids the study of gene expression dynamics, and contributes to the discovery of rare cell types. It is applicable to both UMI-based and non-UMI-based data.