scRMD

scRMD imputes dropout events in single-cell RNA sequencing (scRNA-seq) data using restricted robust matrix decomposition to recover true gene expression profiles and improve downstream analyses.

Key Features:

• Robust Matrix Decomposition: Models dropout imputation using a restricted robust matrix decomposition framework that assumes the underlying gene expression matrix is low-rank and dropouts are infrequent.
• Improved Data Quality: Recovers missing gene expression values to reduce technical noise in scRNA-seq datasets.
• Enhanced Analytical Power: Restores gene-to-gene and cell-to-cell relationships to increase the precision of downstream biological analyses.
• Computational Efficiency: Implements the decomposition-based imputation approach with consideration for scalable computation on large single-cell transcriptomic datasets.

Scientific Applications:

• Differential Expression Analysis: Improves sensitivity and specificity for identifying differentially expressed genes by imputing dropout values.
• Clustering Analysis: Provides more complete expression matrices to support more accurate identification of cellular subpopulations.

Methodology:

scRMD employs a restricted robust matrix decomposition that assumes a low-rank true expression profile and rare dropout events to distinguish true zeros from technical dropouts and impute missing values.