M3Drop

M3Drop applies the Michaelis-Menten model to characterize dropout events in single-cell RNA sequencing (scRNA-seq) data and identify significantly variable genes for downstream analyses such as clustering.

Key Features:

• Modeling Dropout Patterns: Fits the Michaelis-Menten model to dropout patterns in scRNA-seq data to quantify technical dropout versus biological signal.
• Null Model for Differential Expression: Uses the Michaelis-Menten fit as a null hypothesis to detect genes exhibiting variability beyond expected dropout-driven variation.
• Facilitation of Downstream Analyses: Outputs lists of significantly variable or differentially expressed genes to support downstream tasks such as clustering and cell-type classification.

Scientific Applications:

• Clustering: Provides gene sets that improve cell clustering by focusing on genes with significant variability across single cells.
• Gene-Level Analyses: Enables investigation of gene expression dynamics and variability at single-cell resolution.
• Differential Expression Detection: Supports identification of differentially expressed genes by distinguishing biological variability from dropout-related noise.

Methodology:

Fits the Michaelis-Menten model to dropout frequency patterns in scRNA-seq data and uses that model as a null hypothesis to identify genes with excess variability.