Suvo Chatterjee

ReMeDy identifies differentially methylated regions by jointly modeling mean and variance changes in DNA methylation data, improving statistical power and biological interpretation.

This paper introduces SACOMA, a spatially-aware unsupervised method to identify co-methylated regions in DNA methylation data, improving sensitivity and false-positive control.

The paper introduces an ensemble framework for differential analysis that combines multiple models to improve performance across various omics data types.

This paper introduces Robseq, a statistical model for RNA-Seq differential expression that addresses FDR inflation caused by group heteroscedasticity, showing improved control of false discovery and type-I error rates.

The paper introduces Tweedie models for differential expression analysis in scRNA-seq, showing improved performance over existing methods across platforms.