Gunalan Natesan

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

The paper introduces a binomial model to identify biologically variable genes in scRNA-seq data, showing it outperforms existing methods in preserving biological structure.

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy

This paper introduces embGAN, a deep learning pipeline for automated cell detection and tracking in label-free 3D time-lapse imaging without manual annotation.

Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development

This paper introduces a new metric called branch distance to analyze developmental phenotypes in C. elegans embryos, revealing previously unnoticed heterogeneity and links between cell fate and cell cycle pathways.

A lack of distinct cell identities in single-cell measurements: revisiting Waddingtons landscape

This paper challenges the idea that distinct cell types correspond to attractors in gene expression space by showing cells of different types occupy the same regions in epigenetic space.