Rohan Gala

This paper introduces scDMGC, a method to analyze high-dimensional transcriptomic data using topological data analysis, revealing cell-type organization and transcriptional gradients in brain datasets.

The paper integrates transcriptomic, morphological, and electrophysiological data to define neuron types in mouse visual cortex and predict their projection targets.

TabVI improves cell type annotation and integration using a probabilistic transformer model, producing interpretable feature attention masks and robust performance across dataset sizes.

This study creates an integrated multimodal cell atlas of Alzheimer's disease by analyzing single nuclei transcriptomes, epigenomes, and spatial coordinates across AD stages.

MMIDAS jointly infers discrete cell types and continuous type-specific variability in single-cell datasets, improving reproducibility and robustness in cell type identification.

FR-Match matches query single-cell RNA-sequencing datasets to reference atlases to identify common and novel cell types and suboptimally clustered cells with high accuracy.

The paper analyzes dendritic and axonal arbors of inhibitory neurons to identify transcriptomic correlates of neuroanatomy.

The paper develops coupled autoencoders to identify cortical neurons across modalities and finds consistent cell types in transcriptomic and electrophysiological data.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper analyzes single-cell RNA-seq data from mouse neocortical neurons to identify widespread neuropeptide signaling networks across all cortical neurons.