Sricharan Reddy Varra

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

CellFuse integrates single-cell and spatial proteomics data using a deep learning framework, outperforming existing methods in accuracy and efficiency.

The paper uses spatial imaging and computational analysis to identify features in metastatic tumors that predict response to immune checkpoint inhibition in TNBC patients.

The paper introduces Pixie, a pipeline for robust pixel-level clustering in multiplexed imaging data to improve phenotyping and annotation across biological contexts.