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A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing

Publication: Journal Article

DelAdel, Alan, Michelle L. Ramseier, Andrew W. Navia, Alex K. Shalek, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, and Lorin Crawford. 2024. “A Knockoff Calibration Method to Avoid Over-Clustering in Single-Cell RNA-Sequencing”. BioRxiv.

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News: July 21, 2022

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Peter Winter

Person: Principal Investigator, Co-Director Ex Vivo, Broad Institute

Peter Winter, PhD Peter is a Principal Investigator at the Broad Institute of MIT and Harvard. He leads a multidisciplinary team that aims to understand how diverse facets of cancer biology (e.g. oncogenic mutations, RNA-state, and the microenvironment) cooperate to drive systems-level tumor...

Zixin Chen

Person: Graduate Student, Broad Institute

Zixin Chen, MD Zixin is a PhD student specializing in pancreatic surgery at West China Hospital of Sichuan University. Since October 2023, he joined the Raghavan Lab as a visiting graduate student. Zixin is interested in understanding the heterogeneity and molecular subtype plasticity of pancreatic...

Nadig, Ajay, Akshaya Thoutam, Madeline Hughes, Anay Gupta, Andrew W. Navia, Nicolo Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, and Lorin Crawford. 2025. “Consequences of Training Data Composition for Deep Learning Models in Single-Cell Biology”. BioRxiv.

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models

Publication: Journal Article

Cultrera_di_Montesano, Sebastiano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini, Peter S. Winter, and Lorin Crawford. 2025. “Hierarchical Cross-Entropy Loss Improves Atlas-Scale Single-Cell Annotation Models”. BioRxiv.

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A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing

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Peter Winter

Zixin Chen

Jacob Smigiel

Hongming Shang

Adapting systems biology to address the complexity of human disease in the single-cell era

Consequences of training data composition for deep learning models in single-cell biology

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models