Andrew W. Navia

This study compares CRISPR knockout and interference using Perturb-seq, finding consistent transcriptional signatures and tradeoffs in off-target activity and cellular response artifacts.

This study evaluates how pre-training dataset size and diversity affect single-cell foundation model performance on zero-shot and fine-tuned tasks using 22.2 million cells.

This paper introduces NCLUSION, a nonparametric clustering method for scRNA-seq that identifies marker genes and clusters simultaneously, showing improved performance and reduced runtime.

This paper introduces a method to prevent over-clustering in scRNA-seq by controlling double-dipping, improving differential expression analysis accuracy.

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

This study examines how SARS-CoV-2 variants and vaccination affect nasal immunity across three waves, identifying changes in cellular states and immune responses.

This study identifies mutation and transcriptional state compatibility in Ph+ ALL MRD, showing that targeting these interactions can overcome BCR::ABL1 independence and improve MRD monitoring.

The paper introduces a method to spatially label and isolate cells using photoactivatable dyes, demonstrating its application in organoids and tissue sections to study spatial gene expression and cell behavior.