Carissa Chen

This paper identifies and categorizes technical biases in imaging spatial transcriptomics and proposes a unified framework to model these biases.

CLUEY improves cell type detection and clustering in single-cell omics by integrating biological knowledge to guide clustering and enhance interpretability.

The paper introduces BrainSTEM, a multi-resolution mapping framework to evaluate midbrain differentiation protocols by comparing them to a fetal brain atlas, revealing off-target cell types in most published models.

SpaNorm improves spatial transcriptomics data normalisation by separating spatial library size effects from biological signals, enhancing domain and spatially variable gene identification.

This study evaluates spatially variable gene detection methods across multiple spatial transcriptomics datasets to assess their performance, reliability, and practicality.

Cepo identifies cell identity by analyzing differentially stable genes from scRNA-seq data, improving cell-type characterization and spatial mapping.

The paper describes the generation and characterization of novel LexA enhancer trap lines in Drosophila through a collaborative interscholastic network.