Dongze He

This paper introduces simpleaf, a tool for efficient sub-gene-level quantification of probe-based single-cell data, showing improved speed and memory use compared to existing tools.

scRM is a single-cell foundation model trained on raw scRNA-seq reads, showing that raw data pretraining is feasible and cost-effective compared to count-based models.

QCatch provides a standardized QC reporting framework for single-cell sequencing data, generating interactive HTML reports and annotated H5AD files for downstream analysis.

The paper generates a single-cell ISR atlas in a mouse model of persistent ISR activation, identifying cell-type-specific ISR programs and their roles in cognitive decline.

DifferentialRegulation identifies differentially regulated genes by analyzing unspliced mRNA abundance across experimental conditions using a Bayesian hierarchical model.

Forseti predicts the splicing status of scRNA-seq reads by combining binding affinity and fragment length models, improving accuracy in downstream analyses.

scCensus evaluates off-target scRNA-seq reads and shows they contain valuable information about chromatin structure, antisense transcription, and cell clustering.

simpleaf simplifies single-cell data processing using alevin-fry tools, adding new functionality while maintaining flexibility and performance.

The paper evaluates a new method for classifying splicing status in single-cell and single-nucleus RNA-seq data, finds it conservative but ambiguous, and introduces new tools to improve classification.

Alevin-fry provides a fast, memory-efficient, and accurate method for quantifying single-cell and single-nucleus RNA-seq data, including RNA velocity inputs.