Yichen Yang

This paper introduces a scalable batch-correction method for integrating large-scale single-cell transcriptomes and shows it matches state-of-the-art performance on 18 million cells from the Human Cell Atlas.

The paper introduces tGPT, a generative model for integrating large-scale single-cell transcriptomes, showing its effectiveness in clustering, trajectory inference, and linking gene patterns to clinical outcomes.

The paper introduces iSEEEK, a method for integrating large-scale single-cell transcriptomes using gene rankings, showing good clustering and transferability across datasets.

This paper introduces an improved version of the CBDA algorithm for handling high-dimensional multisource datasets, validated on synthetic and real-world health data.