Qiaoni Yu

Abstract Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy.

Abstract The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analyses on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages.

Bin Li1 na1, Young Li1 na1, Kun Li1, Lianbang Zhu1, Qiaoni Yu1, Pengfei Cai1, Jingwen Fang1,2, Wen Zhang1, Pengcheng Du1, Chen Jiang1, Jun Lin1 & Kun Qu ORCID: orcid.org/0000-0002-5555-84371,3 Genome Biology volume 21, Article number: 116 (2020) Cite this article Abstract The development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge.