Author

Qing Lan

BGI Group (China)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Developmental Biology· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Research Focus:gene regulatory network inference· 2 Research Focus:spatial domain detection· 2 Assay:spatial transcriptomics· 2 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Organism:Drosophila· 1

Frequent Co-Authors

Xun XuiD· 2 shared Yanrong WeiiD· 2 shared Adeline SeowiD· 1 shared Alan R. SandersiD· 1 shared Alison P. KleiniD· 1 shared Ao CheniD· 1 shared Baosen ZhouiD· 1 shared Bin Jiang· 1 shared

Activity by Year

2016-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersDevelopmental Biology

Xun XuORCID

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Author profile →

3 papers

Recent·Most cited

Spatiotemporal transcriptome atlas of human embryos after gastrulation

Jiexue Pan, Yuejiao Li, Zhongliang Lin, Qing Lan, Ying Zhang, Huixi Chen, Shengwei Sui, Man Zhai, Gaochen Zhang, Yi Cheng, Yunhui Tang, Qingchen Wang, Yue Xu, Guoling Li, Chunyan Xu, , Hefeng Huang

The paper creates a spatiotemporal transcriptome atlas of human embryos after gastrulation, revealing organ-specific regulons and regulatory networks during development.

Nature · May 27, 2026Peer-reviewedDevelopmental Biology

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

Mingyue Wang, Qinan Hu, Tianhang Lv, Yuhang Wang, Qing Lan, Zhencheng Tu, Rong Xiang, Yanrong Wei, Kai Han, Yanru An, Mengnan Cheng, Jiangshan Xu, Miguel Angel Esteban, Haorong Lu, Wangsheng Li, , Longqi Liu

This study uses Stereo-seq to generate high-resolution spatiotemporal transcriptomic maps of Drosophila embryos and larvae, revealing new subregions and gene regulatory networks.

bioRxiv · Oct 21, 2021 · PreprintDevelopmental Biology

Winners curse correction and variable thresholding improve performance of polygenic risk modeling based on summary-level data from genome-wide association studies

Jianxin Shi, Ju-Hyun Park, Jubao Duan, Sonja Berndt, Winton Moy, Kai Yu, Lei Song, William Wheeler, Xing Hua, Debra Silverman, Montserrat Garcia-Closas, Chao Agnes Hsiung, Jonine D Figueroa, Victoria K Cortessis, Núria Malats, , Nilanjan Chatterjee

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.

bioRxiv · Jan 10, 2016 · PreprintGenetics