L
Lei Xiong
Researcher at Tsinghua University
Publications - 4
Citations - 172
Lei Xiong is an academic researcher from Tsinghua University. The author has contributed to research in topics: Scale (ratio) & Population. The author has an hindex of 2, co-authored 4 publications receiving 88 citations.
Papers
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Journal ArticleDOI
SCALE method for single-cell ATAC-seq analysis via latent feature extraction.
Lei Xiong,Kui Xu,Kang Tian,Yanqiu Shao,Lei Tang,Ge Gao,Michael Q. Zhang,Tao Jiang,Qiangfeng Cliff Zhang +8 more
TL;DR: SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation, and generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATac-seq experiments.
Posted ContentDOI
CD127 imprints functional heterogeneity to diversify monocyte responses in human inflammatory diseases
Bin Zhang,Yuan Zhang,Yuan Zhang,Lei Xiong,Yuzhe Li,Yunliang Zhang,Yunliang Zhang,Jiuliang Zhao,Jiang Hui,Can Li,Can Li,Yunqi Liu,Xindong Liu,Haofei Liu,Yi-Fang Ping,Qiangfeng Cliff Zhang,Xiu-Wu Bian,Yan Zhao,Xiaoyu Hu +18 more
TL;DR: This work phenotypically and molecularly characterized a human monocyte subset marked by CD127 that retained anti-inflammatory properties within the pro-inflammatory environments, uncovering remarkable functional diversity among monocytes and signifying M127 as a potential therapeutic target for human inflammatory disorders.
Posted ContentDOI
Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space
TL;DR: SCALEX is developed, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space and outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations whileaining true biological differences.
Posted ContentDOI
Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space
TL;DR: SCALEX as mentioned in this paper integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner, which substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark singlecell datasets of diverse modalities.