Author

Li Wang

The University of Texas at ArlingtonORCID 0000-0001-9357-6938Unclaimed ORCID profile · follow to get notified of new preprints

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Li Wang33 papers · Chinese Academy of Sciences

Biology Disciplines

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Bioinformatics· 2 Genomics· 2

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Approach:statistical model· 4 Research Focus:benchmarking· 2 Organism:human· 2 Approach:machine learning (classical)· 2 Assay:scRNA-seq· 2 Research Focus:trajectory inference· 2 Approach:benchmark study· 1 Assay:ChIP-seq· 1

Frequent Co-Authors

Cole TrapnelliD· 2 shared Qi MaoiD· 2 shared Xiaojie QiuiD· 2 shared Arman Rahimzamani· 1 shared Barry I. FreedmaniD· 1 shared Bing SongiD· 1 shared Bingcheng Ren· 1 shared Donald W. BowdeniD· 1 shared

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10 top collaborators · 14 mutual co-author links

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2 shared papersGenomics

Cole TrapnellORCID

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4 papers

Recent·Most cited

De-noising Spatial Transcriptomics Data Based on Position and Image Information

Yunguan Wang, Bing Song, Shidan Wang, Mingyi Chen, Yang Xie, Guanghua Xiao, Li Wang, Tao Wang

This paper develops Sprod, a method to denoise spatial transcriptomics data using location and image information, improving downstream analyses.

bioRxiv · Nov 3, 2021 · PreprintBioinformatics

Towards inferring causal gene regulatory networks from single cell expression measurements

Xiaojie Qiu, Arman Rahimzamani, Li Wang, Bingcheng Ren, Qi Mao, Timothy Durham, José L. McFaline‐Figueroa, Lauren M. Saunders, Cole Trapnell, Sreeram Kannan

This paper introduces Scribe, a toolkit for causal gene regulatory network inference from single-cell data, showing that pseudotime data performs worse than true time series data.

Cell Systems · Mar 1, 2020Peer-reviewedGenomics

Progression of chronic kidney disease in African American with type 2 diabetes mellitus using topology learning in electronic medical records

Li Wang, Xufei Zheng, Lynn S. Huang, Jianzhao Xu, Fang‐Chi Hsu, Shyh‐Huei Chen, Maggie C.Y. Ng, Donald W. Bowden, Barry I. Freedman, Jing Su

This study uses EMRs and GWAS data to map CKD progression trajectories in African Americans with type 2 diabetes, identifying high-risk and low-risk pathways linked to APOL1 genotypes.

bioRxiv · Jul 4, 2018 · PreprintBioinformatics

Reversed graph embedding resolves complex single-cell developmental trajectories

Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A. Pliner, Cole Trapnell

Monocle 2 learns branched single-cell trajectories without supervision via reversed graph embedding, revealing a non-fusing myoblast outcome and knockout myeloid cells diverted onto opposite fate branches.

Nature Methods · Aug 21, 2017Peer-reviewedGenomics✦ In-depth digest