Author

Ning Leng

Morgridge Institute for ResearchORCID 0000-0002-1015-5749Unclaimed ORCID profile · follow to get notified of new preprints

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Ning Leng2 papers

Biology Disciplines

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Approach:statistical model· 3 Research Focus:differential expression· 2 Assay:scRNA-seq· 2 Research Focus:batch correction / integration· 1 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1

Frequent Co-Authors

Christina M. KendziorskiiD· 3 shared James Alexander ThomsoniD· 3 shared Li‐Fang ChuiD· 3 shared Rhonda L. BacheriD· 3 shared Ron M. StewartiD· 3 shared Angela L. Elwell· 1 shared Audrey P. GaschiD· 1 shared Bret M. DuffiniD· 1 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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3 shared papersGenomics

Christina M. KendziorskiORCID

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3 papers

Recent·Most cited

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis

Morten Seirup, Li‐Fang Chu, Srikumar Sengupta, Ning Leng, Hadley Browder, Kevin Kapadia, Christina M. Shafer, Bret M. Duffin, Angela L. Elwell, Jennifer M. Bolin, Scott Allen Swanson, Ron M. Stewart, Christina M. Kendziorski, James Alexander Thomson, Rhonda L. Bacher

This study evaluates the reproducibility of spatial ordering analysis across different single-cell RNA-seq protocols, finding high reproducibility despite differences in sequencing depth and computational methods.

PLoS ONE · Sep 28, 2020Peer-reviewedGenomics

Trendy: Segmented regression analysis of expression dynamics for high-throughput ordered profiling experiments

Rhonda L. Bacher, Ning Leng, Li‐Fang Chu, Zijian Ni, James Alexander Thomson, Christina M. Kendziorski, Ron M. Stewart

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.

BMC Bioinformatics · Oct 16, 2018Peer-reviewedGenomics

SCnorm: A quantile-regression based approach for robust normalization of single-cell RNA-seq data

Rhonda L. Bacher, Li‐Fang Chu, Ning Leng, Audrey P. Gasch, James Alexander Thomson, Ron M. Stewart, Michael A. Newton, Christina M. Kendziorski

SCnorm provides a quantile-regression based normalization method for scRNA-seq data to reduce artifacts and improve downstream analysis accuracy.

Nature Methods · Apr 17, 2017Peer-reviewedGenomics