S
Shiquan Sun
Researcher at University of Michigan
Publications - 35
Citations - 1098
Shiquan Sun is an academic researcher from University of Michigan. The author has contributed to research in topics: Feature selection & Medicine. The author has an hindex of 12, co-authored 32 publications receiving 566 citations. Previous affiliations of Shiquan Sun include Xidian University & Xi'an Jiaotong University.
Papers
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Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies
TL;DR: Analyzing four published spatially resolved transcriptomic datasets using SPARK shows it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.
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Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
TL;DR: This work compares 18 different dimensionality reduction methods on 30 publicly available scRNA-seq datasets that cover a range of sequencing techniques and sample sizes and provides important guidelines for choosing dimensionality Reduction methods for sc RNA-seq data analysis.
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Collaborative filtering recommendation algorithm based on user preference derived from item domain features
TL;DR: This work introduces a framework that utilizes item domain features to construct user preference models and combines these models with collaborative filtering (CF) and aids to detecting the implicit relationships among users, which are missed by the conventional CF method.
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Differential expression analysis for RNAseq using Poisson mixed models.
Shiquan Sun,Shiquan Sun,Michelle M Hood,Laura J. Scott,Qinke Peng,Sayan Mukherjee,Jenny Tung,Xiang Zhou +7 more
TL;DR: A Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence is presented and a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution is developed.
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Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies.
Zhongshang Yuan,Zhongshang Yuan,Huanhuan Zhu,Ping Zeng,Sheng Yang,Shiquan Sun,Can Yang,Jin Liu,Xiang Zhou +8 more
TL;DR: A powerful TWAS method based on probabilistic Mendelian Randomization, PMR-Egger, which is reasonably robust under various types of model misspecifications, is more powerful than existing TWAS/MR approaches, and can directly test for horizontal pleiotropy.