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Posts by Collection
portfolio
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publications
Dropout Imputation and Batch Effect Correction for Single-Cell RNA-seq Data
Published in Journal of Bio-X, 2019
Recommended citation: Li, Gang, et al. "Dropout imputation and batch effect correction for single-cell RNA sequencing data." Journal of Bio-X Research 2.4 (2019): 169-177. https://journals.lww.com/jbioxresearch/Fulltext/2019/12000/Dropout_imputation_and_batch_effect_correction_for.4.aspx
SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection
Published in Briefings in Bioinformatics, 2020
Recommended citation: Yuchen Yang, Gang Li, Huijun Qian, Kirk C Wilhelmsen, Yin Shen, Yun Li, SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection, Briefings in Bioinformatics , , bbaa097, https://doi.org/10.1093/bib/bbaa097 https://doi.org/10.1101/672261
Deep Fiducial Inference
Published in Stat, 2020
Recommended citation: Li, G, Hannig, J. Deep fiducial inference. Stat. 2020;e308. https://doi.org/10.1002/sta4.308 https://doi.org/10.1002/sta4.308
CUE: CpG impUtation Ensemble for DNA Methylation Levels Across the Human Methylation450 (HM450) and EPIC (HM850) BeadChip Platforms
Published in Epigenetics, 2020
Recommended citation: Gang Li, Laura Raffield, Mark Logue, Mark W. Miller, Hudson P. Santos Jr, T. Michael O’Shea, Rebecca C. Fry & Yun Li (2020) CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms, Epigenetics, DOI: 10.1080/15592294.2020.1827716 https://doi.org/10.1080/15592294.2020.1827716
iSMNN: Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinementn
Published in Briefings in Bioinformatics, 2021
Recommended citation: Yang, Y., Li, G., Xie, Y., Wang, L., Lagler, T. M., Yang, Y., Liu, J., Qian, L., & Li, Y. (2021). iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement. Briefings in bioinformatics, bbab122. Advance online publication. https://doi.org/10.1093/bib/bbab122 https://pubmed.ncbi.nlm.nih.gov/33839756/
talks
Across-Platforms Imputation for DNA Methylation Levels
Published:
Presented at 2018 American Society of Human Genetics Annual Meeting. See more info here. This is a joint work with Prof. Yun Li et. al.
Deep Fiducial Inference and Approximate Fiducial Computation
Published:
Presented at Sixth Bayesian, Fiducial, and Frequentist (BFF6) Conference on Model Uncertainty. This is a joint work with Prof. Jan Hannig.
(Contributed Talk) Deep Fiducial Inference and Approximate Fiducial Computation
Published:
Presented at 2019 MUMS Transition Workshop and SPUQ. See my slides on SAMSI website. This is a joint work with Prof. Jan Hannig.
Deep Fiducial Inference and Approximate Fiducial Computation
Published:
Received Student and Early-Career Travel Award!
(Invited Talk) Ensemble Imputation for DNA Methylation Levels Across Platforms
Published:
Presented at The 2019 ICSA China Conference. This is a joint work with Prof. Yun Li et. al.
Ensemble Imputation for DNA Methylation Levels Across Platforms
Published:
Presented at 2019 American Society of Human Genetics Annual Meeting. This is a joint work with Prof. Yun Li et. al.
Ensemble Imputation for DNA Methylation Levels Across Platforms
Published:
2020 Conference on Statistical Practice. This is a joint work with Prof. Yun Li et. al.
SMNN: Batch Effect Correction for Single-Cell RNA-seq Data via Supervised Mutual Nearest Neighbor Detection
Published:
Presented at 2020 Eastern North American Region Spring Meeting. This is a joint work with Prof. Yun Li et. al.
SMNN: Batch Effect Correction for Single-Cell RNA-seq Data via Supervised Mutual Nearest Neighbor Detection
Published:
Presented at 2020 JSM. This is a joint work with Prof. Yun Li et. al.
DeepGWAS to Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network
Published:
Presented at 2020 American Society of Human Genetics Annual Meeting. Our abstract has been selected as reviewer’s choice (~10%). This is a joint work with Prof. Yun Li et. al.
Coupling Machine Learning with Fiducial Inference and Genetics
Published:
Gang successfully passed the public oral defense.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at UW 4D-Genome Meeting.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at 2021 4DN Annual Meeting .
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at 2022 UW GS postdoctoral seminar.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang was invited to give a talk, “Integration of imaging and sequencing data in the context of visual cell sorting”, at 2022 HSPH Program in Quantitative Genomics Seminar.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang was invited to give a talk, “Integration of imaging and sequencing data in the context of visual cell sorting”, at The 5th International Conference on Econometrics and Statistics(EcoStat 2022).
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at 2022 WA GS retreat.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at 2022 4DN annual meeting.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang was invited to give a talk at the 8th Young Statistician Forum at Shanghai University of Finance and Economics.
Integration of imaging and sequencing data in the context of visual cell sorting
Published:
Gang presented at 2023 UW CMB Annual Meeting.
Pseudotime analysis and integration for time-series single-cell sequencing and imaging data
Published:
Gang presented at UW 4D-Genome Meeting.
Pseudotime analysis and integration for time-series single-cell sequencing and imaging data
Published:
Gang presented at 2023 Statistical Genetics Symposium.
Pseudotime analysis for time-series single-cell sequencing and imaging data
Published:
Gang presented at 2023 UW CMB Annual Meeting.
Pseudotime analysis for time-series single-cell sequencing and imaging data
Published:
Gang presented at 2023 MLCB.
Pseudotime analysis for time-series single-cell sequencing and imaging data
Published:
Gang presented at 2023 4DN annual meeting.
Deep Fiducial Inference and Apporximate Fiducial Computation
Published:
Gang was invited to give a talk at 2023 Computational and Methodological Statistics meeting.
teaching
(2017 spring) Introduction to Data Analysis (STOR 151)
Undergraduate course, UNC at Chapel Hill, STOR, 2017
Served as a teaching assistant, graded the homework for the course.
(2020 spring) Introduction to Data Models and Inference (STOR 155)
Undergraduate course, UNC at Chapel Hill, STOR, 2020
Served as a teaching assistant for 4 sections (>300 students). Held office hours and tutorial sessions every week.
(2020 fall) Introduction to Data Models and Inference (STOR 155)
Undergraduate course, UNC at Chapel Hill, STOR, 2020
Served as a teaching assistant for 2 sections (>100 students). Held office hours and tutorial sessions weekly.
(2020 fall) Statistical Methods in Genetic Association Studies / Introduction to Statistical Genetics (BIOS782/BCB725)
Undergraduate course, UNC at Chapel Hill, STOR, 2020
Served as a teaching assistant. Held office hours weekly and graded homework.
(2020 fall - invited guest lecture) Genetic Epidemiology: Methods and Applications (EPI 743)
Graduate course, UNC at Chapel Hill, 2020
Invited to give a lecture on DNA methylation imputation.
(2021 spring) Statistical Computing (STOR 893.2)
Graduate course, UNC at Chapel Hill, STOR, 2021
Served as a teaching assistant. Graded homeworks and held office hours weekly. First graduate-level computation course in STOR department in my Ph.D. study. I am excited to be part of it.