Contributed Papers

Single-Cell and Next-generation Sequencing Omics Data Analysis

Tue, Aug 5, 8:30 AM - 10:20 AM Room CC-103B Music City Center

Asmita RoyChair

Section on Statistics in Genomics and Genetics

7 Presentations

Experimental Design and Differential Inference for Comparative Single-cell RNA-sequencing Studies

8:35 AM - 8:50 AM

Co-authors: Fangda Song (The Chinese University of Hong Kong, Shenzhen), Kevin Y. Yip (Sanford Burnham Prebys Medical Discovery Institute), Fangda Song (The Chinese University of Hong Kong, Shenzhen), Yingying Wei (The Chinese University of Hong Kong)

Single-cell RNA-sequencing experiments Differential gene expression Differential abundance Experimental design Model identifiability Integrative analysis

McRigor: a statistical method to enhance rigor of metacell partitioning in single-cell data analysis

8:50 AM - 9:05 AM

Co-authors: Pan Liu, Pan Liu, Jingyi Jessica Li (UCLA)

Metacell partitioning Single-cell RNA-seq Single-cell ATAC-seq Data sparsity Permutation

Detection of Cell-type-specific eQTL for scRNA-seq Data with Unknown Cell Types

9:05 AM - 9:20 AM

Co-authors: Jiasheng Li (The Chinese University of Hong Kong, Shenzhen), Jiasheng Li (The Chinese University of Hong Kong, Shenzhen), Fangda Song (The Chinese University of Hong Kong, Shenzhen)

Single-cell RNA Sequencing (scRNA-seq)Expression quantitative trait loci (eQTL)Integrative Analysis Mixture Model

Harmonizing Heterogeneous Single-cell Gene Expression Data with Individual-level Covariates

9:20 AM - 9:35 AM

Co-authors: Yudi Mu, Yudi Mu, Vivian Li (University of California, Riverside)

single-cell RNA-seq integration generalized linear latent variable model

Estimating a phylogenetic forest for single-cell RNA-sequencing data

9:35 AM - 9:50 AM

Co-authors: Shuyi WANG, Yingying Wei (The Chinese University of Hong Kong), Shuyi WANG

single-cell RNA-sequencing data phylogenetic tree Dirichlet diffusion tree Bayesian hierarchical model

Identifying cell-specific cancer susceptibility genes using transcriptome-wide association studies

9:50 AM - 10:05 AM

Co-authors: Fei Qin, Fei Qin, Kai Yu, Jianxin Shi

Transcriptome-wide association studies Single-cell RNA sequencing Cell type Cancer Genome-wide association studies

Cn-RNN: a Supervised Learning Framework for CNV Detection with Sequencing Data

10:05 AM - 10:20 AM

Co-authors: Dayuan Wang, Dayuan Wang, Wenhan Bao, Fei Qin, Feifei Xiao (University of Florida)

Copy Number Variants (CNV) Detection Recurrent Neural Networks (RNN)Supervised Learning Statistical Genetics Deep Learning in Genomics