CS 260 (Seminar in Bioinformatics) Homepage

Venue : Watkins 1117

Time : MWF 3:10-4pm

Instructor :

Tao Jiang (jiang@cs.ucr.edu)
Office hours: MW 4-5pm. Office: WCH 336.

The theme of the courses is "Computational Challenges in the Analysis of Single-Cell Omics Data". Single-cell technologies have advanced a lot in recent years and moved many aspects of omics studies into the single-cell era. Through this seminar, we hope to learn some recent developments in the field concerning the analysis of various types of single-cell omics data, including genomics, metagenomics, transcriptomics, epigenomics, proteomics, or multi-omics, with a special focus on interesting computational/algorithmic issues.

The form of the seminar will be like a journal club. Every student is expected to give two 40-minute presentations of papers in the recent literature. However, if you prefer, one of the presentations could also be about your work (even if it may not concern single cells :-)

Here is a comprehensive collection of single cell omics data analysis papers that you may choose from.

The following is the tentative schedule of the presentations:

1/9 Tao Jiang: Combinatorial Methods for Inferring Isoforms from Short Sequence Reads

1/14 Tao Jiang: Toward More Sensitive Differential Expression Analysis on RNA-Seq Data

1/16 Ashraful Arefeen: DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

1/23 Yuzhu Duan: Gene expression signature search and functional enrichment methods for discovering novel modes of action of bioactive compounds

1/28 Dipan Shaw: Using neural networks for reducing the dimensions of single-cell RNA-Seq data

1/30 Dipankar Ranjan Baisya: DeepCRISPR: optimized CRISPR guide RNA design by deep learning

2/4 Yangyang Hu: 4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?

2/6 Tiantian Ye: Unsupervised embedding of single-cell Hi-C data

2/13 Paraskevi Dimoragka: CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data

2/15 Yuzhu Duan: UMI-count modeling and differential expression analysis for single-cell RNA sequencing

2/20 Ashraful Arefeen: Sequential regulatory activity prediction across chromosomes with convolution neural networks

2/22 Dipankar Ranjan Baisya: Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

2/25 Dipan Shaw: Network embedding-based representation learning for single cell RNA-seq data

2/27 Weihua Pan: Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

3/1 Paraskevi Dimoragka: An accurate and robust imputation method scImpute for single-cell RNA-seq data

3/4 Yangyang Hu: Identification of spatial expression trends in single-cell gene expression data

3/6 Hao Chen: Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

3/11 Tiantian Ye: Reversed graph embedding resolves complex single-cell trajectories

3/13 Jianyu Zhou: Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development