AI and Machine Learning in Single Cell Genomics - Day 2

Dive into the cutting-edge world of single-cell genomics with this comprehensive conference recording from Day 2 of the AWSOM24 Conference, featuring AI and Machine Learning. Explore a series of expert talks covering spatial tissue profiling, multi-omics, and innovative analytical approaches in single-cell technologies. Learn about the latest advancements in bioinformatics, single-cell analytics, and experimental applications of technologies like scRNAseq, snATACseq, and spatial transcriptomics for biological, clinical, and translational research. Gain insights from renowned speakers from Harvard Medical School, Georgia Tech, and Emory University as they discuss topics such as spatial morphoproteomic features, gene expression encoding spatial location, parallelized doublet detection, and cell type discovery using ATAC-Seq features. The 7-hour recording includes breaks, a lunch session, and concludes with an awards ceremony and closing remarks, providing a thorough overview of the current state and future directions in single-cell genomics research.

Spatial Tissue Profiling: from a Novelty to a Discovery Powerhouse, Ioannis Vlachos, Harvard Medical School
Studying Single Cells Through Multi-Omics and Spatiotemporal Context, Xiuwei Zhang, Georgia Tech
BREAK
Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles, Thomas Hu, Georgia Tech
Spatial Location Encoded in Gene Expression: A New Analytical Approach to Spatial Transcriptomics, Yeojin Kim, Georgia Tech
parDoub: Parallelized Doublet Detection for scRNA-seq, Kiersten Campbell, Emory
Discovering Cell Types and States from Reference with Heterogeneous Single-Cell ATAC-Seq Features, Yuqi Cheng, Georgia Tech
LUNCH BREAK/POSTER SESSION OFFLINE
Awards Session
Closing Remarks, Manoj Bhasin & Saurabh Sinha