6th Workshop on Computational Advances for Single-Cell Omics Data Analysis
Workshop Chairs:
Alex Zelikovsky (Georgia State University, alexz@gsu.edu)
Marmar Moussa (University of Oklahoma, marmar.moussa@ou.edu)
Murray Patterson (Georgia State University, mpatterson30@gsu.edu)
Recent technological advances have enabled high-throughput profiling of genomes, transcriptomes, epigenomes, and proteomes at single cell resolution. These revolutionary single-cell-omics technologies promise to bring unprecedented insights into tissue heterogeneity and unveil subtle regulatory processes that are undetectable by bulk sample analysis. However, fully realizing their potential requires the development of novel computational and statistical analysis methods capable of handling the massive data sizes and significant levels of technical and biological noise. The goal of the CASCODA workshop is to bring together bioinformaticians, biologists, computer/data scientists, and statisticians to discuss the latest developments in computing infrastructure, mathematical and statistical modeling, algorithms, and visualization methods for single-cell-omics data.
Workshop topics of interest include but are not limited to:
Bioinformatics workflows for single-cell -omics data analysis
Cell atlases
Clustering and cell type inference
Integration of multiple modalities of single-cell data
Lineage inference
Modeling of missing data and imputation
Normalization and batch effect removal
Quality control for single-cell sequencing data
Single-cell phylogenetics
Single-cell RNA-Seq quantification
Single-cell spatial reconstruction
Variant calling and haplotyping
Visualization of single-cell -omics data
The meeting is by invitation only. If you would like to inquire about the possibility of being invited, please contact the workshop chairs as soon as possible, but no later than November 30, 2024. Following the workshop, speakers will be invited to submit extended abstracts for publication in the Springer LNBI (pending approval) post-proceedings volume devoted to ICCABS 2025 and/or full length articles to a special issue of the Journal of Computational Biology.