Speaker: Hsiao-Chi Liao (University of Melbourne) Abstract: Single-cell multimodal technologies provide an opportunity to study biological mechanisms in a more comprehensive manner. CITE-seq (cellular indexing of transcriptomes and epitopes) assay simultaneously measures mRNA and surface proteins at the single-cell level, and is one of the most popular single-cell multi-omics platforms. The integrated analysis of mRNA expression and protein abundance can help reveal biological insight that would not have been possible from separate analyses of each modality. Unwanted variation from sources such as shared batches and domain-specific library size effects inevitably exists in data from both domains. If not properly corrected, the unwanted variation can potentially lead to misleading conclusions being made from the downstream analyses. We propose a method for removing unwanted variation from matched single-cell multi-omics data that allows us to estimate joint and modality-specific unwanted effects. In our preliminary study with four matched single-cell multi-omics datasets, we have shown that our approach is generally competitive in terms of preserving biological signals and mitigating the undesired technical effects compared to current methods such as Seurat, and can do better when the biological and unwanted variation are associated, as it can avoid removing too much biological signal from the data. About the speaker: Hsiao-Chi is a doctoral candidate at the School of Mathematics and Statistics, The University of Melbourne, supervised by Dr. Agus Salim, Dr. Terry Speed, and Dr. Davis McCarthy. Her research interests are integrative analysis of single-cell multi-omics datasets and methods development for analysing omics data. She is currently working on her PhD projects that aim to develop statistical methods for removing unwanted variation from proteomics and transcriptomics data.