SMS scnews item created by Miranda Luo at Wed 15 May 2024 1232
Type: Seminar
Distribution: World
Expiry: 21 May 2024
Calendar1: 20 May 2024 1300-1400
Auth: (jluo0722) in SMS-SAML

Statistical Bioinformatics Seminar: Hsiao-Chi Liao

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.

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