On Thursday, February 11th at 7 pm UTC | 8 pm CET, as part of the Why R? Webinar series, we have the honour to host Dr Koen Van den Berge, postdoctoral Scholar at UC Berkeley and Ghent University. He will talk about the interpretation of single-cell RNA-seq trajectories using additive models for differential expression analysis.
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- Koen Van den Berge
Koen has obtained Master’s degrees in Biology and Statistical Data Analysis at Ghent University. His doctoral research under the supervision of Prof. Lieven Clement at Ghent University concerned the development of statistical methods for differential expression analysis, multiple testing and normalization of high-throughput sequencing data. Currently, he is a postdoctoral researcher at the University of California, Berkeley and Ghent University, focussing on the development of tools for the interpretation of single-cell RNA-seq datasets, under the supervision of Prof. Lieven Clement and Prof. Sandrine Dudoit.
Interpretation of single-cell RNA-seq trajectories using additive models for differential expression analysis.
In single-cell RNA-sequencing, gene expression is measured at the level of single cells, opening the door to answer
questions related to cell-level heterogeneity in gene expression. In dynamic biological systems, researchers often use
trajectory inference to estimate developmental cell lineages based on the observed data, e.g., the development of a stem
cell to multiple mature cell types, which has radically enhanced single-cell RNA-seq research by enabling the
corresponding study of dynamic changes in gene expression.
Downstream of trajectory inference, it is vital to interpret the trajectory through the discovery of genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages. This requires statistical models that are capable of estimating flexible patterns of average gene expression as a function of the developmental time. We will briefly introduce additive models as one approach that serves this purpose. Next, we will introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. If the dataset consists of multiple conditions (e.g., treatment groups), we will explore how we can assess differential progression along a developmental path (i.e., lineage), and differential expression between conditions within that path.