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View Submission - SDS2022
A0169
Title: Identification of antigen specificity in single cell RNAseq experiments using biclustering methods for binary data Authors:  Mohamad Zafer Merhi - Hasselt University (Belgium) [presenting]
Dan Lin - GlaxoSmithKline (Belgium)
Ahmed Essaghir - GlaxoSmithKline (Belgium)
Ziv Shkedy - Hasselt University (Belgium)
Abstract: The single-cell RNA-sequencing technology allows the assessment of heterogeneous cell-specific changes and their biological characteristics. In our current study, we focus on single cell omics data for immune profiling purposes. T-cells exhibit unique behavior referred to as cross-reactivity; the ability of T-Cells to recognize two or more peptide-MHC complexes by the TCR. A CD8+ T Cell is defined as specific for an antigen if the cell binds to the antigen. The work is applied to single-cell RNA-seq data (publicly available in https://support.10xgenomics.com/single-cell-vdj/datasets/) consisting of CD8+ T Cells obtained using a state-of-the-art single-cell omics technology from 10X Genomics and our aim is to assess and understand the heterogeneous characteristics and the binding specificities of these CD8+ T Cells, i.e., we aim to identify the specificity of the CD8+ T cells to one (or more) antigen(s). For the identification of specific CD8+ T Cells, we proposed an unsupervised data analysis pipeline. Biclustering methods are applied to recover and explore the cross-reactive behaviour of T Cells and to identify a subset of cells which are specific to a subset of antigens. Clustering methods are used to link these subsets to the RNA-seq data. Furthermore, we discuss the challenges of the application and evaluation of clustering algorithms on the single cell RNA-seq data.