Statistical Bioinformatics Webinar: Lin -- Transfer Learning for Data Integration of Single-cell RNA-seq and ATAC-seq

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https://uni-sydney.zoom.us/meeting/register/tJMuc-yupzgqG9wuIVJI7qB8lAOGUreWpvP4 

Single-cell transcriptomics profiling with single-cell RNA-seq (scRNA-seq) has provided
unprecedented resolution in charatersing cell identities, cell functions across diverse
tissues and conditions. Recent advances in measuring multiple modalities of single
cells, such as single-cell ATAC sequencing (scATAC-seq), further enable characterisation
of cells from different aspects. While scATAC-seq data provides the epigenomics
profiling of cells, its extreme sparsity leads to its lack of the power of cell type
identification. Therefore, integrative analysis of scRNA-seq and scATAC-seq allows not
only cell type label transferring but also better understanding of the cellular
phenotypes. We develop an end-to-end transfer learning algorithm, scJoint, to integrate
scRNA-seq and scATAC-seq data. By building an integrative framework with neural network
based dimension reduction and semi-supervised cell type prediction model, our algorithm
is able to transfer labels from scRNA-seq to scATAC-seq data and construct a joint
embedding for the two modalities. We illustrate our algorithm with two mouse cell atlas
data from scRNA-seq and scATAC-seq data. We found that our algorithm outperforms the
existing methods by a large margin in both joint visualisation of two modalities and
cell type prediction.