Sparse reduced-rank regression for exploratory visualization of single cell patch-seq recordings

bioRxiv(2019)

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摘要
High-throughput single cell transcriptomics is rapidly emerging as the technique of choice to establish a census of neurons in the nervous system. Integrating the resulting cell type census with a physiological and anatomical taxonomy has been difficult, as most techniques require the tissue to be dissociated before sequencing. The recently proposed patch-seq technique allows to acquire multi-modal single cell data, where RNA-seq data is collected together with physiological and morphological information from the same cells. The technique typically results in data sets which have many more dimensions (expression levels of genes and electrophysiological properties) than measurements (cells), making it computationally difficult to relate the two modalities. Here we present a framework based on sparse reduced-rank regression for obtaining an interpretable visualization of the relationship between high-dimensional transcriptomic data and electrophysiological information on the single-cell level.
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