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Title:
Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles | bioRxiv
Description:
Advances in RNA-seq technologies provide unprecedented insight into the variability and heterogeneity of gene expression at the single-cell level. However, such data offers only a snapshot of the transcriptome, whereas it is often the progression of cells through dynamic biological processes that is of interest. As a result, one outstanding challenge is to infer such progressions by ordering gene expression from single cell data alone, known as the cell ordering problem. Here, we introduce a new method that constructs a low-dimensional non-linear embedding of the data using laplacian eigenmaps before assigning each cell a pseudotime using principal curves. We characterise why on a theoretical level our method is more robust to the high levels of noise typical of single-cell RNA-seq data before demonstrating its utility on two existing datasets of differentiating cells.
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Keywords {🔍}
biology, data, cell, medrxiv, subject, reviews, clinical, preprint, ordering, singlecell, rnaseq, biorxiv, university, trials, epidemiology, search, results, laplacian, eigenmaps, principal, curves, high, pdf, gene, expression, level, cells, method, copyright, institute, technology, cancer, bioinformatics, context, genetics, microbiology, papers, submitted, cookies, skip, main, content, homesubmitfaqblogalerts, rssresourcesaboutchannels, keyword, advanced, follow, resolution, pseudotemporal, profiles,
Topics {✒️}
single-cell rna-seq data epidemiology subject categories clinical trials single cell data single-cell level genetics microbiology biorxiv subject category cell ordering problem epidemiology papers reviews author/funder keyword advanced search health-related information dynamic biological processes generous financial support chan zuckerberg initiative imperial college london vrije universiteit amsterdam articles animal behavior ordering gene expression caleb webber doi context cell preprint laplacian eigenmaps gene expression laplacian eigenmaps high levels data offers theoretical level results follow principal curves outstanding challenge low-dimensional linear embedding noise typical existing datasets rights reserved reuse allowed california institute massachusetts institute top previous posted september 18 report results user experience continue find granted biorxiv differentiating cells copyright holder set cookies
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