Quantifying the clusterness and trajectoriness of single-cell RNA-seq data
PLoS Computational Biology, ISSN: 1553-7358, Vol: 20, Issue: 2, Page: e1011866
2024
- 4Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the “clusterness” and “trajectoriness” of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley’s K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85186424808&origin=inward; http://dx.doi.org/10.1371/journal.pcbi.1011866; http://www.ncbi.nlm.nih.gov/pubmed/38416795; https://dx.plos.org/10.1371/journal.pcbi.1011866; https://dx.doi.org/10.1371/journal.pcbi.1011866; https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011866
Public Library of Science (PLoS)
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