Enhancing single-cell cellular state inference by incorporating molecular network features
Posted 14 Jul 2019
bioRxiv DOI: 10.1101/699959
Posted 14 Jul 2019
In biological systems, genes function in conjunction rather than in isolation. However, traditional single-cell RNA-seq (scRNA-seq) analyses heavily rely on the transcriptional similarity of individual genes, ignoring the inherent gene-gene interactions. Here, we present SCORE, a network-based method, which incorporates the validated molecular network features to infer cellular states. Using real scRNA-seq datasets, SCORE outperforms existing methods in accuracy, robustness, scalability, data integration and removal of batch effect. When applying SCORE to a newly generated human ileal scRNA-seq dataset, we identified several novel stem/progenitor clusters, including a Cripto-1+ cluster. Moreover, two distinct groups of goblet cells were identified and only one of them tended to secrete mucus. Besides, we found that the recently identified BEST4+OTOP2+ microfold cells also highly expressed CFTR, which is different from their colonic counterparts. In summary, SCORE enhances cellular state inference by simulating the dynamic changes of molecular networks, providing more biological insights beyond statistical interpretations.
- Downloaded 1,593 times
- Download rankings, all-time:
- Site-wide: 13,142
- In bioinformatics: 1,480
- Year to date:
- Site-wide: 30,798
- Since beginning of last month:
- Site-wide: 49,761
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!