Rxivist logo

bigSCale: An Analytical Framework for Big-Scale Single-Cell Data

By Giovanni Iacono, Elisabetta Mereu, Amy Guillaumet-Adkins, Roser Corominas, Ivon Cuscó, Gustavo Rodríguez-Esteban, Ivo Glynne Gut, Luis Alberto Pérez-Jurado, Ivo Gut, Holger Heyn

Posted 03 Oct 2017
bioRxiv DOI: 10.1101/197244 (published DOI: 10.1101/gr.230771.117)

Single-cell RNA sequencing significantly deepened our insights into complex tissues and latest techniques are capable processing ten-thousands of cells simultaneously. Increasing cell numbers, however, generate extremely large datasets, extending processing time and challenging computing resources. Current scRNAseq analysis tools are not designed to analyze datasets larger than thousands of cells and often lack sensitivity to identify marker genes. With bigSCale, we provide an analytical framework being scalable to analyze millions of cells, addressing challenges of future large datasets. To handle the noise and sparsity of scRNAseq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering and marker identification. A directed convolution strategy allows processing of extremely large datasets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using a biological model of aberrant gene expression in patient derived neuronal progenitor cells and simulated datasets, which underlined its speed and accuracy in differential expression analysis. To test its applicability for large datasets, we applied bigSCale to analyze 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as Reelin positive Cajal-Retzius neurons, for which we determined a previously not recognized heterogeneity associated to distinct differentiation stages, spatial organization and cellular function. Together, bigSCale presents a perfect solution to address future challenges of large single-cell datasets.

Download data

  • Downloaded 1,546 times
  • Download rankings, all-time:
    • Site-wide: 6,506 out of 101,137
    • In genomics: 1,010 out of 6,270
  • Year to date:
    • Site-wide: 53,080 out of 101,137
  • Since beginning of last month:
    • Site-wide: 67,222 out of 101,137

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)


  • 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
  • 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!