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Fast searches of large collections of single-cell data using scfind

Nature Methods volume 18, pages 262–271 (2021)Cite this article

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Abstract

Single-cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single-cell data we have developed scfind, a single-cell analysis tool that facilitates fast search of biologically or clinically relevant marker genes in cell atlases. Using transcriptome data from six mouse cell atlases, we show how scfind can be used to evaluate marker genes, perform in silico gating, and identify both cell-type-specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

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Fig. 1: Compression and search times.
Fig. 2: Basic search, identification of cell-type-specific and housekeeping genes.
Fig. 3: Subquery optimization.
Fig. 4: Free text searches and visualization of results.
Fig. 5: Determining cell-type specificity of distal enhancers using RNA-seq and ATAC-seq data.

Data availability

The index LinnarssonAtlas.rds for the the BCA data from http://linnarssonlab.org/data/ can be downloaded from https://scfind.cog.sanger.ac.uk/indexes/LinnarssonAtlas.rds. The index mca.rds for the the MCA data from https://figshare.com/s/865e694ad06d5857db4b can be downloaded from https://scfind.cog.sanger.ac.uk/indexes/mca.rds. The index tm_10x.rds for the the TM, 10X and TM, FACS data from https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733 can be downloaded from https://scfind.cog.sanger.ac.uk/indexes/tm_10X.rds and https://scfind.cog.sanger.ac.uk/indexes/tm_facs.rds respectively. The index atacseq.rds for the the sciATAC-seq data from http://atlas.gs.washington.edu/mouse-atac/data/ can be downloaded from https://scfind.cog.sanger.ac.uk/indexes/atacseq.rds. The source data underlying Figs. 1–3 and 5 are provided as a Source Data file. Source data are provided with this paper.

Code availability

The code for scfind is available at github.com/hemberg-lab/scfind and the code for generating the figures in this manuscript is available at https://github.com/hemberg-lab/scfind-paper-figures. A Code Ocean capsule of the tool is provided (https://doi.org/10.24433/CO.2453077.v1)59.

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Acknowledgements

J.T.H.L., N.P., V.Y.K. and M.H. were supported by a core grant from the Wellcome Trust. J.T.H.L. was also supported by a grant ‘Search tools for scRNA-seq data’ (RR-4145) from the Chan Zuckerberg Initiative and N.P. was supported by UK Dementia Research Institute (DRI) grant RRZA/175. We thank members of the Hemberg group, Y. Liu, T. Bergmann and A. Meziani for assisting with beta testing of the software and L. Garcia-Alonso, V.J. Hall, A. Ori, S. Teichmann and R. Vento for feedback on the manuscript. We thank J. Eliasova for assistance with Fig. 1a.

Author information

Author notes

  1. Martin Hemberg

    Present address: Evergrande Center for Immunologic Disease, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

  2. These authors contributed equally: Jimmy Tsz Hang Lee, Nikolaos Patikas.

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Jimmy Tsz Hang Lee, Nikolaos Patikas, Vladimir Yu Kiselev & Martin Hemberg

  2. UK Dementia Research Institute, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

    Nikolaos Patikas

Authors
  1. Jimmy Tsz Hang Lee
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Contributions

M.H. conceived the project and supervised the research. J.T.H.L., N.P., V.Y.K. and M.H. contributed to the code. J.T.H.L., N.P. and M.H. analyzed the data. J.T.H.L. and M.H. wrote the manuscript with input from N.P. and V.Y.K.

Corresponding author

Correspondence to Martin Hemberg.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Qi Liu and Itoshi Nikaido for their contribution to the peer review of this work. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Discussion, Tables 1–12 and Supplementary Figs. 1–18.

Reporting Summary

Supplementary Table 1

Precision, recall and F1 scores for all cell types in the atlases considered.

Supplementary Table 2

Information about the total number of marker genes and the precision and F1 scores that they provide for each cell type.

Supplementary Table 3

Cell-type specificity for the genes found in the MCA and the two TM datasets.

Supplementary Table 4

Number of maximal marker genes for each cell type in the MCA and the two TM datasets.

Supplementary Table 5

Number of cell-type-specific genes for each cell type in the MCA and the two TM datasets.

Supplementary Table 6

Best matches of the TM, FACS dataset from queries generated by sample variants from the index created from PubTator.

Supplementary Table 7

Best matches of the TM, FACS dataset from queries generated by sample diseases names/MeSH/OMIM IDs.

Supplementary Table 8

Best matches of the TM, FACS dataset from queries generated by sample chemical names and their corresponding IDs.

Supplementary Table 9

Best matches of the TM, FACS dataset from queries generated by sample phrases from the dictionary from the PubMed.

Supplementary Table 10

Cell-type specificity of super enhancers.

Supplementary Table 11

Cell-type-specific enhancer-gene pairs.

Supplementary Table 12

Top 20 and 30 marker genes in the three batch correction methods.

Source data

Source Data Fig. 1

Data generated with R package scfind.

Source Data Fig. 2

Data generated with R package scfind.

Source Data Fig. 3

Data generated with R package scfind.

Source Data Fig. 5

Data generated with R package scfind.

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Lee, J.T.H., Patikas, N., Kiselev, V.Y. et al. Fast searches of large collections of single-cell data using scfind. Nat Methods 18, 262–271 (2021). https://doi.org/10.1038/s41592-021-01076-9

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