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Identification of spatial expression trends in single-cell gene expression data

Nature Methods volume 15, pages 339342 (2018) | Download Citation

Abstract

As methods for measuring spatial gene expression at single-cell resolution become available, there is a need for computational analysis strategies. We present trendsceek, a method based on marked point processes that identifies genes with statistically significant spatial expression trends. trendsceek finds these genes in spatial transcriptomic and sequential fluorescence in situ hybridization data, and also reveals significant gene expression gradients and hot spots in low-dimensional projections of dissociated single-cell RNA-seq data.

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Acknowledgements

This work was supported by the Swedish Research Council (grant 2017-01062 to R.S.), the European Research Council (grant 648842 to R.S.) and the Bert L. and N. Kuggie Vallee Foundation (R.S.).

Author information

Affiliations

  1. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

    • Daniel Edsgärd
    • , Per Johnsson
    •  & Rickard Sandberg
  2. Ludwig Institute for Cancer Research, Stockholm, Sweden.

    • Daniel Edsgärd
    • , Per Johnsson
    •  & Rickard Sandberg

Authors

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Contributions

D.E. conceived the idea, developed the method, performed the analyses and wrote the manuscript. P.J. performed seqFISH and clustering analyses. R.S. supervised the project and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Rickard Sandberg.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Number of significant genes identified with the different spatial trend metrics

  2. 2.

    Supplementary Table 2

    List of genes with significant spatial expression patterns in spatial transcriptomics data from mouse olfactory bulb (replicate 3)

  3. 3.

    Supplementary Table 3

    List of cells with significant spatial expression patterns in spatial transcriptomics data from mouse olfactory bulb (replicate 3)

  4. 4.

    Supplementary Table 4

    List of genes with significant spatial expression patterns in spatial transcriptomics data from mouse olfactory bulb (replicate 12)

  5. 5.

    Supplementary Table 5

    List of cells with significant spatial expression patterns in spatial transcriptomics data from mouse olfactory bulb (replicate 12)

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    Supplementary Table 6

    List of genes with significant spatial expression patterns in spatial transcriptomics data from breast cancer (layer 2)

  7. 7.

    Supplementary Table 7

    List of cells with significant spatial expression patterns in spatial transcriptomics data from breast cancer (layer 2)

  8. 8.

    Supplementary Table 8

    List of genes with significant spatial expression patterns in dissociated single-cell RNA-seq data from mouse embryo

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    Supplementary Table 9

    List of cells with significant spatial expression patterns in dissociated single-cell RNA-seq data from mouse embryo

  10. 10.

    Supplementary Table 10

    List of genes with significant spatial expression patterns in seqFISH data from mouse hippocampus

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    Supplementary Table 11

    List of cells with significant spatial expression patterns in seqFISH data from mouse hippocampus

Zip files

  1. 1.

    Supplementary Software

    trendsceek package

About this article

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DOI

https://doi.org/10.1038/nmeth.4634