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Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

Abstract

The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains challenging. We developed pathway and gene set overdispersion analysis (PAGODA) to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

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Figure 1: Overview of PAGODA.
Figure 2: PAGODA analysis of data from 3,005 mouse cortical and hippocampal cells5.
Figure 3: Transcriptional heterogeneity of 65 NPCs in embryonic mouse cortex.

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References

  1. Islam, S. et al. Nat. Methods 11, 163–166 (2014).

    CAS  Article  PubMed  Google Scholar 

  2. Picelli, S. et al. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  PubMed  Google Scholar 

  3. Tang, F. et al. PLoS ONE 6, e21208 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Usoskin, D. et al. Nat. Neurosci. 18, 145–153 (2015).

    CAS  Article  PubMed  Google Scholar 

  5. Zeisel, A. et al. Science 347, 1138–1142 (2015).

    CAS  Article  PubMed  Google Scholar 

  6. Buettner, F. et al. Nat. Biotechnol. 33, 155–160 (2015).

    CAS  Article  PubMed  Google Scholar 

  7. Macosko, E.Z. et al. Cell 161, 1202–1214 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Klein, A.M. et al. Cell 161, 1187–1201 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Patel, A.P. et al. Science 344, 1396–1401 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Grün, D., Kester, L. & van Oudenaarden, A. Nat. Methods 11, 637–640 (2014).

    Article  PubMed  Google Scholar 

  11. Buettner, F. & Theis, F.J. Bioinformatics 28, i626–i632 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. van der Maaten, L.J.P. & Hinton, G.E. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  13. Jaitin, D.A. et al. Science 343, 776–779 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Subramanian, A., Kuehn, H., Gould, J., Tamayo, P. & Mesirov, J.P. Bioinformatics 23, 3251–3253 (2007).

    CAS  Article  PubMed  Google Scholar 

  15. Blaschke, A.J., Staley, K. & Chun, J. Development 122, 1165–1174 (1996).

    CAS  PubMed  Google Scholar 

  16. Rehen, S.K. et al. Proc. Natl. Acad. Sci. USA 98, 13361–13366 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Peterson, S.E. et al. J. Neurosci. 32, 16213–16222 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Herr, K.J., Herr, D.R., Lee, C.W., Noguchi, K. & Chun, J. Proc. Natl. Acad. Sci. USA 108, 15444–15449 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Nat. Methods 11, 740–742 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Pollen, A.A. et al. Nat. Biotechnol. 32, 1053–1058 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Kawaguchi, A. et al. Development 135, 3113–3124 (2008).

    CAS  Article  PubMed  Google Scholar 

  22. Kriegstein, A., Noctor, S. & Martinez-Cerdeno, V. Nat. Rev. Neurosci. 7, 883–890 (2006).

    CAS  Article  PubMed  Google Scholar 

  23. Lein, E.S. et al. Nature 445, 168–176 (2007).

    CAS  Article  PubMed  Google Scholar 

  24. Englund, C. et al. J. Neurosci. 25, 247–251 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Uetsuki, T., Takagi, K., Sugiura, H. & Yoshikawa, K. J. Biol. Chem. 271, 918–924 (1996).

    CAS  Article  PubMed  Google Scholar 

  26. Minamide, R., Fujiwara, K., Hasegawa, K. & Yoshikawa, K. PLoS ONE 9, e84460 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Huang, Z., Fujiwara, K., Minamide, R., Hasegawa, K. & Yoshikawa, K. J. Neurosci. 33, 10362–10373 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Anderson, S.A., Eisenstat, D.D., Shi, L. & Rubenstein, J.L. Science 278, 474–476 (1997).

    CAS  Article  PubMed  Google Scholar 

  29. Wonders, C.P. & Anderson, S.A. Nat. Rev. Neurosci. 7, 687–696 (2006).

    CAS  Article  PubMed  Google Scholar 

  30. Ma, T. et al. Cereb. Cortex 22, 2120–2130 (2012).

    Article  PubMed  Google Scholar 

  31. Anders, S. & Huber, W. Genome Biol. 11, R106 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Fisher, R.A. Statistical Methods for Research Workers (Hafner, 1970).

  33. Abdel, H.E. Encyclopedia of Environmetrics 2nd edn (Wiley, 2012).

  34. Hasings, C., Mosteller, F., Tukey, J.W. & Winsor, C.P. Ann. Math. Stat. 18, 413–426 (1974).

    Article  Google Scholar 

  35. Bailey, S. Publ. Astron. Soc. Pac. 124, 1023 (2012).

    Article  Google Scholar 

  36. Johnstone, I.M. Ann. Stat. 29, 295–327 (2001).

    Article  Google Scholar 

  37. Benjamini, Y. & Hochberg, Y. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  38. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Achim, K. et al. Nat. Biotechnol. 33, 503–509 (2015).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank D. Usoskin, P. Ernfors and S. Linnarsson for helpful comments on the analysis approach. This work was supported by an Ellison Medical Foundation award and a US National Science Foundation (NSF) CAREER award (NSF-14-532) to P.V.K., an NSF graduate research fellowship (DGE1144152) to J.F., and US National Institutes of Health (NIH) grants U01 MH098977 (to K.Z. and J.C.) and NIH R01 NS084398 (to J.C.). G.E.K. was supported by NIH grant T32 AG00216.

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Authors

Contributions

K.Z., J.C. and P.V.K. conceived the study. N.S., R.L., G.E.K., Y.C.Y., F.K. and J.-B.F. carried out the single-cell purification and RNA-seq measurements. G.E.K. and J.C. carried out RNAscope in situ validation. J.F. and P.V.K. designed and implemented the statistical analysis approach, with the help of J.L.H. P.V.K. and J.F. wrote the manuscript with the help of J.C. and K.Z.

Corresponding author

Correspondence to Peter V Kharchenko.

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Competing interests

N.S. and F.K. are a current employees and shareholders of Illumina, Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Notes 1–3 (PDF 9354 kb)

Supplementary Software

Source code: SCDE R Package (ZIP 1862 kb)

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Fan, J., Salathia, N., Liu, R. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods 13, 241–244 (2016). https://doi.org/10.1038/nmeth.3734

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