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Comparative analysis of algorithms for integration of copy number and expression data

Nature Methods volume 9, pages 351–355 (2012)Cite this article

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Abstract

Chromosomal instability is a hallmark of cancer, and genes that display abnormal expression in aberrant chromosomal regions are likely to be key players in tumor progression. Identifying such driver genes reliably requires computational methods that can integrate genome-scale data from several sources. We compared the performance of ten algorithms that integrate copy-number and transcriptomics data from 15 head and neck squamous cell carcinoma cell lines, 129 lung squamous cell carcinoma primary tumors and simulated data. Our results revealed clear differences between the methods in terms of sensitivity and specificity as well as in their performance in small and large sample sizes. Results of the comparison are available at http://csbi.ltdk.helsinki.fi/cn2gealgo/.

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Figure 1: Expression and copy-number values in simulated samples.
Figure 2: Sensitivity, specificity and MCC scores for all methods in simulated and real datasets.

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Acknowledgements

This work was supported by the Academy of Finland (grants 125826 and 255523 (S.H.), and 218022 (O.M.)), Biocentrum Helsinki, Helsinki Biomedical Graduate School (R.L.), The Finnish Cancer Organizations and the Sigrid Jusélius Foundation.

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Authors and Affiliations

  1. Research Programs Unit, Genome-Scale Biology and Institute of Biomedicine, Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Riku Louhimo, Tatiana Lepikhova, Outi Monni & Sampsa Hautaniemi

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  1. Riku Louhimo
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  2. Tatiana Lepikhova
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Contributions

R.L. developed the analysis pipeline, implemented all algorithms and analyzed data. R.L. and S.H. designed the experiments and analyzed results. T.L. performed the qRT-PCR experiments and compiled the ground truth gene list with R.L. R.L. and S.H. wrote the manuscript. O.M. provided the array CGH and gene expression data on HNSCC samples. R.L. and T.L. wrote validation parts of Online Methods.

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Correspondence to Sampsa Hautaniemi.

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The authors declare no competing financial interests.

Supplementary information

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Supplementary Figures 1–4, Supplementary Table 2, Supplementary Note, Supplementary Results (PDF 1608 kb)

Supplementary Table 1

Sensitivity, specificity and MCC scores for all algorithms in all datasets. (XLS 11 kb)

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Louhimo, R., Lepikhova, T., Monni, O. et al. Comparative analysis of algorithms for integration of copy number and expression data. Nat Methods 9, 351–355 (2012). https://doi.org/10.1038/nmeth.1893

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