False Discovery Rates in Biological Networks

Lu Yu, Tobias Kaufmann, Johannes Lederer
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:163-171, 2021.

Abstract

The increasing availability of data has generated unprecedented prospects for network analyses in many biological fields, such as neuroscience (e.g., brain networks), genomics (e.g., gene-gene interaction networks), and ecology (e.g., species interaction networks). A powerful statistical framework for estimating such networks is Gaussian graphical models, but standard estimators for the corresponding graphs are prone to large numbers of false discoveries. In this paper, we introduce a novel graph estimator based on knockoffs that imitate the partial correlation structures of unconnected nodes. We then show that this new estimator provides accurate control of the false discovery rate and yet large power.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-yu21a, title = { False Discovery Rates in Biological Networks }, author = {Yu, Lu and Kaufmann, Tobias and Lederer, Johannes}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {163--171}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/yu21a/yu21a.pdf}, url = {https://proceedings.mlr.press/v130/yu21a.html}, abstract = { The increasing availability of data has generated unprecedented prospects for network analyses in many biological fields, such as neuroscience (e.g., brain networks), genomics (e.g., gene-gene interaction networks), and ecology (e.g., species interaction networks). A powerful statistical framework for estimating such networks is Gaussian graphical models, but standard estimators for the corresponding graphs are prone to large numbers of false discoveries. In this paper, we introduce a novel graph estimator based on knockoffs that imitate the partial correlation structures of unconnected nodes. We then show that this new estimator provides accurate control of the false discovery rate and yet large power. } }
Endnote
%0 Conference Paper %T False Discovery Rates in Biological Networks %A Lu Yu %A Tobias Kaufmann %A Johannes Lederer %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-yu21a %I PMLR %P 163--171 %U https://proceedings.mlr.press/v130/yu21a.html %V 130 %X The increasing availability of data has generated unprecedented prospects for network analyses in many biological fields, such as neuroscience (e.g., brain networks), genomics (e.g., gene-gene interaction networks), and ecology (e.g., species interaction networks). A powerful statistical framework for estimating such networks is Gaussian graphical models, but standard estimators for the corresponding graphs are prone to large numbers of false discoveries. In this paper, we introduce a novel graph estimator based on knockoffs that imitate the partial correlation structures of unconnected nodes. We then show that this new estimator provides accurate control of the false discovery rate and yet large power.
APA
Yu, L., Kaufmann, T. & Lederer, J.. (2021). False Discovery Rates in Biological Networks . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:163-171 Available from https://proceedings.mlr.press/v130/yu21a.html.

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