Audrey Fu, PhD

Research Interests

I develop statistical methods and algorithms for a better understanding of diverse and high-dimensional data in genomics and other biomedical fields. Main developments in my group in recent years include various statistical models and machine learning algorithms for causal network inference under the principle of Mendelian randomization, deep learning algorithms for analyzing single-cell RNA-sequencing data, methods to profile types of gene regulation of gene variants, and to identify disease-relevant cell types from jointly analyzing single-cell gene expression data and summary statistics from genomewide association studies for diverse complex traits and diseases. My group actively practices open science and have distributed open-source software packages in R and Python for all my methodology works over widely used software-sharing websites such as GitHub and the Comprehensive R Archive Network (CRAN).

Mentoring

Accepting new M.S. students in 2024-2025

Accepting new Ph.D. students in 2024-2025

Selected Publications

Google Scholar publication list

1. Sater, S. H., Natividad, G. C., Seiner, A. J., Fu, A. Q., Shrestha, D., Bershad, E. M., Marshall-Goebel, K., Macias, B. R., Laurie, S. S., and Martin, B. A. (2022) MRI-based quantification of posterior ocular globe flattening during 60 days of strict 6° head-down tilt bedrest with and without daily centrifugation. Journal of Applied Physiology. 133, 1349-1355.
2. Badsha, M. B., Martin, E. A. and Fu, A. Q. (2021). MRPC: An R package for inference of causal graphs. Frontiers in Genetics. 12 632. R package: MRPC. https://cran.r-project.org/package=MRPC. (version 2)
3. Sater, S.H., Sass, A.M., Seiner, A., Natividad, G.C., Shrestha, D., Fu, A.Q., Oshinski, J.N., Ethier, C.R. and Martin, B.A. (2021). MRI-based quantification of ophthalmic changes in healthy volunteers during acute 15° head-down tilt as an analogue to microgravity. Journal of the Royal Society Interface. 18(177), p.20200920.
4. Williams, G., Thyagaraj, S., Fu, A., Oshinski, J., Giese, D., Bunck, A.C., Fornari, E., Santini, F., Luciano, M., Loth, F. and Martin, B.A. (2021). In vitro evaluation of cerebrospinal fluid velocity measurement in type I Chiari malformation: repeatability, reproducibility, and agreement using 2D phase contrast and 4D flow MRI. Fluids and Barriers of the CNS. 18(1), pp.1-15.
5. Khani, M., Fu, A.Q., Pluid, J., Gibbs, C.P., Oshinski, J.N., Xing, T., Stewart, G.R., Zeller, J.R. and Martin, B.A., (2020). Intrathecal catheter implantation decreases cerebrospinal fluid dynamics in cynomolgus monkeys. PLoS One. 15(12), e0244090.
6. Gu, T., Fu, A. Q., Bolt, M. and Zhao, X. (2020). Systematic identification of A-to-I editing associated regulators from multiple human cancers. Computers in Biology and Medicine. 119. 103690.
7. Badsha, M. B.*, Li, R.*, Liu, B.*, Li, Y. I., Xian, M., Banovich, N. and Fu, A. Q. (2020) Imputation of single-cell gene expression with an autoencoder neural network. Quantitative Biology. 1-17. doi: 10.1007/s40484-019-0192-7. *Equal contributions. Python package: LATE. https://github.com/audreyqyfu/LATE.
8. Badsha, M. B. and Fu, A. Q. (2019). Learning causal biological networks with the principle of Mendelian randomization. Frontiers in Genetics. 10 460. doi: 10.3389/fgene.2019.00460. R package: MRPC. https://cran.r-project.org/package=MRPC. (version 1)
9. Gu, T., Fu, A. Q., Bolt, M. and White, K. P. (2019). Clinical relevance of noncoding adenosine-to-inosine RNA editing in multiple human cancers. JCO Clinical Cancer Informatics. doi: 10.1200/CCI.18.00151.
10. Calderon, D., Bhaskar, A., Knowles, D. A., Golan, D., Raj, T., Fu, A. Q.* and Pritchard, J. P.* (2017). Inferring relevant cell types for complex traits by using single-cell gene expression. The American Journal of Human Genetics. 101 686-699. *Equal contributions. R package: rolypoly. https://github.com/dcalderon/rolypoly.
11. Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology. 15 6 447-471. R package: noise. https://cran.r-project.org/web/packages/noise/.
12. Wang, X.*, Fu, A. Q.*, McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. *Equal contributions. R package: cancerGI. https://cran.r-project.org/web/packages/cancerGI/index.html.
13. Fu, A. Q., Russell, S., Bray, S. and Tavaré, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7 3 1334-1361. R package: DIRECT. https://cran.r-project.org/web/packages/DIRECT/index.html.
14. Housden, B. E., Fu, A. Q., Krejci, A., Bernard, F., Fischer, B., Tavaré, S., Russell, S. and Bray, S. J. (2013) Transcriptional dynamics elicited by a short pulse of Notch activation involves feed-forward regulation by E(spl)/Hes genes. PLoS Genetics 9 1 e1003162.
15. Stojnic, R., Fu, A. Q. and Adryan, B. (2012) A graphical modelling approach to the dissection of highly correlated transcription factor binding site profiles. PLoS Computational Biology 8 11 e1002725.
16. Fu, A. Q., Genereux, D. P., Stöger, R., Burden, A. F., Laird, C. D. and Stephens, M. (2012) Statistical inference of in vivo properties of human DNA methyltransferases from double-stranded methylation patterns. PLoS ONE 7 3 e32225. R code: MethylHMM. https://github.com/audreyqyfu/MethylHMM.
17. Fu, A. Q., Genereux, D. P., Stöger, R., Laird, C. D. and Stephens, M. (2010) Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals. The Annals of Applied Statistics 4 (2) 871-892.
18. Fu, A. Q. and Adryan, B. (2009) Scoring overlapping and adjacent signals from genome-wide ChIP and DamID assays. Molecular BioSystems 5 1429-1438.
19. Sung, Y. J., Di, Y., Fu, A. Q., Rothstein, J. H., Sieh, W., Tong, L., Thompson, E. A. and Wijsman, E. M. (2007) Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric lod scores, variance components lod scores, and Bayes factors. BMC Proceedings 1 (Suppl 1):S93.
20. Sieh, W., Basu, S., Fu, A. Q., Rothstein, J. H., Scheet, P. A., Stewart, W. C. L., Sung, Y. J., Thompson, E. A. and Wijsman, E. M. (2005) Comparison of marker types and map assumptions using Markov chain Monte Carlo-based linkage analysis of COGA data. BMC Genetics 6 (Suppl 1):S11.

Honors and Recognitions

NIH Pathway to Independence Award (K99/R00; 2014-2019)

International Society for Bayesian Analysis Travel Award (2010)

Dorothy and Leon Gilford Fellowship, Department of Statistics, University of Washington (2003)