Research interests:

In general, I am interested in developing statistical models and efficient computational methods for genetic and genomic data, with the goal of tackling important problems in human genetics and diseases. Ongoing projects include:

Development of network (or graph) inference methods for genomic data in order to better understand gene regulation. Our approaches include machine learning methods and Bayesian methods. The former can learn networks efficiently, whereas the latter enable estimation of probabilities of edge presence and direction.
Development of deep learning algorithms for imputation and other inference problems in single-cell gene expression data. Such data often contains a large amount of zeros (which may be viewed as missing values) and is further complicated by nonlinear relationships among genes and multiple cell types.

Past projects include:

Application of a regression model and survival analysis methods to integrate high-throughput RNAi double knockdown data with diverse genomic and clinical data from The Cancer Genome Atlas (TCGA) consortium. We inferred a map of interactions for frequently mutated genes in breast cancer, and explored its topological properties, implications for gene regulation and impact on the survival of cancer patients.
A Bayesian clustering method and associated Markov Chain Monte Carlo (MCMC) algorithm for efficiently clustering time-course gene expression data;
Bayesian hierarchical models and associated MCMC algorithms for DNA methylation data;
A hidden Markov model for studying identity-by-descent in sibling pairs while accounting for linkage disequilibrium.

Publications:
Google Scholar

Software:
baycn: An R package for Bayesian inference of causal networks with a prior network, designed mainly for individual-level genotype and molecular phenotype data. See GitHub for the developer version of the package.
MRPC: An R package for inference of causal networks using a machine learning approach, designed mainly for individual-level genotype and molecular phenotype data. See GitHub for the developer version of the package.
LATE: A Python package for imputing zeros (treated as missing values) in single-cell RNA-sequencing data.
noise: An R package for estimation of intrinsic and extrinsic noise from gene expression data from single-cell two-reporter experiments.
cancerGI: An R package for analyses of cancer gene interactions, using RNAi knockdown data, as well as data from the TCGA consortium.
DIRECT: An R package for Bayesian clustering of multivariate data under the Dirichlet-process prior.
MethylHMM: A collection of R and C code for inference under hidden Markov models for double-stranded DNA methylation data.

People:
Current:
Mohamed Megheib: Ph.D, George Washington University. Postdoc Research Fellow.
Jarred Kvamme: Ph.D student in The Graduate Program in Bioinformatics and Computational Biology. Bandita Karki: MS student in Statistics. Former: (First position after leaving the lab)
Md Bahadur Badsha: Ph.D, Kyushu Institute of Technology, Japan. Postdoc Research Fellow. (Sera Prognostics, Inc.)
Rui Li: Ph.D, Washington State University. Postdoc Research Fellow. (University of Massachusetts Medical School)
Evan Martin: Ph.D student in The Graduate Program in Bioinformatics and Computational Biology. (Pacific Northwest National Laboratory)

Teaching:
Fall 2020: STAT 431 Statistial Analysis
Spring 2021: STAT 550 Regression
Fall 2020: STAT 565 Computer Intensive Methods
Spring 2020: STAT 431 Statistial Analysis
Spring 2020: STAT 550 Regression
Spring 2018: STAT 431 Statistial Analysis
Spring 2018: STAT 550 Regression
Fall 2016: STAT 431 Statistial Analysis
Fall 2016: BCB 501 Research Seminar
Fall 2015: STAT 431 Statistial Analysis

Awards:

NIH Pathway to Independence Award (K99/R00), NIH/NHGRI, 2014-2019.
International Society for Bayesian Analysis Travel Award, 2010.
Dorothy and Leon Gilford Fellowship, Department of Statistics, University of Washington, 2003.

Email:
audreyf at uidaho dot edu

Mailing Address:
UI-Department of Statistical Science, University of Idaho
875 Perimeter Dr. MS 1104, Moscow, ID 83844-1104