1 citations · 3 across the 8 of their papers we have counts for
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The permuted score test for robust differential expression analysis
Timothy Barry, Ziang Niu, Eugene Katsevich +1
Negative binomial (NB) regression is a popular method for identifying differentially expressed genes in genomics data, such as bulk and single-cell RNA sequencing data. However, NB…
Doubly robust and computationally efficient high-dimensional variable selection
Abhinav Chakraborty, Jeffrey Zhang, Eugene Katsevich
Variable selection can be performed by testing conditional independence (CI) between each predictor and the response, given the other predictors. A doubly robust and powerful optio…
Saddlepoint approximations for plug-in resampling
Ziang Niu, Zhihan Huang, Jyotishka Ray Choudhury +1
Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computational cost can be prohibitive. We recognize that several…
Reconciling model-X and doubly robust approaches to conditional independence testing
Ziang Niu, Abhinav Chakraborty, Oliver Dukes +1
Model-X approaches to testing conditional independence between a predictor and an outcome variable given a vector of covariates usually assume exact knowledge of the conditional di…
Exponential family measurement error models for single-cell CRISPR screens
Timothy Barry, Kathryn Roeder, Eugene Katsevich
CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbatio…
Filtering the rejection set while preserving false discovery rate control
Eugene Katsevich, Chiara Sabatti, Marina Bogomolov
Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the International Classification of Diseases (ICD), the directed a…
Multilayer Knockoff Filter: Controlled variable selection at multiple resolutions
Eugene Katsevich, Chiara Sabatti
We tackle the problem of selecting from among a large number of variables those that are 'important' for an outcome. We consider situations where groups of variables are also of in…