Publications
Flexible generation of genomic features sets for null hypothesis testing with bootRanges
Enrichment analysis is a widely utilized technique in genomic analysis that aims to determine if there is a statistically significant association between two sets of genomic features. To conduct this type of hypothesis testing, an appropriate null model is typically required. However, the null distribution that is commonly used can be overly simplistic and may result in inaccurate conclusions. bootRanges provides fast functions for generation of block bootstrapped genomic ranges representing the null hypothesis in enrichment analysis. As part of a modular workflow, bootRanges offers greater flexibility for computing various test statistics leveraging other Bioconductor packages. We show that shuffling or permutation schemes may result in overly narrow test statistic null distributions and over-estimation of statistical significance, while creating new range sets with a block bootstrap preserves local genomic correlation structure and generates more reliable null distributions. It can also be used in more complex analyses, such as accessing correlations between cis-regulatory elements (CREs) and genes across cell types or providing optimized thresholds, e.g. log fold change (logFC) from differential analysis.
Wancen Mu, Eric S Davis, Stuart Lee, Mikhail G Dozmorov, Douglas H Phanstiel, Michael I Love
Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes.
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I Love
List of publications
Machine learning based methods for predicting guide RNA effects on cell fitness and gene expression in CRISPR epigenomic experiments
Wancen Mu, Tianyou Luo, Alejandro Barrera, Lexi R. Bounds, Tyler S. Klann, Maria ter Weele, Julien Bryois, Gregory E. Crawford, Patrick F. Sullivan, Charles A. Gersbach, Michael I. Love, Yun Li
The tidyomics ecosystem: Enhancing omic data analyses
William J Hutchison, Timothy J Keyes, Helena L Crowell, Charlotte Soneson, Wancen Mu, Ji-Eun Park, Eric S Davis, Abdullah A Nahid, Ming Tang, Victor Yuan, Pierre-Paul Axisa, Jonathan W Kitt, Chi-Lam Poon, Noriaki Sato, Miha Kosmac, Jacques Serizay, Raphael Gottardo, Martin Morgan, Stuart Lee, Michael Lawrence, Stephanie C Hicks, Garry P Nolan, Kara L Davis, Anthony T Papenfuss, Michael I Love, Stefano Mangiola
bioRxiv
Next-generation sequencing (NGS) in metastatic colorectal cancer (mCRC): novel mutated genes and their effect on response to therapy
Federico Innocenti, Wancen Mu, Xueping Qu, Fang-Shu Ou, Omar Kabbarah, Monica Bertagnolli, Charles David Blanke, Alan P. Venook, Heinz-Josef Lenz, Naim U. Rashid
Journal of clinical oncology
Flexible generation of genomic features sets for null hypothesis testing with bootRanges
Wancen Mu, Eric S Davis, Stuart Lee, Mikhail G Dozmorov, Douglas H Phanstiel, Michael I Love
Bioinformatics 2023
Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I Love
Bioinformatics 2022