Research

Overview of recent research topics, along with links to key papers and other associated resources. For more details, see Publications and Software.

Development of a scalable method to identify spatially variable genes in spatial transcriptomics data: nnSVG (Weber et al. 2023) (R/Bioconductor package)

Development of an improved computational framework for differential analyses in high-dimensional cytometry data: diffcyt (Weber et al. 2019) (R/Bioconductor package)

Collaborative analysis of single-nucleus and spatial transcriptomics data from the locus coeruleus region in postmortem human brain samples (Weber and Divecha et al. 2023)
Development of an unsupervised analysis workflow during a collaborative analysis of spatial transcriptomics data from the dorsolateral prefrontal cortex (DLPFC) region in postmortem human brain samples (Maynard and Collado-Torres et al. 2021)

Benchmark evaluation of methods for genetic variation-based demultiplexing of pooled single-cell RNA sequencing samples from tumor samples from high-grade serous ovarian cancer (HGSOC) and lung adenocarcinoma (Weber et al. 2021)

Benchmark of methods for genetic variation-based demultiplexing of pooled single-cell RNA sequencing samples from tumors (see 'Applications in cancer biology' above)
Review and best practices guidelines on performing different types of benchmarking studies (Weber et al. 2019)
Benchmark comparison of clustering methods for high-dimensional cytometry data (Weber and Robinson 2016)

Online book providing code examples and discussion on computational analysis workflows for spatial transcriptomics data using R/Bioconductor: Orchestrating Spatial Transcriptomics Analysis with Bioconductor (OSTA)
Software infrastructure for storing and manipulating spatial transcriptomics datasets within the R/Bioconductor framework: SpatialExperiment (Righelli, Weber, Crowell et al. 2022) (R/Bioconductor package)
Contributions to the development of a comprehensive analysis workflow for high-dimensional cytometry data (Nowicka et al. 2019) (R/Bioconductor package)

Development of open-source R packages through the Bioconductor project (see Software for details)
Publication of preprints (bioRxiv and arXiv)
Reproducible code and data resources (see Software and GitHub for details)
Research Symbiont award, received in January 2021 in recognition of efforts to create open code and data resources