Elizabeth A. Purdom

The paper identifies a 'double-dipping' issue in scRNA-Seq where cell-type clustering and phenotype testing are performed on the same data, leading to inflated false discovery rates, and proposes solutions to mitigate this problem.

The paper investigates the impact of post-selection inference in scRNA-Seq phenotype testing and proposes batch correction to mitigate false discovery rate inflation.

The paper introduces spatialFDA, a framework for analyzing differential co-localisation in spatial omics data, showing its effectiveness in controlled simulations and biological applications.

This paper introduces a statistical framework for disease classification using scRNA-seq data, achieving better interpretability and performance compared to existing methods.

This study uses single-cell RNA sequencing to trace the developmental trajectory of olfactory epithelium in mice, identifying Ascl1+ cells as multipotent progenitors that become transcriptionally primed over time.

TESSERA is a method for analyzing multi-sample spatial transcriptomics data, enabling differential expression analysis while accounting for spatial correlation and sample-specific factors.

This study identifies hundreds of drought-responsive genes in sorghum using multi-season transcriptomic data from field experiments.

This study identifies a latent activated stem cell state in olfactory epithelium using single-cell transcriptomic and epigenomic profiling during injury-induced regeneration.

The paper introduces GloScope, a framework for representing scRNA-Seq data at the sample level to enable visualization and quality control across population-scale studies.

Dune improves replicability of cell type discovery in scRNA-Seq by optimizing the trade-off between cluster resolution and cross-dataset consistency.

Cobolt integrates multi-modality single-cell data by providing a framework for joint analysis of gene expression and chromatin accessibility.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

The paper introduces mbkmeans, a fast clustering tool for large single-cell datasets using mini-batch k-means, showing improved performance over standard k-means on datasets up to 1.3 million cells.

The paper identifies non-neuronal cell types in the olfactory system expressing SARS-CoV-2 entry genes, suggesting indirect mechanisms for COVID-19-associated anosmia.

MethCP detects differentially methylated regions in WGBS data using change point models, outperforming existing methods in accuracy and sensitivity for both two-group comparisons and time-course data.

This paper evaluates normalization methods for scRNA-seq using a framework called scone, showing that top methods improve agreement with validation data.

The paper introduces clusterExperiment, a Bioconductor package for clustering large gene expression datasets using Resampling-based Sequential Ensemble Clustering (RSEC) to identify biologically meaningful subtypes.

Slingshot builds a cluster-level minimum spanning tree to find branching lineages without fixing their number, then fits simultaneous principal curves for stable per-lineage pseudotimes.