Nicolas Bray

This study compares Seurat and Scanpy, finding significant differences in their scRNA-seq analysis outputs, comparable to low sequencing depth or cell coverage.

The paper describes using kallisto, bustools, and kb-python to quantify RNA-seq data from bulk, single-cell, and single-nucleus samples.

Geospatial exploratory statistics, including local, bivariate and multivariate spatial methods, are packaged for spatial omics behind one interface, with matched R/Bioconductor and Python implementations.

The paper identifies population structure in gene expression data using canonical correlation analysis between genotype and expression principal components, finding genes associated with diseases affecting African Americans.

The paper introduces a method to perform gene-level differential analysis with transcript-level resolution by aggregating transcript-level results and p-value aggregation methods, enhancing sensitivity and enabling application to perturbation analysis.

Read pairs that fail kallisto pseudoalignment because of conflicting matches are filtered by pizzly to call, assemble and quantify gene fusions from raw RNA-seq in minutes rather than hours.

The paper identifies an IL2RA enhancer associated with autoimmunity using CRISPRa and shows it affects gene activation timing without blocking expression.

The paper introduces a method for differential RNA-Seq analysis that separates biological and inferential variance using bootstrapping and response error modeling, implemented in the sleuth app.

The Lair provides tools to analyze published RNA-Seq data from the Short Read Archive, enabling accessible and extendable exploration without technical expertise.

The paper presents a probabilistic method for RNA-seq quantification that achieves near-optimal accuracy.