Lukas M. Weber

smoothclust: Spatial smoothing and boundary-density summaries for spatial transcriptomics

smoothclust provides a framework for spatial domain analysis in spatial transcriptomics, including a clustering workflow and a boundary density metric to summarize tissue architecture.

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

The paper introduces nnSVG, a scalable method for identifying spatially variable genes using nearest-neighbor Gaussian processes, and demonstrates its performance on experimental data and simulations.

Deconvolution reveals compositional differences in high-grade serous ovarian cancer subtypes

The paper uses deconvolution on bulk transcriptomic data to show that HGSOC subtypes are driven by tumor microenvironment composition, particularly fibroblast and immune cell content.

distinct: a novel approach to differential distribution analyses

distinct identifies differential distribution patterns in single-cell data by comparing empirical cumulative distribution functions, outperforming existing methods in detecting subtle changes.

The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

The paper characterizes the molecular landscape of the human locus coeruleus using spatially-resolved transcriptomics and single-nucleus RNA-sequencing, providing a publicly accessible resource.

VistoSeg: processing utilities for high-resolution Visium/Visium-IF images for spatial transcriptomics data

VistoSeg processes high-resolution Visium/Visium-IF images to integrate image-derived metrics with spatial gene expression data for spatial transcriptomics analysis.

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

This study evaluates how experimental factors affect the performance of tumor deconvolution algorithms using bulk RNA-seq data.

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

spatialLIBD provides an R/Bioconductor package for interactive visualization of spatially-resolved transcriptomics data from the 10x Genomics Visium platform.

SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor

SpatialExperiment provides a data infrastructure for spatially resolved transcriptomics data in R/Bioconductor, with examples from Visium and seqFISH platforms.

Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design

The paper evaluates genetic demultiplexing tools for pooled single-cell RNA-seq in cancer, showing they work effectively despite challenges from ambient RNA.

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

miQC is a probabilistic framework that jointly models mitochondrial DNA and gene detection metrics to improve single-cell RNA-seq quality control.

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

The paper maps spatial gene expression in the human dorsolateral prefrontal cortex using spatial transcriptomics and identifies layer-specific gene expression patterns linked to neuropsychiatric disorders.

diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

diffcyt identifies differential cell populations in high-dimensional cytometry data using high-resolution clustering and empirical Bayes tests.

Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data

The paper compares clustering methods for high-dimensional flow and mass cytometry data, finding several methods effective with FlowSOM being fast and suitable for large datasets.