Helena Lucia Crowell

Analysis of multicellular anatomical structures from spatial omics data using sosta

The paper introduces a method for analyzing multicellular anatomical structures from spatial omics data and provides an open-source tool for this purpose.

A transcriptional map of human tonsil architecture:beyond the sum of (single cell) parts

The paper uses spatial molecular imaging data to map human tonsil architecture and provides an analytical framework for Spatial Immunology.

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.

On feature selection to disentangle cell type and state transcriptional programs

The paper evaluates feature selection strategies to disentangle cell type and state effects in single-cell data, aiming to improve differential testing consistency between clustering and neighborhood-based approaches.

Differential detection workflows for multi-sample single-cell RNA-seq data

The paper develops a workflow for differential detection analysis in single-cell RNA-seq data and shows it provides complementary information to differential expression analysis.

In Situ Inference of Copy Number Variations in Image-Based Spatial Transcriptomics

This paper adapts copy number variation inference to image-based spatial transcriptomics data to map malignant clones and tumor microenvironment at single-cell resolution.

Tracing colorectal malignancy transformation from cell to tissue scale

This paper traces colorectal cancer progression by integrating spatial transcriptomics, single-nucleus RNA-sequencing, and histopathology to map cellular and molecular changes across tumor stages.

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

STAMP enables low-cost, high-throughput single-cell analysis by integrating transcriptomics and proteomics imaging, supporting both single and multimodal profiling of diverse cell types.

The tidyomics ecosystem: Enhancing omic data analyses

The paper introduces tidyomics, an ecosystem bridging Bioconductor and tidy R to streamline omic analysis using data from the Human Cell Atlas.

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.

Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability

This paper meta-analyzes single-cell method benchmarks to identify challenges in extensibility, interoperability, and reproducibility.

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.

Built on sand: the shaky foundations of simulating single-cell RNA sequencing data

This paper evaluates synthetic scRNA-seq data generators, finding they often introduce artificial effects and fail to accurately mimic real data for method comparisons.

CellMixS: quantifying and visualizing batch effects in single cell RNA-seq data

CellMixS quantifies and visualizes batch effects in scRNA-seq data by developing a cell-specific mixing score and comparing metrics for their performance.

On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

This paper develops a simulation platform and analysis tools for multi-condition differential state analysis in scRNA-seq data, comparing cell-level mixed models and pseudobulk approaches.

Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma

This study identifies Hand2 as a key regulator of mesothelium progenitors in zebrafish and shows its reactivation in mesothelioma, linking developmental processes to disease.

Channel crosstalk correction in suspension and imaging mass cytometry

The paper introduces a bead-based compensation workflow for mass cytometry to correct channel crosstalk, improving data quality and panel development.