Patrick J. Danaher

Breast cancer through the lens of whole transcriptome spatial imaging

The paper uses spatial transcriptomics to analyze a breast tumor, identifying spatially distinct biological processes and potential therapeutic targets.

Deep profiling of lupus nephritis kidneys reveals dynamic changes in myeloid cells associated with disease progression

This study uses single-cell RNA-sequencing and spatial transcriptomics to analyze myeloid cell dynamics in lupus nephritis, showing shifts in immune responses linked to disease progression.

Accounting for Spatial Correlation in Graphical Analysis of SpatialTranscriptomics Data

The paper introduces SpaceDecorr, a method to reduce false-positive correlations in spatial transcriptomics data by adjusting for spatial dependencies and technical artifacts.

FastReseg: using transcript locations to refine image-based cell segmentation results in spatial transcriptomics

FastReseg improves cell segmentation accuracy in spatial transcriptomics by using transcriptomic data to correct image-based segmentation errors.

Community assessment of methods to deconvolve cellular composition from bulk gene expression

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

Differential Expression Analysis for Spatially Correlated Data

The paper addresses challenges in differential expression analysis of spatial transcriptomics data by mitigating segmentation errors and accounting for spatial correlation, reducing false discoveries.

MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

The paper introduces a GAN-based method to generate high-quality membranes for cells with poor staining in fluorescent microscopy images, improving segmentation accuracy.

InSituCor: a toolkit for discovering non-trivial spatial correlations in spatial transcriptomics

InSituCor identifies non-trivial spatial correlations in spatial transcriptomics data by filtering out correlations explained by cell type landscapes.

An information theoretic approach to detecting spatially varying genes

This paper introduces Maxspin, an information-theoretic method for detecting spatially varying genes, showing improved accuracy across spatial transcriptomics platforms and simulations.

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Insitutype types cells in spatial transcriptomics with a likelihood model over every expression value, admits image and neighbourhood priors, and is reported to cluster millions of cells quickly.

Background modeling, Quality Control and Normalization for GeoMx RNA data with GeoDiff

The paper introduces a statistical model-based workflow for quality control and normalization of GeoMx RNA data, outperforming conventional methods in reducing background noise and technical variability.

High-plex Multiomic Analysis in FFPE at Subcellular Level by Spatial Molecular Imaging

An automated in-situ imaging platform reads 980 RNAs and 108 proteins in FFPE tissue at subcellular resolution, with 0.0092 false calls per cell; 800,000-plus cells released publicly.

Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data

SpatialDecon quantifies cell populations in spatially-resolved gene expression data, showing spatially heterogeneous immune responses in lung tumors.