Raphaël Gottardo

An eicosanoid-enriched follicular microenvironment shapes germinal centre immune dynamics in antiretroviral-treated PLWH

The paper identifies eicosanoid-enriched follicular microenvironments in HIV-infected individuals on antiretroviral therapy that may influence antibody responses and viral reservoir persistence.

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

Neutralization activity in chronic HIV infection is characterized by a distinct programming of follicular helper CD4 T cells

The paper identifies distinct follicular helper CD4 T cell programming in HIV-positive individuals who produce broadly neutralizing antibodies, linking it to improved B cell follicle architecture and reduced suppressor cells.

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Phoenix predicts spatially resolved single-cell gene expression from histology, identifying new biomarkers and validating them across cancers and treatment regimens.

From Transcripts to Cells: Dissecting Sensitivity, Signal Contamination, and Specificity in Xenium Spatial Transcriptomics

The paper evaluates Xenium spatial transcriptomics performance, identifies technical limitations, and introduces SPLIT to improve signal purity and cell-type resolution.

Deciphering spatial heterogeneity of patient-derived organoids of colorectal tumors for drug discovery

This study develops a platform to analyze spatial heterogeneity in colorectal tumor organoids using spatial transcriptomics and validates it against single-cell and tumor spatial data.

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.

Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

The paper identifies a prognostic infiltration zone in glioblastoma using histology and spatial transcriptomic integration, revealing specific cell composition associated with long-term survival.

KIR3DL1 and Tox identify clonally expanded encephalitogenic neuron-specific CD8+ T cells in autoimmune encephalitis

The paper identifies clonally expanded neuron-specific CD8+ T cells in anti-Ri AIE patients using an unbiased approach and shows they express KIR3DL1 and TOX.

Follicular Bcl6 reactivity is associated with a unique immune landscape and spatial transcriptome in COVID-19

The paper analyzes lung-draining lymph nodes from COVID-19 autopsies to identify distinct follicular Bcl6 reactivity subgroups and their associated immune and spatial transcriptomic profiles.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets

ADTnorm normalizes and integrates CITE-seq datasets to remove batch effects and improve cell-type separation across 13 public datasets.

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

The paper compares three transcriptomic methods on FFPE tumor samples to assess their ability to detect inter- and intra-patient heterogeneity.

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.

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.

The Immune Signatures Data Resource: A compendium of systems vaccinology datasets

The paper introduces the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets for comparative vaccine response analysis.

Individual Level Differential Expression Analysis for Single Cell RNA-seq data

The paper introduces IDEAS, a method for differential expression analysis in scRNA-seq data at the individual level, finding neurogranin-expressing neurons and ERBB signals in microglia are associated with autism.

New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

FAUST is a new interpretable machine learning method for single-cell cytometry that discovers and validates T cell correlates of response to anti-PD-1 immunotherapy.

An innate immune activation state prior to vaccination predicts responsiveness to multiple vaccines

The paper identifies a pre-vaccination transcriptional state linked to pro-inflammatory genes that predicts higher antibody responses to multiple vaccines.

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

BayesSpace enhances spatial transcriptomics resolution and clustering, revealing tissue structures undetectable at lower resolutions.

Integrated analysis of multimodal single-cell data

Weighted-nearest neighbor (WNN) analysis in Seurat v4 learns per-cell modality weights to define cell states, enabling a 228-antibody human PBMC CITE-seq reference for RNA-only query mapping.

Superscan: Supervised Single-Cell Annotation

Superscan is a supervised machine learning model for single-cell RNA-seq cell type annotation, showing improved accuracy and speed over existing methods.

Single-cell analysis of chromatin silencing programs in developmental and tumor progression

This paper develops scCUT&Tag to profile H3K27me3 in single cells, distinguishing cell types in blood and identifying PcG activity changes in brain tumors before and after treatment.

Orchestrating Single-Cell Analysis with Bioconductor

This paper provides an overview of single-cell RNA sequencing analysis using Bioconductor, highlighting its tools and methods for handling high-dimensional data.

Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

The paper reproduces and extends findings from Ntranos et al., showing that MAST with Sidak p-value aggregation outperforms multivariate logistic regression on experimental scRNA-seq data.

cytometree: a binary tree algorithm for automatic gating in cytometry analysis

cytometree is an automated flow cytometry gating algorithm that outperforms existing methods in accuracy and speed.

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.