Romain Lopez

The paper introduces CellMAPP and CHART to analyze multiplexed antibody imaging data, revealing modular morphological programs in response to genetic perturbations.

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

The paper introduces a framework for multimodal chemical screens combining RNA, protein, and epigenomic readouts, revealing compound-specific mechanisms and off-target effects in T cells.

PRESAGE predicts perturbation expression responses using gene embeddings and outperforms existing methods in functional evaluations.

SpatialAgent integrates large language models with dynamic tool retrieval, multimodal interpretation, and claim verification to automate end-to-end spatial biology workflows from gene-panel design to tissue annotation and hypothesis generation.

This paper develops a degron-modified Cas12a system to enable accurate single-cell detection of pre-crRNAs, enhancing Perturb-seq capabilities for multiplex guide expression.

This paper introduces SC-VAE, a supervised contrastive framework for analyzing cell perturbation data, showing improved sensitivity in detecting perturbation effects compared to existing methods.

This paper introduces an active learning method to efficiently design Perturb-seq experiments by leveraging multi-modal priors, improving perturbation space exploration with fewer experiments.

The paper introduces a contrastive analysis framework to disentangle perturbation effects from natural cell variation in optical pooled screens, showing improved performance in generating embeddings and reducing technical artifacts.

The paper introduces lvm-DE, a Bayesian method for differential expression analysis in scRNA-seq using deep generative models, improving log fold change estimation and differential gene detection.

The paper introduces multiGroupVI, a deep generative model for analyzing grouped scRNA-seq data, and shows it outperforms existing methods on simulated data and identifies regional differences in mouse small intestine data.

The paper introduces DestVI, a method for multi-resolution spatial transcriptomics analysis that captures continuous cell state variation, and applies it to study inflammation in lymph nodes and tumors.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

The paper introduces totalVI, a probabilistic framework for joint analysis of RNA and protein data from single cells, demonstrating its utility in integration, dimensionality reduction, clustering, and differential expression testing.

This paper introduces scVI and scANVI, deep generative models for integrating single-cell RNA-seq data and annotating cell states, showing improved accuracy and scalability over existing methods.

AutoZI detects gene-specific zero-inflation in single-cell RNA sequencing data using a spike-and-slab prior and variational inference, outperforming existing methods on simulated and real data.

The paper introduces totalVI, a probabilistic framework for analyzing CITE-seq data by jointly modeling RNA and protein measurements, addressing technical noise and enabling batch correction.

Scrublet detects scRNA-seq doublets by simulating doublets from random cell pairs and scoring cells by simulated-doublet neighbor fraction, reaching AUC 0.99 on human-mouse mixtures.

scVI maps cells into a low-dimensional latent space with variational inference and neural networks, scaling past a million cells while absorbing batch effects and sensitivity differences.