Maria Carilli

Estimating cis and trans contributions todifferences in gene regulation

The paper introduces a framework to determine cis or trans regulation contributions to gene expression differences between strains or species and applies it to yeast, human-chimpanzee hybrid, and mouse data.

Hybrid crosses reveal a cell-type-specific landscape of mouse regulatory variation

The paper uses hybrid crosses to analyze regulatory variation in mouse tissues, finding cell-type-specific trans-acting effects and highlighting the limitations of bulk tissue analysis.

The Rayleigh Quotient and Contrastive Principal Component Analysis II

This paper extends contrastive PCA by introducing spatial and functional variants, applying them to analyze gene expression in cancer and immune response to vaccination.

Foundation Models Improve Perturbation Response Prediction

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.

Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation

The paper characterizes transcriptomes across 8 tissues in 8 mouse strains to identify expression variation linked to genetic differences and phenotypic traits.

The Rayleigh Quotient and Principal Component Analysis I

This paper introduces rho PCA, a method that improves upon contrastive PCA by approximating the Rayleigh quotient, offering better accuracy and efficiency for dimension reduction in genomics data.

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression

The paper detects viral sequences in single-cell data to identify novel viruses affecting host gene expression in rhesus macaques.

Spectral neural approximations for models of transcriptional dynamics

This paper introduces neural network approximations for solving steady-state distributions in multi-species transcriptional models, enabling efficient inference of biophysical parameters from transcriptomic data.

Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data

The paper introduces Monod, a Python package that integrates single-cell RNA sequencing data using biophysical models of transcription to distinguish biological and technical variation and reveal regulatory processes.