Peter S. Carbonetto

Torino, a reference-free framework for isoform discovery, identifies RNA processing patterns linked to disease risk by analyzing read coverage variation across species and disease states.

The paper introduces immgenT-GP, a gene-program framework that identifies 200 reproducible gene programs in mouse T cells, capturing lineage identity, activation states, and tissue location.

The paper investigates whether persistent inflammation in Crohn's disease is due to environmental triggers or intrinsic epithelial changes by comparing organoid and tissue data from patients in remission.

The paper introduces mvSuSiE, a multi-trait fine-mapping method that improves causal SNP identification by jointly analyzing multiple traits, discovering more causal SNPs and narrower credible sets compared to single-trait approaches.

SuSiE 2.0 improves genetic fine-mapping and phenotype prediction with faster performance, better calibration, and new methods like SuSiE-ash.

fSuSiE improves fine-mapping of QTLs by modeling spatial genetic effects on molecular traits, achieving higher resolution in identifying causal variants and their molecular targets.

The paper identifies TCR-V gene usage patterns in T cells sorted by anti-TCR mAbs using single-cell RNAseq and finds enriched ERV1-family LTR expression in specific TRBV regions.

The paper examines T cell changes in mammary glands during pregnancy and lactation, finding an expansion of T effector cells and similarities to intestinal T cells.

The paper modifies the SuSiE model to improve calibration in small-sample studies, reducing false positives in genetic fine-mapping.

This paper introduces GBCD to decompose tumor transcriptional heterogeneity in scRNA-seq data, identifying a new prognostic gene expression program in pancreatic cancer.

The paper constructs a cell-type-resolved chromatin accessibility atlas in the human intestine and identifies regulatory programs and genetic variants associated with Crohn's disease.

The paper introduces an efficient algorithm for dimensionality reduction of single-cell RNA-seq data, showing it outperforms existing methods in speed and quality.

This paper introduces GoM DE, a differential expression analysis method that allows cells to have partial membership in multiple groups, improving the interpretation of structure in single-cell sequencing data.

The paper introduces a flexible empirical Bayes method for multivariate multiple regression to improve prediction of multi-tissue gene expression from genotypes.

This paper extends the SuSiE model to work with summary data, showing it is competitive with existing methods in speed and accuracy for fine-mapping genetic variants.

SuSiE writes sparse regression coefficients as a sum of single-effect vectors; its IBSS algorithm returns a credible set per effect and outperformed existing fine-mapping methods in numerical experiments.

The paper introduces flexible statistical methods for genomic studies with multiple conditions, improving power and effect estimates by accounting for arbitrary correlations in effect sizes.