Massimo Andreatta

Evaluating cell type annotations in single-cell omics in the absence of ground truth

This paper introduces ISC, a framework to evaluate cell type annotations in single-cell RNA-seq without ground truth, revealing reproducibility gaps and enabling tool benchmarking.

Cell type composition drives patient stratification in single-cell RNA-seq cohorts

The paper evaluates methods for patient stratification in scRNA-seq cohorts and finds that cell-type composition-based approaches outperform complex methods in stratification performance and computational efficiency.

Metabolic reinvigoration of NK cells by IL-21 enhances immunotherapy against MHC-I deficient solid tumors

This paper shows that IL-21-Fc reprograms NK cell metabolism to enhance their function against MHC-I deficient tumors, leading to tumor eradication in preclinical models.

Wounding triggers invasive progression in human basal cell carcinoma

This study shows that wounding, such as biopsy, triggers invasive progression in human basal cell carcinoma by activating a wound response gene program and reprogramming cancer-associated fibroblasts.

ECM Signatures Reveal Quiescent Stem Cell Diversity in the Colonic Niche

The paper identifies quiescent stem cell subtypes in the colon using ECM gene signatures and shows their presence in colorectal cancer and pancreas.

Semi-supervised integration of single-cell transcriptomics data

This study introduces STACAS v2, a semi-supervised method for integrating scRNA-seq data that outperforms existing methods by preserving biological variability using cell type annotations.

scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

scGate automates marker-based purification of cell populations from single-cell RNA-seq data, outperforming existing methods in benchmark tests.

Projecting single-cell transcriptomics data onto a reference T cell atlas to interpret immune responses

The paper introduces ProjecTILs, a method to project scRNA-seq data onto T cell reference atlases to interpret immune responses and perturbations.

UCell: robust and scalable single-cell gene signature scoring

UCell provides a fast and robust method for scoring gene signatures in single-cell data using the Mann-Whitney U statistic.

STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

STACAS improves integration of single-cell RNA-seq datasets with partial cell type overlap by correcting batch effects and filtering anchors.

NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T cell epitope predictions

NNAlign_MA deconvolves poly-specific MHC peptidome data to accurately characterize binding motifs and improve T cell epitope predictions.

Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes

The paper describes computational approaches for analyzing immunopeptidomics data, focusing on motif identification, false hit filtering, and machine learning applications for predicting MHC ligands.

NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

NetMHCpan-4.0 improves peptide-MHC class I interaction predictions by integrating binding affinity and eluted ligand data, enhancing identification of naturally processed ligands and T cell epitopes.