Lorenzo Martini

ATLAS: a scverse-compatible package for multi-omic single-cell trajectory inference integration

ATLAS integrates single-cell RNA-seq and ATAC-seq data to improve trajectory inference by jointly analyzing transcriptomic and chromatin accessibility information.

Integrative Comparison of GeneHancer and Single-Cell Co-accessibility Reveals Active Enhancer-Gene Interactions

This paper integrates GeneHancer annotations with single-cell chromatin accessibility data to identify high-confidence enhancer-gene interactions specific to a human PBMC dataset.

Spike Train Scalograms (STS): a Deep Learning Classification Pipeline for Neuronal Cell Types

The paper introduces a deep learning pipeline using wavelet transforms to classify neuronal cell types from electrophysiological data with high accuracy.

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

This paper presents a bioinformatic workflow for analyzing transcriptomic and epigenomic single-cell data separately and jointly to better understand cellular heterogeneity.

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

This paper investigates the correlation between transcription factor expression and motif information in functional genomic regions to understand TF dynamics across cell types.

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

GRAIGH integrates GeneHancer database with scATAC-seq data to interpret chromatin accessibility and detect cellular heterogeneity by identifying cell-type-specific enhancer-gene associations.

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM.

This paper introduces a meta-analysis framework to study the contributions of the Gene Activity Matrix and its correlation with gene expression using the GAGAM model.

GAGAM: a genomic annotation-based enrichment of scATAC-seq data for Gene Activity Matrix

This paper introduces GAGAM, a method to enhance scATAC-seq data interpretation by linking peaks to genes through genomic annotations, improving interpretability over traditional Gene Activity Matrices.

Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

The paper evaluates the performance of inhibitory interneuron markers in scRNA-seq datasets and finds that marker quality correlates with dataset reliability.