Luke Zappia

anndataR improves interoperability between R and Python in single-cell transcriptomics

anndataR enables interoperability between R and Python by allowing native reading and writing of H5AD files in R, with compatibility testing and conversion to R single-cell objects.

Deciphering Brain Organoids Heterogeneity by Identifying Key Quality Determinants

The paper identifies the Feret diameter as a reliable quality marker for brain organoids and shows mesenchymal cells negatively impact organoid formation.

Optimal distance metrics for single-cell RNA-seq populations

The paper evaluates distance metrics for single-cell RNA-seq data and recommends mean squared error for comparing populations and evaluating gene expression models.

Integrating single-cell RNA-seq datasets with substantial batch effects

This paper improves scRNA-seq dataset integration by introducing regularization strategies for cVAEs, enhancing batch correction while preserving biological variation.

Population-level integration of single-cell datasets enables multi-scale analysis across samples

This paper introduces scPoli, a deep learning model for integrating single-cell datasets, enabling multi-scale analysis of population-level data by linking sample and cell-level features.

Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas

This paper integrates multiple scRNA-seq datasets to create a mouse islet atlas, revealing new insights into β-cell states and differences across diabetes models.

Benchmarking atlas-level data integration in single-cell genomics

BBKNN, Scanorama, and scVI lead complex scRNA-seq integration in a 38-setup, nine-task, 14-metric benchmark; highly variable gene selection helps, whereas scaling favors batch removal over bio-conservation.

splatPop: simulating population scale single-cell RNA sequencing data

splatPop simulates population-scale scRNA-seq data with eQTL effects and batch effects for method testing.

Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape

The paper updates the scRNA-tools database, showing trends in scRNA-seq analysis tools over five years, including a shift toward integration and reference datasets.

Sfaira accelerates data and model reuse in single cell genomics

Sfaira provides a data and model zoo for single-cell RNA-seq, enabling fast reuse of pre-trained models and datasets across human and mouse tissues.

High throughput single cell RNA-seq of developing mouse kidney and human kidney organoids reveals a roadmap for recreating the kidney

The paper uses single-cell RNA-seq to analyze developing mouse kidney and human kidney organoids, identifying cell clusters and revealing insights into kidney development and organoid maturation.

Clustering trees: a visualisation for evaluating clusterings at multiple resolutions

The paper introduces clustering trees to visualize clustering results at multiple resolutions and demonstrates their use on simulated and real datasets.

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

The paper introduces the scRNA-tools database to catalog and curate scRNA-seq analysis tools, highlighting trends in tool development and open-source practices.

Splatter: Simulation Of Single-Cell RNA Sequencing Data

Splatter simulates single-cell RNA-seq data using a gamma-Poisson model, offering reproducible and well-documented simulations for multiple cell types and differentiation paths.

Gene Length And Detection Bias In Single Cell RNA Sequencing Protocols

This paper investigates gene length bias in scRNA-seq across different protocols and datasets, finding that UMI-based protocols reduce gene length bias compared to full-length transcript protocols.