Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.

ClustAll: An R package for patient stratification in complex diseases.

In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce ClustAll, a Bioconductor package designed for unsupervised patient stratification using clinical data. ClustAll is based on the previously validated methodology ClustAll, a clustering framework that effectively handles intricacies in clinical data, including mixed data types, missing values, and collinearity.

Reviewability and supportability: New complementary principles to empower research software practices.

In today's scientific landscape, research software has evolved from being a supportive tool to becoming a fundamental driver of discovery, particularly in life sciences. Beyond its roots in software engineering, research software now plays a crucial role in facilitating efficient data analysis and enabling the exploration of complex natural phenomena. The advancements in simulations and modeling through research software have significantly accelerated the pace of scientific research while reducing associated costs.

Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.

Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes.

A comparative analysis of blastoid models through single-cell transcriptomics.

Pluripotent-stem-cell-derived blastocyst-like structures (blastoids) offer insights into early human embryogenesis (5-10 days post-fertilization). The similarity between blastoids and human blastocysts remains uncertain. To investigate, we evaluated single-cell RNA sequencing (scRNAseq) data from seven blastoid models, comparing them to peri-implantation blastocysts.