Background Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal. Results We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression. Conclusion Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.

The dermoscopic features described herein help the clinician to distinguish MCC from other benign and malignant red nodules. Increasing recognition of the presenting features will facilitate earlier diagnosis of MCC and reduced mortality.

Severe cutaneous adverse reactions (SCARs) encompass a heterogeneous group of delayed hypersensitivity reactions, which are most frequently caused by drugs. Our understanding of several aspects of SCAR syndromes has evolved considerably over the previous decade. This review explores evolving knowledge on the immunopathogenic mechanisms, pharmacogenomic associations, in-vivo and ex-vivo diagnostics for causality assessment and medication cross-reactivity data related to SCAR syndromes. Given the rarity and severity of these diseases, multidisciplinary collaboration through large international, national and/or multicentre networks to collect prospective data on patients with SCAR syndromes should be prioritized. This will further enhance a systematised framework for translating epidemiological, clinical, and immunopathogenetic advances into preventive efforts and improved outcomes for patients.

No abstract