Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

MotivationA key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behavior of complex biological systems in the form of multi-level networks. Modern multiomics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet.ResultsIn this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guIded Multi-Omics Network inference approach, and benchmark their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness and noise levels, while methods implicitly handling missingness performed worst and were generally unstable. Our results show that robust multi-omics network inference with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.Availabilityhttps://github.com/cellmapslab/kimonoContactbenjamin.schubert@helmholtz-muenchen.deSupplementary informationSupplementary data are available at Bioinformatics online.

Invasion of high-grade glioma (HGG) cells through the brain and spinal cord is a leading cause of cancer death in children. Despite advances in treatment, survivors often suffer from life-long adverse effects of the toxic therapies. This study investigated the influence of nutritional ketosis on the therapeutic action of mebendazole (MBZ) and devimistat (CPI-613) against the highly invasive VM-M3 glioblastoma cells in juvenile syngeneic p20-p25 mice; a preclinical model of pediatric HGG. Cerebral implantation of the VM-M3 glioblastoma cells invaded throughout the brain and the spinal column similar to that seen commonly in children with malignant glioma. The maximum therapeutic benefit of MBZ and CPI-613 on tumour invasion and mouse survival occurred only when the drugs were administered together with a ketogenic diet (KD). MBZ reduced VM-M3 tumour cell growth and invasion when evaluated under in-vitro and in-vivo conditions through inhibition of both the glutaminolysis and the glycolysis pathways. Moreover, administration of the drugs with the KD allowed lower dosing of the juvenile mice, which minimized toxicity while improving overall survival. This preclinical study in juvenile mice highlights the potential importance of a diet/drug therapeutic strategy for managing childhood brain cancer.