“…The single-sample networks predicted by LIONESS provide a way to unite (i) the extensive literature and methodologies for estimating complex network relationships using genomic data with (ii) statistical analysis techniques that use sample-level information to model heterogeneity and compare phenotypic groups. Applications of LIONESS have included analyzing the yeast cell cycle (Kuijjer et al, 2019a), studying biological processes in lymphobastoid cell lines (Kuijjer et al, 2019a), investigating the relationship between the host transcriptome and nasal microbiome in infants with respiratory syncytial virus infection (Sonawane et al, 2019), investigating microbiome co-occurrence in respiratory infections (Mac Aogáin et al, 2021), identifying regulatory processes associated with brain cancer survival (Lopes-Ramos et al, 2021) and with sexual dimorphism in colorectal cancer chemotherapy response (Lopes-Ramos et al, 2018), characterizing sex differences in twenty-nine tissues from the Genotype-Tissue Expression Project (Lopes-Ramos et al, 2020), and identifying tissue-specific regulatory processes in maize (Fagny et al, 2020). LIONESS has also been integrated into a method to identify cancer driver genes (Pham et al, 2021).…”