The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression.
View Article and Find Full Text PDFIntroduction: Clinical trials for CFTR modulators consider mean changes of clinical status at the cohort level, and thus fail to assess the heterogeneity of the response. We aimed to study the different response profiles to lumacaftor-ivacaftor according to age in children with cystic fibrosis (CF).
Methods: A mathematical framework, including principal component analysis, data clustering, and data completion, was applied to a multicenter cohort of 112 children aged 6-18 years, treated with lumacaftor-ivacaftor.