![]() ![]() g Example subnetworks (subset of significant covariations, selected functional subnetworks are highlighted).Ī miRNA targets tend to covary. Gene covariation enrichment scores indicate the ratio of observed significant covariations relative to the amount of expected covariations (see main text). Gene covariation enrichment scores (CES) for gene sets sharing the same gene ontology or sharing the same KEGG pathway annotation as well as respective controls ( p-values represent a two-sided independent two-group t-test). f Gene sets that share functional annotations are enriched for covariations. Gene names in bold indicate direct targets of Srebpf1, a transcription factor that is well known to regulate cholesterol biosynthesis. Arrows indicate the flow of metabolites, lines indicated significant covariation between genes. Only genes that were robustly detected in our sequencing data are shown. Genes involved in cholesterol biosynthesis from acetyl-CoA. e Cholesterol biosynthesis pathway is highly enriched for gene pair covariations. Green line illustrates the degree distribution of a random network with same number of genes (nodes) and covariations (edges) as the observed network. ![]() Number of significant covariations per gene against the number of genes with that number of covariations (blue points). d Gene covariation network is scale-free ( y ≈ 2.1). c Spearman’s ranked coefficients are in accordance with other covariation and dependency measures. Spearman’s ranked correlation was applied. Each data point represents their respective measurement in the same single cell. Abundances for the two genes are in reads per million (RPM) and plotted in log scale. b Covariation of the genes Ppia and Npm1. Arrows indicate at which rho-value p-values become smaller than 0.01 (rho ∼ 0.253). Value for the gene pair Npm1- Ppia is highlighted. Violin plot of Spearman’s ranked statistics (rho-value) for the entire transcriptome (blue) and for a permuted control matrix (gray). Our results lend support to the concept of post-transcriptional RNA operons, but we further present evidence that nuclear proximity of genes may provide substantial functional regulation in mammalian single cells.Ī Transcriptome-wide covariation (co-expression) values for all possible gene pairs. We find that nuclear organization has the greatest impact, and that genes encoding for physically interacting proteins specifically tend to covary, suggesting importance for protein complex formation. We provide the first evidence that miRNAs naturally induce transcriptome-wide covariations and compare the relative importance of nuclear organization, transcriptional and post-transcriptional regulation in defining covariations. These covariations form a network with biological properties, outlining known and novel gene interactions. We apply a tailored experimental design that eliminates these confounders, and report thousands of intrinsically covarying gene pairs in mouse embryonic stem cells. Single-cell RNA sequencing studies on gene co-expression patterns could yield important regulatory and functional insights, but have so far been limited by the confounding effects of differentiation and cell cycle. 8 Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.7 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.6 Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.5 Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain.4 Center for Evolution and Cancer, The Institute of Cancer Research, London, UK.3 Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden.2 Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.1 Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.
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