MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data

Abstract

Supplementary data are available at Bioinformatics online.

Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations

“…As analyses, such as WGCNA, are optimally suited to identify correlated networks, but do not identify direct gene-to-gene interactions or potential causal links, we used miic analysis (Sella et al, 2018;Verny et al, 2017), recently extended to analyze continuous or mixed-type (e.g., continuous-categorical) data (Cabeli et al, 2020). The miic algorithm identifies direct paths with high confidence, and, as such, reveals potential cause-and-effect links as well as latent regulators, which may not show up when looking only at gene expression data.…”

“…As the Set 3 genes specified the perivascular population corresponding to the major cell cluster A we took advantage of the large number of Leptin receptor-positive (LEPR) single cells to refine the V gene network. To identify direct paths between genes including causal relationships and inferring latent common regulators of expressed genes we applied the multivariate information-based inductive causation algorithm (miic) (Sella et al, 2018;Verny et al, 2017), submitting to it the matrix consisting of 1,712 observations and 109 variables (the Set 3 genes). As expected only a fraction of nodes (52) defining direct high-confidence paths were retained ( Figure 4C).…”

“…We used the miic algorithm which combines constraint-based and information-theoretic frameworks to reconstruct causal and non-causal networks from large scale datasets (Verny et al, 2017;Sella et al, 2018). Miic network predictions belong to the broad class of 'ancestral graphs' that include undirected, directed as well as bi-directed edges originating from latent common causes unobserved in the available dataset and represented by dashed edges in the networks of Figure 4C and Figure 4D.…”

“…We applied a discriminant analysis pipeline to identify the genes predominantly expressed by the different cell sets and analyzed the different cell populations to unravel the gene relatedness (Horvath, 2011;Langfelder and Horvath, 2008). Moreover, we took advantage of the numerous observations provided by recently published single-cell transcriptomes (Tikhonova et al, 2019) to disclose in the perivascular network the direct gene-to-gene interactions and reveal oriented links based on the signature of causality (Sella et al, 2018;Verny et al, 2017). Finally, as proof of concept, we studied the protein expression and activity of the top most discriminant and connected gene, the Wnt signaling pathway facilitator R-spondin 2 (Rspo2), and validated that it was expressed by a small set of perivascular cells, and acted in concert with the Stem Cell Factor (SCF) (also known as Kit Ligand) to amplify ex vivo hematopoietic precursors.…”

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells

“…Gene ontology analysis was done using the human genome as background (Table S1). Functional gene network reconstruction was achieved using the information-theoretic method, MIIC (multivariate information-based inductive causation, Supplementary Information 1 [58,65]). Four independent datasets comprising 153 to 485 primary GBM transcriptomes were used for patient survival analysis (Table S1).…”