Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

Abstract

High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are studyspecific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin β8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Tregspecific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells.

Prediction of Protein−compound Binding Energies from Known Activity Data: Docking‐score‐based Method and its Applications

“…But this method requires natural language analysis, which is an expensive approach. One possible improvement of the present method might be batch effect correction . That is, we could determine some optimal artificial values as the experimental parameters, such as the densities of ligands and substrates, in order to minimize the prediction error.…”

Dendritic cells require TMEM176A/B ion channels for optimal MHC II antigen presentation to naive CD4+ T cells

“…Tmem176a and Tmem176b are homolog genes encoding structurally similar four-span transmembrane proteins 6,10,16 (Figure 1A). In the immune system, our previous studies 6,10,17 combined with the analysis of Immuno-Navigator 18 and Immgen 19 public databases (Figure S1) indicate that Tmem176a and Tmem176b are tightly co-regulated and highly expressed both in conventional DCs and in the RORgt + cell family (depicted in Figure 1B). Taken together, these observations along with reported evidence of genetic compensation in Tmem176b −/− single KO mice 6 strongly suggested the need to simultaneously target both genes to decipher their function.…”

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

“…These unwanted factors can render the data uninterpretable in relation to its metadata, in the sense that they introduce additional covariates into the data (38,39,50). If these unwanted effects are not removed, estimated associations between features after pooling data from multiple studies may be fallacious (27,43,54). Thus, before pooling expression data from heterogeneous studies, the effects of unwanted factors must be removed, and samples/studies that have unwieldy noise must be eliminated from the analysis.…”

“…Genes with significant association may share common biological roles and pathways (26,55,59). Genes with significant association only under specific conditions may reveal their functional significance under those conditions (54,60). MOG provides the researcher with several statistical measures to estimate associations/coexpression between the features/biomolecules.…”