Single-cell gene networks from co-expression
Creators: name: Crow, Megan
Publisher: name: F1000Research
Year: 2016

description: Motivation: Cellular variation is critical in almost any complex biological system, making the desirability of assessing gene networks in a cell-type specific fashion self-evident. Previously, this has only been possible in either low-throughput assays or by inference from combined data where cell-type properties filter a universal network (often protein-protein interaction). With the rise of single-cell RNA-sequencing (scRNA-seq) we are now able to construct high-throughput gene networks derived from data at cellular resolution, enabling the discovery of pathways that are active in individual cells. <br> <br>Background: Biology has increasingly looked to relationships between genes to explain phenotypic variability. One way to determine these groupings is from transcriptional data; genes with similar expression patterns are thought to be involved in the same cellular pathway or function. This can be generalized into co-expression networks, which are built from the assessment of gene-gene relationships across sources of variation. Co-expression networks have become an important resource in the interpretation of gene function and disease, and the comparison of networks built from diverse datasets enables characterization of condition-specific gene modules. Until recently, this type of analysis could only be performed by using bulk tissue or by pooling thousands, or even millions of cells, meaning that individual cell variation could not be assessed. This has changed with the increasing availability and cost-effectiveness of scRNA-seq. Using this technology, researchers have begun to grapple with the heterogeneity of gene expression in single cells, primarily leveraging this information to define previously unknown cell subtypes. Co-expression remains relatively uncharted territory.<br> <br>Meta-analysis: We have performed the first major analysis of single-cell co-expression using data from 31 separate studies comprising 163 individual cell-types [1]. Sampling from more than 200 bulk RNA-seq datasets as a control, we assessed the functional connectivity of both bulk and single cell networks by measuring how well we could predict gene ontology (GO) functions using neighbor voting in cross-validation. We will present results demonstrating that single-cell network connectivity is less likely to overlap with known functions than co-expression derived from bulk data, and that outlier functionality is never observed in any of the single cell networks. Most interestingly, assessing single-cell data in which cell-type was held constant in each network showed little decrease in performance, suggesting that gene sets varying from cell-to-cell within a cell-type are similar to those that vary from cell-type to cell-type. <br> <br>Wet-lab validation: To complement this analysis, we performed our own technically controlled scRNA-seq experiment using genetically targeted mouse interneuron classes to determine features and analysis practices that contribute to functional connectivity. Chandelier cells and parvalbumin-positive fast-spiking basket cells were prepared in known batches to generate co-expression networks for each. This allowed us to take the same meta-analytic approach we took to cross-laboratory comparison to characterization of technical properties within our data. We will present our results, showing that the use of raw or batch-corrected UMI data and post-co-expression network standardization provided the highest degree of network replicability, semantic similarity and overall functional connectivity. Importantly, we found that gene expression levels can be highly predictive of network topology, and that this can influence functional connectivity. To demonstrate this, we will walk through a re-analysis of the BrainSpan RNA-seq data, indicating that previously reported age-related variation of disease gene connectivity shows this dependency. <br> <br>Conclusions: Single-cell RNA-seq data offers both promise and perils. Technical properties create confounds which can only be avoided with care and specific controls. Comparison with previous data in meta-analysis offers particular benefit for single-cell co-expression and we have made our data readily accessible. This can be used for both improving co-expression performance and for cross-laboratory comparison within a common framework.<br><br>References<br>[1] Crow M, Paul A, Ballouz S, Huang Z J, Gillis J. Exploiting single-cell expression to characterize co-expression replicability. Genome Biology. 2016; Accepted.