These tutorials cover the essentials of performing co-expression network analysis in single-cell transcriptomics data, and visualizing the key results.
This tutorial covers the essential functions to construct a co-expression network in single-cell transcriptomics data with hdWGCNA.
This tutorial covers the essential functions to construct a co-expression network in spatial transcriptomics data with hdWGCNA.
This tutorial highlights several approaches for visualizing the hdWGCNA co-expression networks.
These tutorials will provide further biological context for our co-expression modules, potentially revealing what experimental conditions and biological processes that these modules are involved in.
This tutorial covers how to compare module eigengenes between experimental groups.
This tutorial covers how to correlate continuous and categorical variables with module eigengenes or module expression scores, revealing which modules are related to different experimental conditions or covariates.
This tutorial shows how to use Enrichr to compare the gene members of each co-expression module to curated gene lists, thereby pointing towards the biological functions of the co-expression modules.
This tutorial covers how to project co-expression modules from a reference to a query dataset.
This tutorial covers statistical methods for assessing the preservation and reproducibility of co-expression networks using external datasets.
This tutorial covers how to project co-expression modules from a reference to a query dataset for special cases where the data modality or the species do not match between the reference and the query.