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There are many different kinds of "gene regulatory networks", so how to model them with gene expression data depends on what you're trying to model and what type of regulatory mechanisms you want to study. Pick a random gene in the human genome and use a resource database like StringDB. Let's take the TET1 protein for example. Some edges in that ...


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This is an incomplete answer since it's not my field of expertise, but it sounds like Weighted correlation network analysis is what you are referring to. There is a paper which describes how it can be applied to gene expression data. Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008). ...


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I've done sub-clustering a few times on my Seurat data sets. The approach I take is to subset the clusters that need to be clustered (i.e. using subset), carry out a clustering of only those cells, then transfer the subcluster labels back to the original dataset. Here's some rough code, which will need to be modified for your specific situation and code ...


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Increase the clustering resolution parameter to generate more (smaller) clusters, see FindClusters in the Seurat docs. Whether or not this will neatly, split your clusters into subclusters depends on your data, but normally one can easily separate CD4 and NK cells from PBMCs. See also the Clustree approach for determining the optimal resolution. ...


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