Strategies to learn about a gene of interest from single-cell RNA-seq data
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19 months ago

Using a large public single-cell RNA-seq dataset from brain where cells are already segregated by brain region, cell type, marker gene cluster, etc. I am looking to do exploratory analyses to learn whatever I can about a candidate gene of interest. What kinds of strategies are recommended? Here are some I thought of already.

  1. Run co-expression analysis (WGCNA) on clusters or subclasses (25); identify modules/hubs that include the gene of interest; run Gene Ontology enrichment analyses, compare ontologies across clusters/subclasses

  2. Identify the marker genes of each cluster using Seurat; can also find conserved markers across all clusters/subclasses

  3. Gene regulatory network reconstruction; Identify regulons containing the gene of interest, figures with iRegulon or Cytoscape

  4. Trajectory analysis - how does the gene of interest's expression change over cell regulatory/developmental trajectories?

Any ideas are appreciated!

scRNA-seq RNA-seq single-cell • 446 views

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