Manual Clustering, Continued

Alright, some updates from last time:

  • Rather than setting the number of modules independently for each individual crab/transcriptome, I specified a single cut height, which was used on the dendrogram for each crab to separate into modules. For crabs A-F (where three time points are present), this cut height was 1.8, and was chosen because it generally separated crabs into 5-8 modules, all of which appeared to contain single expression patterns. Crabs G, H, and I were part of the elevated-temperature treatment group, which only had 2 time points, and so that cut height just wasn’t adequate. As a result, for these crabs, I bumped up the cut height to 10. These changes can be seen in scripts 71-73. All are the same except for initial inputs and module naming but here’s one as an example

  • I changed my name scheme for the manual clustering method. Previously, each timepoint was described with HML (high, medium, or low), so a module with three timepoints could be MMH (if expression was medium on Day 0, medium on Day 2, and high on Day 17). However, we decided to get a bit more general. Now, expression is grouped into overall expression patterns. There are four possibilities - high to low (HTL), low to high (LTH), high-low-high (HLH), and low-high-low (LHL). You’ll notice this groups together, say, modules that previously would have been called HLL and HHL together as HTL. As a result, there’s some duplication in our module names, so if there are two LTH modules, the first will be called LTH and the second is LTH2.

  • I was able to run this manual clustering method for cbai_transcriptomev2.0 on Mox after installing the pheatmap package! It required me to build a new Singularity container - description of how here and an example here

Beyond that, not too much progress was made on the manual clustering (most of my time was spent just working on stuff for classes, although I found some interesting stuff - coming to you in a blog post soon!) Next steps on this are still fairly similar to what they used to be:

  • Examine output of my manual clustering
    • Compare modules to see what the main expression patterns were
    • Compare genes between crabs to see if there’s overlap
  • Try using the methods from this paper - GitHub repo here - to try to do some visualization and analyze expression with GO terms
  • Again, go back through WGCNA output and see if there’s anything good to glean from there