Alright, at this point, I’ve got a few different projects going. First, I’m examining gene expression within crabs infected with Hematodinium (using Grace Crandall’s samples). Second, I’m working on modeling a few decades of survey data on Hematodinium infection rates from the Alaska Department of Fish and Game (I’ve been meaning to write up a lab notebook post on that, will do it soon).
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Manually Clustering Immune Genes
Previously, we created files of transcripts that matched GO terms related to immune response. However, to make our steps more logically consistent, we recently changed those files to only include transcripts that matched the specific GO term (GO:0006955) for Immune Response. We created files for each of our three transcriptomes - cbai_transcriptomev2.0 (unfiltered), cbai_transcriptomev4.0 (bairdi only), and hemat_transcriptomev1.6 (hematodinium only). That was done using this script
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Immune Genes
A week or so ago, we decided to investigate a possible alternative path. Until now, we have generally been examining all genes. However, we decided that it may be interesting to look specifically at a subset in detail. Precisely, we decided to examine expression of immune genes among all individual libraries to see if we could spot any patterns.
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Manual Modules 4
Back in mid-July, I manually created modules to cluster genes by expression patterns. Again, “manual” oversells it a bit - I really just provided a cut height, looked at the resulting clusters, and then named them by overall expression patterns. A recap of that naming system can be found in my lab notebook entry on 2021-07-12 (named Manual Modules 3)
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Crab Plasticity
In our previous post, we created PCAs for each transcriptome, with each point as one library. This raised some interesting questions, notably that we didn’t necessarily see libraries from the same crab clustered together (though for some crab, they did clearly cluster). This could be an indication of crab plasticity, as they adjust to both tank effects and the temperature treatment.
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