Examining sample clustering

Something Shelly brought up at the end of last quarter is how odd my sample clustering is. Previously, I compared dendograms and PCA plots for my samples using different mincov settings for methylKit. Of the settings I used, mincov = 3 produced the best clustering and PCA output:

Figures 1-2. Dendogram and PCA plots for C. virginica gonad sequence data using mincov = 3.

She suggested I revist these plots to see if I could improve clustering by changing my alignment stringency in bismark. HJ mentioned looking at SNP data may also help explain my poor clustering. Looking at these plots again, I see that O1 is farther from the other treatment samples in the PCA, and very separated in the dendogram. This sample also had the lowest mapping efficiency. I decided to see what happened to clustering if I removed that sample before looking into different alignments or SNPs.

Figures 3-4. Dendogram and PCA plots for sequence data, omitting sample 1.

Without sample 1, the clustering in the PCA looked a bit better. The red samples are from the control treatment, while the blue samples are the high pCO₂ treatment. It could be that there’s no coordinated methylation response to ocean acidification, or that alignment stringency or SNPs are affecting clustering. I have to do some more digging.

Going forward

1. See how alignment stringency or SNPs affect clustering
2. Determine if a formal gene enrichment is necessary
3. If necessary, select the most appropriate gene enrichment method
4. Describe functions of most interesting genes with DML and DMR