Refining multivariate and modeling approaches
During our meeting Friday, I realized I interpreted the data analysis task incorrectly. Instead of combining CpG methylation status and genomic location information to use for multivariate and modeling approaches, I needed to consider the data separately!
Remaking M. capitata tracks
Before I could revise my analysis, Hollie pointed out that the M. capitata gene track did not have the correct amount of genes in this issue. In addition to using genes with the AUGUSTUS tag, I needed to pull genes that had the GeMoMa tag (from a different related species). In this Jupyter notebook, I matched multiple patterns with
grep to revise the gene, CDS, and intron tracks.
#Match both AUGUSTUS and GeMoMa gene annotations !grep -e "AUGUSTUS gene" -e "GeMoMa gene" Mcap.GFFannotation.gff > Mcap.GFFannotation.gene.gff
I used these revised tracks to create my three flank tracks and intergenic region track. Since the files have the same name, I didn’t need to update my IGV sessions. Instead, I uploaded the revised tracks to
gannet so they would populate IGV.
Now that I had revised tracks, I needed to go back to my Jupyter notebook and characterize M. capitata feature overlaps! In this Jupyter notebook I characterize CG motif overlaps with the revised tracks. Then, I characterized overlaps between individual samples and feature tracks. I did the same thing in this Jupyter notebook for 5x union data. For the union data, I recreated CpG genomic location stacked barplots with this R Markdown script.
Figure 1. *CpG genomic location.
Looking at my output, it no longer looks like MBD-BS is no longer the best for capturing gene regions. Since I ran this code quickly and late at night, I need to double check that this stacked barplot is trustworthy.
Revised statistical analyses
Now, the big stuff. I took the output for individual samples and recreated my summary tables in this R Markdown script. Based on feedback from Friday, I ran individual NMDS, ANOSIM, and model sets for the CpG methylation status and genomic location datasets. Similar to the methylation status models, I ran individual models for each genome feature since all the proportions add up to 1. I included replicate information as a random effect.
I ran my analyses and questions by Evan, and he suggested I switch over from NMDS and ANOSIM to PCoA and perMANOVA. Apparently, the creators of the
vegan package recommend this. The perMANOVA has a more robust statistical output format as well. I kept my centered log-ratio transformation and euclidean distance matrix, but fed it into
cmdscale to run a PCoA instead. I investigated the PCs, eigenvalues, and loadings. I then used
adonis to run a global perMANOVA. To understand if a significant perMANOVA result was due to centroid or variance differences between groups, I ran a beta dispersion model. A non-significant result indicates that differences are due to centroids, not variance. After my global perMANOVA, I ran pairwise perMANOVA test to understand the significant global perMANOVA result. Similar to when I was running ANOSIM, I ended up with complete enumeration. However, it was easier to interpret the perMANOVA output and see how much variance the test explained. After running my regressions, I extracted p-values and corrected them using a false discovery rate.
Although most of my questions are addressed, I still have two more. When running the beta dispersion model for my P. acuta data, I found that group variances were significantly different from eachother! My guess is that sample 8 is an influential point or outlier that is contributing to variance differences between groups. I need to investigate this further. While I didn’t need to make any changes to the models (so nice!), the models are set up in a way that compares WGBS to RRBS and MBD-BS and not compare the two enrichment methods. I’m not sure which post-hoc tests I need to use to compare these two methods. I knit the output into this document, emailed Evan, and posted my follow-up questions here. The end is (hopefully) near!
- Finalize analysis
- Check that union stacked barplots were generated correctly
- Update methods
- Update results
- Update figures
- Look into program for mCpG identification