Process Methylation Data to Individual Objects

[wilcas]

In selection of other probes, there remains the issue of how to deal with data in expanding the number of mediation events detected. Ideally, I'd be using one nice LV method to reduce data down to 1 dimension, but it seems from simulation that this fails to capture the associations between that data and a target (i.e. more LVs are necessary).

I have a few suggestions as to how to continue/what to do to try to deal with this:

• Pick representative probes/peaks of highly correlated blocks
• Break down probes/peaks into multiple LVs and try a multi-target regression version of CIT instead of using linear models
• Represent probes/peaks with single LV anyways

In each of the above cases I need to decide:

1. How to group every feature by gene (fixed windows around TSS should be good enough)
2. Whether or not it's valuable to pre-select sets of SNPs to group and re-do transcriptome/genome wide QT-LV study

3. How I justify how the method accounts for complex non-linear interaction between omics data

   EDIT

   For now I'm going to group probes/peaks based on the gene they associate with within a window. Then I am going to take these top (bonferroni corrected threshold) QTMs/eQTAs and summarize them by LV.

To do this I will need to:

• [x] run an eQTM analysis
• [x] run an eQTA analysis
• [ ] modify my existing code to run this new embedded CIT analysis, should be as simple as getting the input file right (modifying the CIT.txt-like file) and changing my get mediator functions

[wilcas]

Considerations

For now I am going to test probes/peaks and their association to a given gene's expression whose TSS is within 1 MB of that probe/peak.

• In eQTLs, we see a sharp drop off in association toward the ends of this window, I should verify this for eQTMs and eQTAs
• Trouble finding literature that talks about this in very explicit terms, might need to read entire papers, lets make sure I'm clear