Where are they now?

**tldr**
![bar](http://eagle.fish.washington.edu/cnidarian/skitch/Screenshot_3_13_15__9_20_AM_1AB34D8C.png)

---

In an effort to find out where heat stress induced differentially methylated loci are in the oyster genome (to ultimately inform on function) I have been using `bedtools` to see where the DMLs lie on the genome. As this was done on an array platform I also felt I need to take into consideration where probes were, noting that they were not randomly distributed across the genome but rather targetted to genes. 

I have determined the proportion of DMLs (n=10028, 10148, 11690) for each oyster that fall within a given genomic feature and compared that to the proporiton of total probes (n=697753) that fall within each genomic feature. For example in just looking at Oyster 2 DMLs and DEGs ... 

```
!intersectbed \
-wb \
-a ./data/2014.07.02.colson/genomeBrowserTracks/logFC_HS-preHS/2014.07.02.2M_sig.bedGraph \
-b /Users/sr320/data-genomic/tentacle/Cuffdiff_geneexp.sig.gtf \
| cut -f 6 \
| sort | uniq -c 
!intersectbed \
-wb \
-a /Users/sr320/git-repos/paper-Temp-stress/ipynb/data/array-design/OID40453_probe_locations.gff \
-b /Users/sr320/data-genomic/tentacle/Cuffdiff_geneexp.sig.gtf \
| cut -f 11 \
| sort | uniq -c 
```
output:  
880 Cufflinks    
117460 Cufflinks

```
# Enter the data comparing Oyster 2 then Probes
obs = array([[880, 10028], [117460, 697753]])

# Calculate the chi-square test
chi2_corrected = stats.chi2_contingency(obs, correction=True)
chi2_uncorrected = stats.chi2_contingency(obs, correction=False)

# Print the result
print('CHI SQUARE')
print('The corrected chi2 value is {0:5.3f}, with p={1:5.3f}'.format(chi2_corrected[0], chi2_corrected[1]))
print('The uncorrected chi2 value is {0:5.3f}, with p={1:5.3f}'.format(chi2_uncorrected[0], chi2_uncorrected[1]))
```
output: 

CHI SQUARE      
The corrected chi2 value is 352.138, with p=0.000     
The uncorrected chi2 value is 352.654, with p=0.000

~ [jupyter notebook](http://nbviewer.ipython.org/github/sr320/paper-Temp-stress/blob/master/ipynb/Array-feature-overlap-04.ipynb)


---

To be honest I feel like I am missing some nuance in the analysis, however at this point I believe I will keep pushing through by seeing of the results break out based on whether the DML is hypo or hypermethylated. If you forgot hear is the breakdown.

Oyster | Hypo-methylated | Hyper-methylated | Hypo-3plus-merged | Hypo-3plus-merged
--- | --- | --- | --- | ---
2 | 7224 | 2803 | 108 | 4
4 | 6560 | 3587 | 48 | 10
6 | 7645 | 4044 | 53 | 9

This also sheds light on the fact that I am currently ignoring clustering (3-plus), something else to put on the list!