tldr
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
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]))
CHI SQUARE The corrected chi2 value is 352.138, with p=0.000 The uncorrected chi2 value is 352.654, with p=0.000
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!
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