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==Analysis of BSPP data from Geographic Astrophy (GA) patients== *96 blood samples from cases and controls were provided by Kang Zhang's group. *Noi performed the BSPP capture and the sequencing was done in BioGem (110929_SN1001) ===Summary statistics on sequencing, mapping and CpG methylation calling=== Noi should fill in this part. ===Regression analysis=== *The working directory is on genome-miner /home/kunzhang/mQTL/KangZhang_GA *One data set (2200001) was removed due to low capture efficiency and low sequencing depth. *Methylation levels from the rest of 95 samples (turned out to be 94) were combined into a matrix (up to 20% missing values was allowed): allBED2Matrix.pl BED 76 1 0.1 > KangZhang_GA_BSPP_95_methylationMatrix.txt & *I ended up getting a matrix containing 136,417 variable CpG sites. *I wrote a Perl Script to perform linear regression on five factors: disease status, sex, age, BMI, smoking: [[Media:GA_methylation_regression.txt|GA_methylation_regression.pl]]; [[Media:phenotype_info.txt|Phenotype Info file]]. ./GA_methylation_regression.pl KangZhang_GA_BSPP_95_methylationMatrix.txt > KangZhang_GA_BSPP_95_methylation_regression.txt & *Sites that might be significantly contributed by one of these factors were reported. **I used a stringent cutoff of 3.5 for the t-statistics on reporting the data in individual files (allowed for more detailed analysis in R). **The test statistics for all the tested sites were also reported in a separate file (KangZhang_GA_BSPP_95_methylation_regression.txt). **If I simply use a cutoff for |t-value| of 2.5 (equivalent to p=0.014), then I can count how many Cpg sites are significantly correlated with each factor. <b>Factor # sites</b> GA 1515 Sex 2718 Age 2310 BMI 1861 Smoking 2002 GA+Age 249 (somewhat expected as age is a major contributor of GA) GA+Sex 53 GA+BMI 28 GA+Smoking 34 It's important to note that no correction for multiple testing was done here. Even with the least stringent FDR correction, no site is significant. An independent estimation of the false positive rate is to repeat the analysis on randomly permutated data, and ask how many sites appear significant based on the same criteria. I did this by randomly shuffling the methylation levels for a CpG sites among the samples. <b>Factor # sites</b> GA 1979 Sex 1855 Age 1913 BMI 2201 Smoking 1816 Therefore we got close to 2000 significant sites even with randomly permutated data, which suggests that the majority of the sites found in this regression analysis are false positive. An exception is probably Age, which is correlated with more Cpg sites in non-permutated data. To further test the possibility that there are some true signals among the false positives, I performed pathway enrichment analysis with GREAT on the sites significantly correlated with GA. For the sites identified in the non-permutated data set, I found 45 enrichment pathways or categories. In contrast, with the sites identified in the randomly permutated data set, no enrichment was found. ===Methylation marker selection=== *In this analysis, I tried to select a minimal subset of CpG sites that can classify the patients from the healthy controls at the greatest power. *Since there are too many sites in the data set, I decided to filter out most of the non-informative sites first, then use a feature selection method to identify a small subset of most informative markers. I did this with a [[Media:GA_methylation_MRMR_prep.txt|Perl script]] and [http://penglab.janelia.org/proj/mRMR mRMR]. **Step I: perform linear regression as previously, and only keep the sites with |t value|>2.5. **Step II: reformat the data in the right format, and perform feature selection with mRMR. **The output of mRMR is a list of 50 sites ranked based on a score ([[Media:KangZhang_GA_BSPP_95_MRMR_output.txt|mRMR output file]]). I then took the methylation data on these 50 sites, and did PCA analysis with R. Clearly patients and controls can be classified quite well based on the top 50 CpG sites. Even with the top 20 sites, the classification was still quite accurate. **[[Media:Gene_associated_with_top_50_markers.txt|Gene associated with the top 50 markers]]. [[Image:Classification_top_10_sites.png|1000px]] [[Image:Classification_top_20_sites.png|1000px]] [[Image:Classification_top_50_sites.png|1000px]]
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