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===48 African Methylomes (UPenn)=== *PCA plots: on matrix with ~50K Cpgs (no missing values) and on matrix with ~1.2K SNPs (also no missing values allowed) [[File:UPenn_African_PCA.pdf]] *Note: qc factor is very strongly correlated with genetic factors within these samples *'''Sarah mentioned that PC2 might be related to metabolism''' *Histogram of the Cpg rotations on PC2: [[File:UPenn_African_PCA_rotations.pdf]] *Used >=0.012 cut off to obtain the dominating Cpgs on PC2 to get 805 Cpg sites (791 are on chromosome X, could be related to X chromosome inactivation or imprinting?) *Uploaded 805 sites and the ~50K sites as background to GREAT. Enrichment at GO Biological Process terms: [[File:UPenn_Great_PC2_GO_Biological_Process.txt]] *'''Dr.Zhang: we should expand to include sites that have missing values''' *New methylation matrix: -QC cut off of 0.95 for every sample (only 47 samples reached 0.95 QC value) -Starts minimum depth cut off of 5 -Requires at least 40 values at each Cpg -Requires minimum standard deviation at each site of 0.1 -Total number of sites = 93,924 *K-means or H-clustering can handle missing values but can't calculate the correlation on a large matrix (all paired Cpgs correlation test is computationally intensive). In addition, the best way to handle missing values is by imputation (which takes a long time), if the missing values are ignored then the correlation might be inaccurate. *So I decided to perform linear regression of PC2 scaled values versus all Cpgs -Each sample (n=47) has a value on PC2 -Each Cpg has at least 40 methylation values. -Imputation is 1000 times (not a lot but took over 20 hours to complete) -Used 0.001 p(empirical) cut off => 2399 significant sites (FDR = 0.001*93924/2399 = 0.04) *Uploaded 2399 sites and the ~94K sites as background to GREAT. [[File:UPenn_Great_PC2_regressedSites.txt]]
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