Missing values in junction coverage ratios were imputed using missMDA R package . PCA analysis was then performed using prcomp R package . We regressed out principal components ( PCs ) accounting for 95 % of the variation in our imputed dataset ( 176 PCs ) . We then put back original missing values in the dataset and derived Z - scores used in the outlier analysis . We looked at the correlation pattern between the 10 first PCs and known covariates from our dataset . In brief , PC1 was mainly separating the source of the data ( UDN , CGS , CHEO or PIVUS ) . PC2 was highlighting differences between the first batch and the other batches . Overall , we observed some level of correlation between all known covariates and the PCs that were regressed out from the data .