## Table 2 |
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Thirteen principal components result |
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PC1: 84.447% | PC2: 10.149% | PC3: 3.033% | ➩ ∑ = 97.63 % |

PC4: 0.846% | PC5: 0.681% | PC6: 0.249% | ➩ ∑ = 1.776 % |

PC7: 0.188% | PC8: 0.122% | PC9: 0.087% | ➩ ∑ = 0.397 % |

PC10: 0.060% | PC11: 0.055% | PC12: 0.036% | ➩ ∑ = 0.151 % |

PC13: 0.033% | ➩ ∑ = 0.033 % |

These are the total variances explained by the percentage for each principal component of the UTPM-normalized data. Total of 13 principal components (PC1, PC2, PC3, PC4, … PC13) is produced as an outcome of the PCA for our each data-column representation. However, reduced data dimension is the way of selecting the highest variances of principal components among them, higher the value, which determines the most likely principal component, needs to be taken. In this instance, first three principal components (total of 97.63%) are good enough to represent the entire data selected due to the highest variances among others (total of 2.357% = 1.776%+ 0.397%+ 0.151%+ 0.033%).

Uragun and Rajan

Uragun and Rajan *BMC Neuroscience* 2013 **14**:114 doi:10.1186/1471-2202-14-114