I am reading a chapter about principal component analysis (PCA). It states that for any random varible $X \in \mathbb{R}^p$ with $n$ observations, $E[X] = \mu$ and $Cov[X] = \Sigma$ the i-th PC is $y_i = \gamma_i^{T} X$, where $\gamma_i$ is the eigenvector corresponding to the i-th largest eigenvalue of the covariance matrix of $X$. Then, it is stated that the PC-transformation is defined as $Y = \Gamma^T (X - \mu)$.
This confuses me because in earlier studies I learned PCA is applied to centered data. So I always thought that the i-th PC corresponds to $y_i = \gamma_i^{T} Z$, where $Z$ is centered data.
So, I think the statement with the PC-transformation is right in the book but the first statement of which defines the PC is wrong and even a contradiction to the statement of the PC-transformation.
Could please somebody make clear whether I am right or wrong.