Distinctive patterns of miRNA expression between cervical cancer and normal samples revealed by principal component analysis. microRNA incidence values from each sample were projected onto the first two principal components, using cube-rooted data. This two-dimensional representation of the ~714 dimensional primary data resulted in evident separation between normal and tumour samples but not between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) samples. The first principal component explains 21.2% of the variation present in the data and the second explains 11.6%. ASC, adenosquamous cell carcinoma; T, tumour; N, normal.