Feature extraction software 9.5.1.1

The cRNA was amplified, labeled, and hybridized to a 60 K Agilent 60-mer oligomicroarray, according to the manufacturer’s instructions. All hybridized microarray slides were scanned by an Agilent scanner. Relative hybridization intensities and background hybridization values were calculated using Agilent Feature Extraction Software (9.5.1.1). G3 Human Gene Expression Microarray 8 × 60 K, v2, was used for analysis. The raw signal intensities of all samples were log2-transformed and normalized using the quantile algorithm of the ‘preprocessCore’ library package on Bioconductor software. We selected the probes, excluding the control probes, where the detection P-values of all samples were less than 0.01, and used these to identify differentially expressed genes. We then applied the Linear Models for Microarray Analysis (limma) package within the Bioconductor software. The results derived from this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus and are accessible through the Gene Expression Omnibus (GEO) Series, Accession Number GSE61843 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc = GSE61843).

The general flowchart of data processing and detailed methods were described in Figure 1. Briefly, the process detail of circRNA microarray hybridization can be found in our previous work [3 (link), 7 (link)]. Agilent Microarray Scanner was used to scan the arrays and acquired array images analyzed using Agilent Feature Extraction Software 9.5.1.1. Quantile normalization and subsequent data processing were performed using R project and the Bioconductor package of Limma [11 (link), 12 (link)].
Changes in circRNAs expression with p-values < 0.05 and fold-changes ≥1.5 were considered statistically significant. Fold change filtering was calculated for screening DECs, which were visualized with Volcano Plot. Hierarchical clustering was performed to display the distinguishable DECs patterns between samples.