We read with great interest the article by Li et al. The results are illuminating for understanding the relationship among Hsa-circRNA11783-2, coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM). However, from our perspective, the bioinformatics analyses need further context as the statistics for differential fold changes in expression data are not explained fully. The authors seem to use unadjusted p values for detecting differentially expressed circular RNA (differentially expressed genes (DEGs)) between the control, CAD and T2DM group. Due to the high false positives caused by a large number of probes and multiple comparisons, it seems essential to analyse microarray data properly to reach a reliable result by a statistical method. Only selecting circular RNA with greater than two fold change with unadjusted p values < 0.05 in expression is not reliable and suitable for high-level microarray analysis.
Keywords: Bioinformatics; circular RNA; microarray analysis; statistics.