Quality control plot for high dimensional omics data

Abstract

Quality control (QC) becomes more important in pre-processing analysis of high dimensional omics data. Several routine QC processes became a standard process in omics data analysis. The standard QC analysis includes calculating quality-related measures, checking the consistency among samples, detecting outlying observations and so forth. QC analysis tends to be more important in the era of high dimensional omics data. Although several QC analysis tools providing simple graphical display have been developed by many researchers, they usually require a subjective decision on QC. Here, we propose high-dimensional data quality control (HidQC) plot which is a simple and efficient QC tool for handling high dimensional omics data. HidQC plot primarily focuses on identifying samples of poor quality by conducting a contrast analysis for the between/within group distances and the summary distances. HidQC plot checks the quality by investigating the consistency of samples for each group. Unlike other QC plots, HidQC plot provides the p-value of each sample based on the permutation test, which can be used as a more objective criterion to determine whether to use the sample or not. We applied HidQC plot to MicroArray Quality Control (MAQC) project 1 data to demonstrate its usefulness.