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Statistical quality control analysis of high-dimensional omics data
by Yongkang Kim; Gyu-Tae Kim; Min-Seok Kwon; Taesung Park
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 18, No. 3, 2017

 

Abstract: Quality control (QC) is a most important pre-processing procedure to remove unwanted variation in omics data, such as microarray, next generation sequencing, and mass spectrometry data. QC has become a standard procedure for identifying important biological 'signatures' of interest. Although several QC analysis tools are now used widely, these usually require a subjective guideline to determine the quality of the omics data being assessed. Here, we propose a new simple QC plot for high-dimensional omics data that can identify samples of poor quality in a more objective manner. The proposed QC plot can easily identify samples of poor quality by comparing the between/within group distances, between all possible pairs of samples. Through a permutation procedure, the distribution of these distances is derived, generating p-values for each sample. These p-values can then be used as a more objective criterion to determine the quality of the sample. To exemplify the utility of this approach, we applied the proposed QC plot to MicroArray Quality Control (MAQC), project 1 data.

Online publication date: Tue, 03-Oct-2017

 

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