MDQC: a new quality assessment method for microarrays based on quality control reports

Abstract

Motivation: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems. Results: We propose a novel multivariate approach to evaluate the quality of an array that examines the 'Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems.

Controlling the size of multivariate outlier tests with the MCD estimator of scatter

“…Another striking example arises in the process of producing microarray data, where it is crucial to evaluate the quality of an array and to identify those with low quality. Cohen Freue et al (2007) developed a multivariate technique based on robust distances similar to (1) computed on different subsets of variables. Several distances are obtained for each array and ignoring the multiplicity of the resulting tests increases the probability of labelling false outliers and of discarding potentially useful information.…”

HumMeth27QCReport: an R package for quality control and primary analysis of Illumina Infinium methylation data

“…But, while several R packages can assist scientists with quality control analysis of other data types (i.e. microarray [8-10], RNA-seq [11], target enrichment experiments [12]), only four are tailored to support DNA methylation research and, in particular, only two specifics for Illumina data. Of these, charm [13] implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol; it finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools.…”

Two-Stage, In Silico Deconvolution of the Lymphocyte Compartment of the Peripheral Whole Blood Transcriptome in the Context of Acute Kidney Allograft Rejection

“…Affymetrix Human Genome U133 Plus 2.0 (Affymetrix, Inc., Santa Clara, CA, USA) microarrays were processed at the Microarray Core Laboratory at Children's Hospital, Los Angeles in order to assess whole genome expression. The microarrays were checked for quality using the “affy” (version 1.16.0) and “affyPLM” (version 1.14.0) libraries, part of the BioConductor project, as well as “mdqc” (Mahalanobis Distance quality control) [27], an internally developed method. All microarrays that passed quality control were background corrected and normalized using quantile normalization (as in RMA) [28] and summarized using a factor analysis model (factor analysis for robust microarray summarization [FARMS]) [29], via the “farms” library.…”