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Assessing affymetrix GeneChip microarray quality
Matthew N McCall, Peter N Murakami, Margus Lukk, Wolfgang Huber, Rafael A Irizarry
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-137
Abstract: We begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.We find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) [1] and the MicroArray Quality Control (MAQC) projects [2,3] have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.Defining quality in the context of a microarray experiment is not an easy task. The American Society for Quality (ASQ) defines quality as
Qualitative Assessment of Gene Expression in Affymetrix Genechip Arrays  [PDF]
Radhakrishnan Nagarajan,Meenakshi Upreti
Quantitative Biology , 2006, DOI: 10.1016/j.physa.2006.06.004
Abstract: Affymetrix Genechip microarrays are used widely to determine the simultaneous expression of genes in a given biological paradigm. Probes on the Genechip array are atomic entities which by definition are randomly distributed across the array and in turn govern the gene expression. In the present study, we make several interesting observations. We show that there is considerable correlation between the probe intensities across the array which defy the independence assumption. While the mechanism behind such correlations is unclear, we show that scaling behavior and the profiles of perfect match (PM) as well as mismatch (MM) probes are similar and immune to background subtraction. We believe that the observed correlations are possibly an outcome of inherent non-stationarities or patchiness in the array devoid of biological significance. This is demonstrated by inspecting their scaling behavior and profiles of the PM and MM probe intensities obtained from publicly available Genechip arrays from three eukaryotic genomes, namely: Drosophila Melanogaster, Homo Sapiens and Mus musculus across distinct biological paradigms and across laboratories, with and without background subtraction. The fluctuation functions were estimated using detrended fluctuation analysis (DFA) with fourth order polynomial detrending. The results presented in this study provide new insights into correlation signatures of PM and MM probe intensities and suggests the choice of DFA as a tool for qualitative assessment of Affymetrix Genechip microarrays prior to their analysis. A more detailed investigation is necessary in order to understand the source of these correlations.
SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip
Hironori Fujisawa, Youko Horiuchi, Yoshiaki Harushima, Toyoyuki Takada, Shinto Eguchi, Takako Mochizuki, Takayuki Sakaguchi, Toshihiko Shiroishi, Nori Kurata
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-131
Abstract: We have developed SNEP, a new method that allows simultaneous detection of both nucleotide and expression polymorphisms. SNEP involves a robust statistical procedure based on the idea that a nucleotide polymorphism observed at the probe level can be regarded as an outlier, because the nucleotide polymorphism can reduce the hybridization signal intensity. To investigate the performance of SNEP, we used three species: barley, rice and mice. In addition to the publicly available barley data, we obtained new rice and mouse data from the strains with available genome sequences. The sensitivity and false positive rate of nucleotide polymorphism detection were estimated based on the sequence information. The robustness of expression polymorphism detection against nucleotide polymorphisms was also investigated.SNEP performed well regardless of the genome size and showed a better performance for nucleotide polymorphism detection, when compared with other previously proposed methods. The R-software 'SNEP' is available at http://www.ism.ac.jp/~fujisawa/SNEP/ webcite.Affymetrix GeneChip expression arrays are high-density short oligonucleotide microarrays that were initially designed to monitor genome-wide expression profiles [1]. Affymetrix probe sets consist of several (typically 11) 25-mer short oligomer probes matching each gene [perfect match (PM) probes] and accompanying probes with single complementary substitutions in the 13th base of each PM probe [mismatch (MM) probes]. Signal intensities for the probes are obtained by hybridizing labeled genomic DNA (gDNA) or mRNA to the expression array. Recently, nucleotide polymorphisms have been detected with these probes by hybridizing gDNA from human malaria parasite [2], yeast [3], malaria mosquito [4], Arabidopsis [5,6], and rice [7], and by hybridizing mRNA from yeast [8], Arabidopsis [9], barley [10-12], maize [13], and mammals [14]. A nucleotide polymorphism observed at a probe level was called a single feature polymorphism
SNiPer: Improved SNP genotype calling for Affymetrix 10K GeneChip microarray data
Matthew J Huentelman, David W Craig, Albert D Shieh, Jason J Corneveaux, Diane Hu-Lince, John V Pearson, Dietrich A Stephan
BMC Genomics , 2005, DOI: 10.1186/1471-2164-6-149
Abstract: A database of 948 individuals genotyped on the GeneChip? Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at http://www.tgen.org/neurogenomics/data webcite.The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip? without sacrificing accuracy, thereby increasing the amount of valid data generated.Single nucleotide polymorphisms (SNPs) are fast becoming the markers of choice for genome-wide linkage scans, loss of heterozygosity (LOH), comparative genomic hybridization (CGH) and whole-genome association studies [1]. This is due to the existence of high throughput technologies like the GeneChip? Human Mapping Array from Affymetrix coupled with the abundant and uniform distribution of SNPs throughout the human genome [2-6]. The GeneChip? Mapping Array relies on the hybridization of biotin-tagged fragments of SNP-containing DNA to complementary DNA oligomers chemically tiled on a silicon wafer in order to genotype 10,204 SNPs with a mean inter-marker spacing of 258 Kb [7]. The assay utilizes a relatively minor amount of genomic DNA (250 ng) and a series
Detection of pathogenic copy number variants in children with idiopathic intellectual disability using 500 K SNP array genomic hybridization
JM Friedman, Shelin Adam, Laura Arbour, Linlea Armstrong, Agnes Baross, Patricia Birch, Cornelius Boerkoel, Susanna Chan, David Chai, Allen D Delaney, Stephane Flibotte, William T Gibson, Sylvie Langlois, Emmanuelle Lemyre, H Irene Li, Patrick MacLeod, Joan Mathers, Jacques L Michaud, Barbara C McGillivray, Millan S Patel, Hong Qian, Guy A Rouleau, Margot I Van Allen, Siu-Li Yong, Farah R Zahir, Patrice Eydoux, Marco A Marra
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-526
Abstract: We performed 500 K Affymetrix GeneChip? array genomic hybridization in 100 idiopathic intellectual disability trios, each comprised of a child with intellectual disability of unknown cause and both unaffected parents. We found pathogenic genomic imbalance in 16 of these 100 individuals with idiopathic intellectual disability. In comparison, we had found pathogenic genomic imbalance in 11 of 100 children with idiopathic intellectual disability in a previous cohort who had been studied by 100 K GeneChip? array genomic hybridization. Among 54 intellectual disability trios selected from the previous cohort who were re-tested with 500 K GeneChip? array genomic hybridization, we identified all 10 previously-detected pathogenic genomic alterations and at least one additional pathogenic copy number variant that had not been detected with 100 K GeneChip? array genomic hybridization. Many benign copy number variants, including one that was de novo, were also detected with 500 K array genomic hybridization, but it was possible to distinguish the benign and pathogenic copy number variants with confidence in all but 3 (1.9%) of the 154 intellectual disability trios studied.Affymetrix GeneChip? 500 K array genomic hybridization detected pathogenic genomic imbalance in 10 of 10 patients with idiopathic developmental disability in whom 100 K GeneChip? array genomic hybridization had found genomic imbalance, 1 of 44 patients in whom 100 K GeneChip? array genomic hybridization had found no abnormality, and 16 of 100 patients who had not previously been tested. Effective clinical interpretation of these studies requires considerable skill and experience.Chromosomal imbalance has been recognized as the most frequent cause of intellectual disability (ID) for 50 years [1-3]. Until recently, most of this genomic imbalance was diagnosed by cytogenetic analysis, but studies over the past few years have found that ID is caused by constitutional gains or losses of submicroscopic genomic segme
Assessment of the relationship between signal intensities and transcript concentration for Affymetrix GeneChip? arrays
Eugene Chudin, Randal Walker, Alan Kosaka, Sue X Wu, Douglas Rabert, Thomas K Chang, Dirk E Kreder
Genome Biology , 2001, DOI: 10.1186/gb-2001-3-1-research0005
Abstract: A linear relationship between transcript abundance and signal was consistently observed between 1 pM and 10 pM transcripts. The signal ceased to be linear above the 10 pM level and commenced saturating around the 100 pM level. The 0.1 pM transcripts were virtually undetectable in the presence of eukaryotic background. Our measurements show that preponderance of the signal for mismatch probes derives from interactions with the target transcripts.Landmark studies outlining an observed linear relationship between signal and transcript concentration were carried out under highly specialized conditions and may not extend to commercially available arrays under routine operating conditions. Additionally, alternative metrics that are not based on the difference in the signal of members of a probe pair may further improve the quantitative utility of the Affymetrix GeneChip? array.Even though the DNA microarray is still an emerging technology, its usefulness as a profiling tool is well established. Affymetrix GeneChip? arrays enable the concurrent assessment of expression levels for thousands of genes in a single experiment. At the molecular level, however, the microarray experiment is a challenging biophysical problem that is extremely dependent on probe-target kinetics, specificity and design. Among the principal sources of variability are the nonspecific interactions due to combinatorial complexity of the genome, the thermodynamic equivalence of probes, the accuracy and spatial uniformity of probe synthesis and the preparation, amplification and fractionation of cDNA and cRNA.The photolithographically synthesized oligonucleotide microarrays that underlie the Affymetrix GeneChip? array use pairs (typically 16 or 20) of perfect-match (PM) and mismatch (MM) features. Each feature is a rectangular region containing oligonucleotides complementary to a corresponding region of a gene. Because of the inherent difficulties of oligonucleotide synthesis, the proportion of full-length
The chemiluminescence based Ziplex? automated workstation focus array reproduces ovarian cancer Affymetrix GeneChip? expression profiles
Michael CJ Quinn, Daniel J Wilson, Fiona Young, Adam A Dempsey, Suzanna L Arcand, Ashley H Birch, Paulina M Wojnarowicz, Diane Provencher, Anne-Marie Mes-Masson, David Englert, Patricia N Tonin
Journal of Translational Medicine , 2009, DOI: 10.1186/1479-5876-7-55
Abstract: The new chemiluminescence-based Ziplex? gene expression array technology was evaluated for the expression of 93 genes selected based on their Affymetrix GeneChip? profiles as applied to ovarian cancer research. Probe design was based on the Affymetrix target sequence that favors the 3' UTR of transcripts in order to maximize reproducibility across platforms. Gene expression analysis was performed using the Ziplex Automated Workstation. Statistical analyses were performed to evaluate reproducibility of both the magnitude of expression and differences between normal and tumor samples by correlation analyses, fold change differences and statistical significance testing.Expressions of 82 of 93 (88.2%) genes were highly correlated (p < 0.01) in a comparison of the two platforms. Overall, 75 of 93 (80.6%) genes exhibited consistent results in normal versus tumor tissue comparisons for both platforms (p < 0.001). The fold change differences were concordant for 87 of 93 (94%) genes, where there was agreement between the platforms regarding statistical significance for 71 (76%) of 87 genes. There was a strong agreement between the two platforms as shown by comparisons of log2 fold differences of gene expression between tumor versus normal samples (R = 0.93) and by Bland-Altman analysis, where greater than 90% of expression values fell within the 95% limits of agreement.Overall concordance of gene expression patterns based on correlations, statistical significance between tumor and normal ovary data, and fold changes was consistent between the Ziplex and Affymetrix platforms. The reproducibility and ease-of-use of the technology suggests that the Ziplex array is a suitable platform for translational research.During the last decade, the advent of high-throughput techniques such as DNA microarrays, has allowed investigators to interrogate the expression level of thousands of genes concurrently. Due to the heterogeneous nature of many cancers in terms of both their genetic and m
Feature-level exploration of a published Affymetrix GeneChip control dataset
Rafael A Irizarry, Leslie M Cope, Zhijin Wu
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-8-404
Abstract: In a recent Genome Biology article, Choe et al. [1] describe a spike-in experiment that they use to compare expression measures for Affymetrix GeneChip technology. In this work, two sets of triplicates were created to represent control (C) and experimental (S) samples. We describe here some properties of the Choe et al. [1] control dataset one should consider before using it to assess GeneChip expression measures. In [2] and [3] we describe a benchmark for such measures based on experiments developed by Affymetrix and GeneLogic. These datasets are described in detail in [2]. A web-based implementation of the benchmark, is available at [4]. The experiment described in [1] is a worthy contribution to the field as it permits assessments with data that is likely to better emulate the nonspecific binding (NSB) and cross-hybridization seen in typical experiments. However, there are various inconsistencies between the conclusions reached by [1] and [3] that we do not believe are due to NSB and cross-hybridization effects. In this Correspondence we describe certain characteristics of the feature-level data produced by [1] which we believe explain these inconsistencies. These can be divided into characteristics induced by the experimental design and an artifact.There are three characteristics of the experimental design described by [1] that one should consider before using it for assessments like those carried out by Affycomp. We enumerate them below and explain how they may lead to unfair assessments. Other considerations are described by Dabney and Storey [5].First, the spike-in concentrations are unrealistically high. In [3] we demonstrate that background noise makes it harder to detect differentially expression for genes that are present at low concentrations. We point out that in the Affymetrix spike-in experiments [2,3] the concentrations for spiked-in features result in artificially high intensities but that a large range of the nominal concentrations are actually in
A statistical framework for consolidating "sibling" probe sets for Affymetrix GeneChip data
Hua Li, Dongxiao Zhu, Malcolm Cook
BMC Genomics , 2008, DOI: 10.1186/1471-2164-9-188
Abstract: The ANOVA model allows us to separate the sibling probe sets into two types: those behave similarly across treatments and those behave differently across treatments. We found that consolidation of sibling probe sets of the former type results in large increase in the number of differentially expressed genes under various statistical criteria. The approach to selecting sibling probe sets suitable for consolidating is implemented in R language and freely available from http://research.stowers-institute.org/hul/affy/ webcite.Our ANOVA analysis of sibling probe sets provides a statistical framework for selecting sibling probe sets for consolidation. Consolidating sibling probe sets by pooling data from each greatly improves the estimates of a gene expression level and results in identification of more biologically relevant genes. Sibling probe sets that do not qualify for consolidation may represent annotation errors or other artifacts, or may correspond to differentially processed transcripts of the same gene that require further analysis.Affymetrix GeneChip is one of the most popular platforms for profiling gene expression at the genome scale. It has been used for detecting differentially expressed genes [1-4], discovering disease markers [5], discovering functionally related genes, and clustering genome-wide expression patterns [6-9]. A single gene may be represented by multiple probe sets on a GeneChip. For example, in the mouse moe4302 chip, there are 45, 101 probe sets corresponding to 25, 724 distinct genes, and 40% of all genes are represented by multiple probe sets, called "sibling probe sets" throughout this paper. For these 40% of genes, almost half of them are represented by more than two probe sets on the chip, and some genes even have more than ten probe sets. Similarly in the human hgu133plus2 chip, the total of 28, 919 genes are represented by 54, 675 probe sets on the chip (Fig. 1).According to Affymetrix, there are three primary reasons for designing s
Genome-wide loss of heterozygosity and copy number alteration in esophageal squamous cell carcinoma using the Affymetrix GeneChip Mapping 10 K array
Nan Hu, Chaoyu Wang, Ying Hu, Howard H Yang, Li-Hui Kong, Ning Lu, Hua Su, Quan-Hong Wang, Alisa M Goldstein, Kenneth H Buetow, Michael R Emmert-Buck, Philip R Taylor, Maxwell P Lee
BMC Genomics , 2006, DOI: 10.1186/1471-2164-7-299
Abstract: Genome-wide detection of chromosomal changes was performed using the Affymetrix GeneChip 10 K single nucleotide polymorphism (SNP) array, including loss of heterozygosity (LOH) and copy number alterations (CNA), for 26 pairs of matched germ-line and micro-dissected tumor DNA samples. LOH regions were identified by two methods – using Affymetrix's genotype call software and using Affymetrix's copy number alteration tool (CNAT) software – and both approaches yielded similar results. Non-random LOH regions were found on 10 chromosomal arms (in decreasing order of frequency: 17p, 9p, 9q, 13q, 17q, 4q, 4p, 3p, 15q, and 5q), including 20 novel LOH regions (10 kb to 4.26 Mb). Fifteen CNA-loss regions (200 kb to 4.3 Mb) and 36 CNA-gain regions (200 kb to 9.3 Mb) were also identified.These studies demonstrate that the Affymetrix 10 K SNP chip is a valid platform to integrate analyses of LOH and CNA. The comprehensive knowledge gained from this analysis will enable improved strategies to prevent, diagnose, and treat ESCC.Genetic instabilities are characteristic of most human cancers. Genome-wide detection of chromosomal changes, including loss of heterozygosity (LOH) and copy number alterations (CNA), either gain or loss, are the focus of substantial attention in cancer research. LOH is frequently observed in a variety of human cancers, and regions with frequent LOH may contain tumor suppressor genes. In addition, LOH may associate with the regions affected by haplo-insufficiency of a group of genes. Thus, detection of LOH will likely remain a cornerstone for predicting tumor aggressiveness for many human tumors [1]. Recently, the discovery of large-scale genome-wide copy number variation has stimulated interest in elucidating the role of CNA in the development of malignancy. The 10 K single nucleotide polymorphism (SNP) array (GeneChip Mapping 10 K array, Affymetrix) offers a high-resolution genomic approach to screen chromosomal alterations systematically. Several studies o
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