Analyzing Multiple-Probe Microarray: Estimation and Application of Gene Expression Indexes

Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re-examined this problem using a new profile likelihood-based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked-in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project (MAQC, 2006)

A power law global error model for the identification of differentially expressed genes in microarray data

BACKGROUND: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. RESULTS: In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. CONCLUSIONS: The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples

Methods for evaluating gene expression from Affymetrix microarray datasets

<p>Abstract</p> <p>Background</p> <p>Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.</p> <p>Results</p> <p>The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to <it>cis</it>-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.</p> <p>Conclusion</p> <p>A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.</p

Sequence dependence of cross-hybridization on short oligo microarrays

One of the critical problems in the short oligo microarray technology is how to deal with cross-hybridization that produces spurious data. Little is known about the details of cross-hybridization effect at molecular level. Here, we report a free energy analysis of cross-hybridization on short oligo microarrays using data from a spike-in study. Our analysis revealed that cross-hybridization on the arrays is mostly caused by oligo fragments with a run of 10–16 nt complementary to the probes. Mismatches were estimated to be energetically much more costly in cross-hybridization than that in gene-specific hybridization, implying that the sources of cross-hybridization must be very different between a PM–MM probe pair. Consequently, it is unreliable to use MM probe signal to track cross-hybridizing signal on a corresponding PM probe. Our results also showed that the oligo fragments tend to bind to the 5′ ends of the probes, and are rarely seen at the 3′ ends. These results are useful for microarray design and data analysis

PLANdbAffy: probe-level annotation database for Affymetrix expression microarrays

Standard Affymetrix technology evaluates gene expression by measuring the intensity of mRNA hybridization with a panel of the 25-mer oligonucleotide probes, and summarizing the probe signal intensities by a robust average method. However, in many cases, signal intensity of the probe does not correlate with gene expression. This could be due to the hybridization of the probe to a transcript of another gene, mapping of the probe to an intron, alternative splicing, single nucleotide polymorphisms and other reasons. We have developed a database, PLANdbAffy (available at http://affymetrix2.bioinf.fbb.msu.ru), that contains the results of the alignment of probe sequences from five Affymetrix expression microarrays to the human genome. We have determined the probes matching the transcript-coding regions in the correct orientation. For each such probe alignment region, we determined the mRNA and EST sequences that contain the probe sequence. In the textual part of the database interface we summarize the data on the sequences that cover the probe alignment region and SNPs that are located inside it. The graphical part of our database interface is implemented as custom tracks to the UCSC genome browser that allows one to utilize all the data that are offered by UCSC browser

Specificity of DNA microarray hybridization: characterization, effectors and approaches for data correction

Microarray-hybridization specificity is one of the main effectors of microarray result quality. In the present review, we suggest a definition for specificity that spans four hybridization levels, from the single probe to the microarray platform. For increased hybridization specificity, it is important to quantify the extent of the specificity at each of these levels, and correct the data accordingly. We outline possible effects of low hybridization specificity on the obtained results and list possible effectors of hybridization specificity. In addition, we discuss several studies in which theoretical approaches, empirical means or data filtration were used to identify specificity effectors, and increase the specificity of the hybridization results. However, these various approaches may not yet provide an ultimate solution; rather, further tool development is needed to enhance microarray-hybridization specificity

Involvement of genes and non-coding RNAs in cancer: profiling using microarrays

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs, RNAs that do not code for proteins) that regulate the expression of target genes. MiRNAs can act as tumor suppressor genes or oncogenes in human cancers. Moreover, a large fraction of genomic ultraconserved regions (UCRs) encode a particular set of ncRNAs whose expression is altered in human cancers. Bioinformatics studies are emerging as important tools to identify associations between miRNAs/ncRNAs and CAGRs (Cancer Associated Genomic Regions). ncRNA profiling, the use of highly parallel devices like microarrays for expression, public resources like mapping, expression, functional databases, and prediction algorithms have allowed the identification of specific signatures associated with diagnosis, prognosis and response to treatment of human tumors

VARIATIONS IN MICROARRAY BASED GENE EXPRESSION PROFILING: IDENTIFYING SOURCES AND IMPROVING RESULTS

Two major issues hinder the application of microarray based gene expression profiling in clinical laboratories as a diagnostic or prognostic tool. The first issue is the sheer volume and high-dimensionality of gene expression data from microarray experiments, which require advanced algorithms to extract meaningful gene expression patterns that correlate with biological impact. The second issue is the substantial amount of variation in microarray gene expression data, which impairs the performance of analysis method and makes sharing or integrating microarray data very difficult. Variations can be introduced by all possible sources including the DNA microarray technology itself and the experimental procedures. Many of these variations have not been characterized, measured, or linked to the sources. In the first part of this dissertation, a decision tree learning method was demonstrated to perform as well as more popularly accepted classification methods in partitioning cancer samples with microarray data. More importantly, results demonstrate that variation introduced into microarray data by tissue sampling and tissue handling compromised the performance of classification methods. In the second part of this dissertation, variations introduced by the T7 based in vitro transcription labeling methods were investigated in detail. Results demonstrated that individual amplification methods significantly biased gene expression data even though the methods compared in this study were all derivatives of the T7 RNA polymerase based in vitro transcription labeling approach. Variations observed can be partially explained by the number of biotinylated nucleotides used for labeling and the incubation time of the in vitro transcription experiments. These variations can generate discordant gene expression results even using the same RNA samples and cannot be corrected by post experiment analysis including advanced normalization techniques. Studies in this dissertation stress the concept that experimental and analytical methods must work together. This dissertation also emphasizes the importance of standardizing the DNA microarray technology and experimental procedures in order to optimize gene expression analysis and create quality standards compatible with the clinical application of this technology. These findings should be taken into account especially when comparing data from different platforms, and in standardizing protocols for clinical applications in pathology

Identification of Copy Number Variants Defining Genomic Differences among Major Human Groups

BACKGROUND:Understanding the genetic contribution to phenotype variation of human groups is necessary to elucidate differences in disease predisposition and response to pharmaceutical treatments in different human populations. METHODOLOGY/PRINCIPAL FINDINGS:We have investigated the genome-wide profile of structural variation on pooled samples from the three populations studied in the HapMap project by comparative genome hybridization (CGH) in different array platforms. We have identified and experimentally validated 33 genomic loci that show significant copy number differences from one population to the other. Interestingly, we found an enrichment of genes related to environment adaptation (immune response, lipid metabolism and extracellular space) within these regions and the study of expression data revealed that more than half of the copy number variants (CNVs) translate into gene-expression differences among populations, suggesting that they could have functional consequences. In addition, the identification of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with the copy number alleles allowed us to detect evidences of population differentiation and recent selection at the nucleotide variation level. CONCLUSIONS:Overall, our results provide a comprehensive view of relevant copy number changes that might play a role in phenotypic differences among major human populations, and generate a list of interesting candidates for future studies