Oligonucleotide microarray data distribution and normalization

The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling

Background

A major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.

Methods

The three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.

Results

The outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four “known” ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.

Conclusions

We developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.

Current Omics Technologies in Biomarker Discovery

Biomarkers are playing an increasingly important role in drug discovery and development and can be applied for many purposes, including disease mechanism study, diagnosis, prognosis, staging, and treatment selection. Advances in high-throughput “omics” technologies, including genomics, transcriptomics, proteomics and metabolomics, significantly accelerate the pace of biomarker discovery. Comprehensive molecular profiling using these “omics” technology has become a field of intensive research aiming at identifying biomarkers relevant for improved diagnostics and therapeutics. Although each “omics” technology plays important roles in biomarker research, different “omics” platforms have different strengths and limitations. This chapter aims to give an overview of these “omics” technologies and their current application in the biomarker discovery.

Explorative analysis of urine by capillary electrophoresis-mass spectrometry in chronic patients with complex regional pain syndrome

Complex Regional Pain Syndrome (CRPS) is characterized by various combinations of sensory, autonomic and motor disturbances. Pain disproportionate to the severity and duration of the inciting event is the most devastating symptom. Diagnosis of CRPS is difficult as the underlying mechanisms remain unclear. To try to derive metabolic indicators potentially characteristic for CRPS, we applied capillary electrophoresis time-of-flight mass spectrometry (CE-ToF-MS) to the explorative analysis of urine. The CE-ToF-MS method provided fast and stable metabolic profiles of urine samples. The mean intraday and interday CVs were <2% and <9% for migration times and peak areas, respectively, demonstrating robustness of the method. With the use of multivariate chemometric analysis, discrimination between urine samples from CRPS patients and controls was obtained, emphasizing differences in metabolic signatures between CRPS-diseased patients and controls. Several compounds, such as 3-methylhistidine, were responsible for discriminating the samples. The biological relevance of these compounds with regard to CRPS is discussed. Thus, CE-ToF-MS-based metabolic profiling of urine from CRPS patients and controls revealed metabolites that differentiate between diseased and control, illustrating the usefulness of this approach to get more insight into the pathology underlying CRPS.

Alignment of capillary electrophoresis-mass spectrometry datasets using accurate mass information

Capillary electrophoresis-mass spectrometry (CE-MS) is a powerful technique for the analysis of small soluble compounds in biological fluids. A major drawback of CE is the poor migration time reproducibility, which makes it difficult to combine data from different experiments and correctly assign compounds. A number of alignment algorithms have been developed but not all of them can cope with large and irregular time shifts between CE-MS runs. Here we present a genetic algorithm designed for alignment of CE-MS data using accurate mass information. The utility of the algorithm was demonstrated on real data, and the results were compared with one of the existing packages. The new algorithm showed a significant reduction of elution time variation in the aligned datasets. The importance of mass accuracy for the performance of the algorithm was also demonstrated by comparing alignments of datasets from a standard time-of-flight (TOF) instrument with those from the new ultrahigh resolution TOF maXis (Bruker Daltonics).

Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavior.

Egr1 and Gas6 facilitate the adaptation of HEK-293 cells to serum-free media by conferring enhanced viability and higher growth rates

Animal-derived serum is an essential media supplement for mammalian cells in cell culture. For a number of reasons including cost, regulatory concerns, lot inconsistency, potential contamination with adventitious agents, and down-stream processing it is desirable to eliminate the use of serum. Existing protocols designed to adapt cells to serum-free media (SFM) are time-consuming and provide little insight into how the cells adapt. To better understand the physiological responses associated with serum withdrawal and to expedite the adaptation process, a Human Embryonic Kidney-293 (HEK-293) cell line was propagated in 10% fetal bovine serum (FBS) and was progressively adapted to SFM and analyzed at specific serum levels by oligonucleotide microarrays. Of the differentially expressed genes two, early growth response 1 (egr1) and growth arrest specific 6 (gas6), were selected for further analysis based on their level of differential expression, overall expression patterns, and proposed functionalities. HEK-293 cells, propagated in 10% FBS were transfected with egr1 or gas6 and then adapted to SFM. Results indicated that higher expression of either gene moderately enhanced the ability of both cell lines to adapt to SFM. Egr1 appeared to have a greater impact on adaptability than gas6. Results also indicated that specific protein production was unaltered when the expression of egr1 was increased. Flow cytometric analysis revealed increased expression of egr1 was associated with an increase in the percentage of cells in the G2/M phases. These results indicate that enhanced expression of egr1 or gas6 facilitate adaptation to SFM by improving growth and viability.

Microarray RNA transcriptional profiling: part II. Analytical considerations and annotation

This review summarizes the various data filtration, transformation and normalization processes for different array platforms (cDNA, oligos, one- and two-color), data analysis methods and their validation, and databases and annotation for RNA transcriptional profiling microarrays. This review is intended to introduce the beginner to the analyses and interpretation of gene expression studies using a nonmathematical approach for easier comprehension. Microarray analysis is not a trivial undertaking as there is no single method that works well for all, and results obtained from these analyses should be considered as a complement to other approaches.

Enhanced Quantile Normalization of Microarray Data to Reduce Loss of Information in Gene Expression Profiles

In microarray experiments, removal of systematic variations resulting from array preparation or sample hybridization conditions is crucial to ensure sensible results from the ensuing data analysis. For example, quantile normalization is routinely used in the treatment of both oligonucleotide and cDNA microarray data, even though there might be some loss of information in the normalization process. We recognize that the ideal normalization, if it ever exists, would aim to keep the maximal amount of gene profile information with the lowest possible noise. With this objective in mind, we propose a valuable enhancement to quantile normalization, and demonstrate through three Affymetrix experiments that the enhanced normalization can result in better performance in detecting and ranking differentially expressed genes across experimental conditions.

Bayesian error-in-variable survival model for the analysis of GeneChip arrays

DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error-in-variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.

Comparison of transcript profiling on Arabidopsis microarray platform technologies

To date there have been few systematic studies comparing the results of transcript profiling from different microarray platform technologies. We evaluated in detail two different Arabidopsis thaliana microarray platforms: our own Genomic Amplicon arrays and the Qiagen long oligonucleotide arrays designed by Operon; furthermore, we cross-validated these arrays against the Affymetrix AG and ATH1 GeneChips. Data were obtained from all three platforms in each of two separate experiments; (1) at 2 h and (2) 8 h following a salicylic acid treatment applied to both wild-type and npr1-3 mutant plants. A total of 20 hybridizations were performed, analyzing the expression of 26,814 unique locus IDs. We demonstrate that intensity rank is a key variable that affects both inter-platform and cross-platform reproducibility. Although general agreement between platform technologies is low, data derived from high signal intensities (90th percentile) can correlate as well between differing platforms as replicates within the same platform (r=0.4-0.7). We also show that the identification of differentially expressed genes by significance analysis of microarrays is influenced by signal intensity and that overlap between significant gene lists from different platform technologies was as high as 67% when low intensity values were removed. Validation of 41 genes by Northern blot hybridization showed that all platform technologies performed well, qualitatively confirming 83-100% of differential gene expression. Our results suggest that the potential for the broad integration of microarray data from different platforms and laboratories is promising.

Mouse ovary developmental RNA and protein markers from gene expression profiling

To identify genes involved in morphogenetic events during mouse ovary development, we started with microarray analyses of whole organ RNA. Transcripts for 60% of the 15,000 gene NIA panel were detected, and about 2000 were differentially expressed in nascent newborn compared to adult ovary. Highly differentially expressed transcripts included noncoding RNAs and newly detected genes involved in transcription regulation and signal transduction. The phased pattern of newborn mouse ovary differentiation allowed us to (1) extend information on activity and stage specificity of cell type-specific genes; and (2) generate a list of candidate genes involved in primordial follicle formation, including podocalyxin (Podxl), PDGFR-beta, and a follistatin-domain-encoding gene Flst1. Oocyte-specific transcripts included many (e.g., Deltex2, Bicd2, and Zfp37) enriched in growing oocytes, as well as a novel family of untranslated RNA's (RLTR10) that is selectively expressed in early stage follicles. The results indicate that global expression profiling of whole organ RNA provides sensitive first-line information about ovarian histogenesis for which no in vitro cell models are currently available.

Effect of normalization on significance testing for oligonucleotide microarrays

MOTIVATION

Normalization techniques are used to reduce variation among gene expression measurements in oligonucleotide microarrays in an effort to improve the quality of the data and the power of significance tests for detecting differential expression. Of several such proposed methods, two that have commonly been employed include median-interquartile range normalization and quantile normalization. The median-IQR method applied directly to fold-changes for paired data also was considered. Two methods for calculating gene expression values include the MAS 5.0 algorithm [Affymetrix. (2002). Statistical Algorithms Description Document. Santa Clara, CA: Affymetrix, Inc. http://www.affymetrix.com/support/technical/whitepapers/sadd-whitepaper.pdf] and the RMA method [Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B., Speed, T. P. (2003a). Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31(4,e15); Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., Speed, T. P. (2003b). Exploration, normalization, and summaries of high density oligonucleotide array probe-level data. Biostatistics 4(2):249-264; Irizarry, R. A., Gautier, L., Cope, L. (2003c). An R package for analysis of Affymetrix oligonucleotide arrays. In: Parmigiani, R. I. G., Garrett, E. S., Ziegler, S., eds. The Analysis of Gene Expression Data: Methods and Software. Berlin: Springer, pp. 102-119].

RESULTS

In considering these methods applied to a prostate cancer data set derived from paired samples on normal and tumor tissue, it is shown that normalization methods may lead to substantial inflation of the number of genes identified by paired-t significance tests even after adjustment for multiple testing. This is shown to be due primarily to an unintended effect that normalization has on the experimental error variance. The impact appears to be greater in the RMA method compared to the MAS 5.0 algorithm and for quantile normalization compared to median-IQR normalization.

Analysis of gene expression profile in yeast aging chronologically

The use of simple model systems such as Saccharomyces cerevisiae and Caenorhabditis elegans has played a primary role in the identification of proteins and pathways that regulate the aging process in eukaryotes. Recent findings have shown that analogous pathways regulate aging in higher eukaryotes and suggest a conserved origin for the molecular mechanisms that regulate stress-resistance and longevity. Genomics approaches that allow the simultaneous monitoring of the expression of thousands of genes are beginning to reveal the complexity of the molecular changes required to extend life span. Here we describe how analysis of the gene expression profiles of wild-type and long-lived yeast aging chronologically can be used to identify proteins that increase stress-resistance and longevity. We also discuss a novel genomics method for the identification of chronologically long-lived yeast mutants.

Identifying biological themes within lists of genes with EASE

EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data.

EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.