Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

BACKGROUND: Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values. RESULTS: Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation. CONCLUSIONS: Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays

The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays

<p>Abstract</p> <p>Background</p> <p>Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available.</p> <p>Results</p> <p>The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls.</p> <p>Conclusions</p> <p>ICEP is a stand-alone Windows application to obtain optimal data quality from custom-designed microarrays and is freely available here (see "Additional Files" section) and at: <url>http://www.alice-dsl.net/evgeniy.vainshtein/ICEP/</url></p

Correlation test to assess low-level processing of high-density oligonucleotide microarray data

BACKGROUND: There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data. RESULTS: We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout. CONCLUSION: Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes

Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data

<p>Abstract</p> <p>Background</p> <p>Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip^®.</p> <p>Results</p> <p>We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip^®processed data. Datasets with unbalanced numbers of up and down regulated genes seem to be particularly susceptible to this type of intensity-dependent technical variation. Unbalanced gene regulation is common when studying cancer samples or genetically manipulated animal models and preservation of this biologically relevant information, while removing technical variation has not been well addressed in the literature. We propose a method based on using rank-invariant, endogenous transcripts as reference points for normalization (GRSN). While the use of rank-invariant transcripts has been described previously, we have added to this concept by the creation of a global rank-invariant set of transcripts used to generate a robust average reference that is used to normalize all samples within a dataset. The global rank-invariant set is selected in an iterative manner so as to preserve unbalanced gene expression. Moreover, our method works well as an overlay that can be applied to data already processed with other probe set summary methods. We demonstrate that this additional normalization step at the "probe set level" effectively corrects a specific type of technical variation that often distorts samples in datasets.</p> <p>Conclusion</p> <p>We have developed a simple post-processing tool to help detect and correct non-linear technical variation in microarray data and demonstrate how it can reduce technical variation and improve the results of downstream statistical gene selection and pathway identification methods.</p

Personalized identification of altered pathway using accumulated data

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2014. 8. 박태성.유전자 네트웍의 이상을 탐지하는 것은 질병의 기작을 이해하고 나아가 개인의 유전자 결함에 맞춤 치료를 선정하는 일에 중요하다. 현재 존재하는 유전자 조절/생체 대사 경로 분석 알고리즘은 대부분 정상과 대조군 집단에서의 차이를 판별하는 데에 초점이 맞추어져 있다. 이러한 방법은 한 개인에 초점을 맞추어 분석을 하는 용도로는 적합하지 못하다. 한 개인의 유전자 네트웍의 이상을 분석함에 있어 가장 이상적인 방법은 같은 환자의 정상 조직과 질병 조직의 정보를 비교하는 것이다. 하지만, 임상적인 이유에서 환자의 정상 조직의 정보는 항상 가용한 것은 아니다. 정상 조직을 채취 하는 것은 임상적인 위험이 따르며, 특별하고 명확한 이유가 없는 한 권장되지 않는다. 따라서 질병 시료의 개인 맞춤 분석에 있어서, 같은 사람의 정상 조직 정보는 가용하지 않은 경우가 많다. 본 논문에서는 개인 분석이라는 측면과 해당 환자의 정상 조직 정보가 가용하지 않을 때 유전자 네트웍을 분석하는 것에 초점을 맞추었다. 본 논문의 방법의 철학은 한 사람의 암 환자 유전자 정보를 많은 수의 집적된 정상 조직의 유전자 정보와 비교하여 이상 유무를 판단하는 것에 있다. 본 논문은 Over-Representation Analysis (ORA), Functional Class Score (FCS) 등의 기존에 알려진 그룹 대 그룹에서의 유전자 네트웍 분석법의 개인향 분석법을 제공한다. 이 방법을 사용하여 본 논문에서는 개인의 유전자 네트웍 이상 점수 (individualized pathway aberrance score : iPAS)를 제시 한다. 본 논문의 방법을 두가지 종류의 암종 (폐 선암종, 대장암) 유전자 발현 데이터에 적용하여 유용성을 보였다. 페 정상 조직과 대장 점막 정상 조직의 유전자 발현 데이터를 참조 표준으로 삼고, 각 암 환자 한 사람씩의 유전자 네트웍의 이상을 분석 하였다. 본 논문의 방법은 기존의 연구에서 밝혀진 환자 생존률과 관련된 유전자 네트웍 이상을 정확히 탐지 하였다. 본 논문의 방법은 기존에 방법이라고 할 수 있는, 환자 한명의 정보를 해당 환자가 속한 코호트의 정보를 참조 표준으로 사용하여 해석하는 것 보다, 더 높은 재현성을 보였다. 재현성 측정은 서로 다른 데이터군을 사용하여, 유전자 네트웍 발굴군에서 발굴한 생존 관련 유전자 네트웍이, 발굴에 사용되지 않았던 데이터군에서도 생존에 유의한 영향을 미치는지 측정하였다. 또한 해당 방법은 유전자 네트웍의 특징을 기반으로 환자와 정상을 구분할 수 있다. 특별히 amino acid synthesis and interconversion pathway의 경우 폐 선암을 독립적인 검증을 위한 데이터군에서도 AUC 0.982로 잘 구분할 수 있다. 또한 본 논문에서 제시한 방법은 돌연변이가 유전자 발현 네트웍에 미치는 영향을 정량화 할 수 있는 방법으로 사용될 수 있다. 본 방법을 사용하였을 때 유방암의 유전자 발현 네트웍에 통계적으로 유의한 영향을 미치는 PI3KCA, TP53, RB1 의 세 유전자를 찾을 수 있었고, 이는 알려진 유방암의 지식과 일치한다. 본 논문의 임상적인 의의는 환자 한 사람에서 정상 조직 정보가 없을 때, 한 사람의 암을 유전자 네트웍 측면에서 해석 할 수 있도록 한 것이다. 이러한 방법은 데이터에 기반한 것으로서, 축적되고 있는 정상 조직 데이터를 사용하여, 더욱 정확한 데이터 기반 의사 결정을 하는 데에 기여할 수 있다. 본 논문의 방법은 유전자 발현 뿐 아니라 돌연 변이 분석과도 연계되어, 환자의 암을 유발하는 유전자 네트웍을 발굴하고, 맞춤 치료제를 선정하는 일에 기여할 수 있다.Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individuals pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual is often unavailable in clinical situation. We therefore suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patients matched normal data is unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it to accumulated normal samples. We propose and examine personalized extensions of pathway statistics, Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), to generate individualized pathway aberrance score (iPAS). Collected microarray data of normal tissue of lung and colon mucosa is served as reference to investigate a number of cancer individuals of lung adenocarcinoma and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified amino acid synthesis and interconversion pathway is a good indicator of lung adenocarcinoma (AUC 0.982 at independent validation). We also suggest a new approach for discovering rare mutations that have functional impact in the context of pathway by iteratively combining rare mutations until no more mutations with pathway impact can be added. The approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data. Clinical importance of the method is providing pathway interpretation of single cancer even though its matched normal data is unavailable.Abstract 1 List of Figures 5 List of Tables 6 1. Introduction 7 1.1 Existing pathway analysis approaches (Group to group) 7 1.1.1 Importance of pathway analysis 8 1.1.2 Component of pathway analysis 9 1.1.3 Classification of existing pathway analysis approaches 17 1.2 Personalized pathway analysis 32 1.3 Purpose and novelty of this study 36 1.4 Outline of thesis 37 2. Methods and materials 39 2.1 Gene expression data 39Docto

An Analysis of Global Gene Expression Resulting from Exposure to Energetic Materials

AN ANALYSIS OF GLOBAL GENE EXPRESSION RESULTING FROM EXPOSURE TO ENERGETIC MATERIALS A Dissertation Presented for the Doctor of Philosophy Degree University of Tennessee, Knoxville VERNON LASHAWN MCINTOSH JR. August 2010 Dedication This dissertation is dedicated to my family. My mother and father Debra and Vernon McIntosh instilled in me the respect for academic excellence and the drive maximize my potential. Early on, my younger brother Kyle started showing signs of a shared interest in biology thus my desire to be a positive role model for him kept me motivated. Last but certainly not least, my loving wife and best friend Nichole has been there to offer love and support throughout my entire undergraduate and graduate degrees. It’s difficult to imagine making it this far without her (and that’s not just because she paid the bills). Abstract Characteristic transcriptional biomarkers have been identified for microbial cultures exposed to 2, 4, 6-trinitrotoluene (TNT), 2, 6-dinitrotoluene (DNT), or triacetone-triperoxide (TATP). This study describes the generation of expression profiles for exposure to each compound, the functional significance of each response, and the identification of the characteristic alterations in gene expression associated with exposure to each compound. Expression profiles were generated from a total of three different candidate organisms: Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida. Common to all three organisms, TNT exposure resulted in increased expression of genes involved in toxin resistance and drug efflux systems. The S.cerevisiae and E.coli expression profiles were both characterized by increased expression of genes involved in iron-sulfur cluster assembly, sulfur containing amino acids, sulfate transport and assimilation and the metabolism of nitrogen compounds. Only E.coli and Saccharomyces were used to generate DNT induced expression profiles; both profiles exhibited high degrees of similarity with each organism’s respective TNT profiles. This was especially true of the E.coli profile where 25 of the 30 alterations were also observed after exposure to TNT. A computational discriminant functional analysis was performed to identify characteristic biomarkers for each exposure. For each compound a set of transcriptional biomarkers (10 or less) was developed. An additional set of biomarkers was developed encompassing both TNT and DNT exposure. These sets of genes serve as a transcriptional fingerprint for exposure to each respective compound. The sensitivity and specificity of each transcriptional fingerprint is sufficient to correctly identify exposure to energetic materials against a background of non-energetic compound exposures. This study makes several novel contributions to the greater body of scientific knowledge: • This is the first documented study of the interactions of TATP in any biological system. • This is the first comprehensive gene expression study of the TNT response by P. putida, E.coli or E.coli. • This is the first application of computational class prediction in the development of biomarkers for exposure to energetic material

Identification of anti-beta₂ glycoprotein I auto-antibody regulated gene targets in the primary antiphospholipid syndrome using gene microarray analysis

Anti-Beta2-Glycoprotein I antibodies (anti-b2GPI) are strongly associated with thrombosis in patients with primary antiphospholipid syndrome (PAPS). Anti-b2GPI activate endothelial cells (EC) resulting in a pro-thrombotic and pro-inflammatory phenotype. In order to characterise EC gene regulation in response to anti-b2GPI, early global gene expression was assessed in human umbilical vein endothelial cells (HUVEC) in response to affinity purified anti-b2GPI. Sera were collected from patients with PAPS and IgG was purified using HiTrap Protein G Sepharose columns. Polyclonal anti-b2GPI were prepared by passing patient IgG through NHS activated sepharose coupled to human b2GPI. Anti-b2GPI preparations were characterized by confirming their b2GPI co-factor dependence, binding to b2GPI and ability to induce leukocyte adhesion molecule expression and IL-8 production in vitro. Two microarray experiments tested differential global gene expression in 6 individual HUVEC donors in response to 5 different PAPS polyclonal anti-b2GPI (50 mg/ml) compared to 5 normal control IgG (50 mg/ml) after 4 hours incubation . Total HUVEC RNA was extracted and cRNA was prepared and hybridised to Affymetrix HG-133A (Exp.1) and HG-133A_2 (Exp.2) gene chips. Data were analyzed using a combination of the MAS 5.0 (Affymetrix) and GeneSpring (Agilent) software programmes. Significant change in gene expression was defined as greater than two fold increase or decrease in expression (p<0.05). Novel genes not previously associated with PAPS were induced including chemokines CCL20, CXCL3, CX3CL1, CXCL5, CXCL2 and CXCL1, the receptors Tenascin C, OLR1, IL-18 receptor 1 and growth factors, CSF2, CSF3, IL-6, IL1b and FGF18. Downregulated genes were transcription factors/signaling molecules including ID2. Microarray results were confirmed for selected genes (CSF3, CX3CL1, FGF18, ID2, SOD2, Tenascin C) using quantitative real-time RT-PCR analysis. This study revealed a complex anti-b2GPI-regulated gene expression profile in HUVEC in vitro. The novel chemokines and pro-inflammatory cytokines identified in this study may contribute to the vasculopathy associated with PAPS.EThOS - Electronic Theses Online ServiceGBUnited Kingdo