Quantification of Gene Expression Based on Microarray Experiment

Summary Various statistical models have been proposed for detecting differential gene expression in data from microarray experiments. Given such detection, we are usually interested in describing the differential expression patterns. Due to the large number of genes that are typically analysed in microarray experiments, possibly more than ten thousand, the tasks of interpretation and communication of all the corresponding statistical models pose a considerable challenge, except perhaps in the simplest experiment involving only two groups. A further challenge is to find methods to summarize the resulting models. These challenges increase with experimental complexity.Biologists often wish to sort genes into ‘classes’ with similar response profiles/patterns. So, in this paper we describe a likelihood approach for assigning genes to these different class patterns for data from a replicated experimental design.The number of potential patterns increases very quickly as the number of combinations in the experimental design increases. In a two group experimental design there are only three patterns required to describe the mean response: up, down and no difference. For a factorial design with three treatments there are 13 different patterns, and with four levels there are 75 potential patterns to be considered, and so on. The approach is applied to the identification of differential response patterns in gene expression from a microarray experiment using RNAextracted from the leaves of Arabidopsis thaliana plants. We compare patterns of response found using additive and multiplicative models. A multiplicative model is more commonly used in the statistical analysis of microarray data because of the variance stabilizing properties of the logarithmic function. Then the error structure of the model is taken to be log-Normal. On the other hand, for the additive model the gene expression value is modeled directly as being from a gamma distribution which successfully accounts for the constant coefficient of variation often observed. Appropriate visualization displays for microarray data are important as a way of communicating the patterns of response amongst the genes. Here we use graphical ‘icons’ to represent the patterns of up/down and no response and two alternative displays, the Gene-plot and a grid layout to provide rapid overall summaries of the gene expression patterns.

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GXD’s RNA-Seq and Microarray Experiment Search: using curated metadata to reliably find mouse expression studies of interest

Database ◽

10.1093/database/baaa002 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 1

Author(s):

Constance M Smith ◽

James A Kadin ◽

Richard M Baldarelli ◽

Jonathan S Beal ◽

Olin Blodgett ◽

...

Keyword(s):

Gene Expression ◽

Microarray Experiment ◽

Gene Expression Omnibus ◽

Easy Access ◽

Free Text ◽

Rna Seq ◽

Endogenous Gene ◽

Expression Studies ◽

Text Searching ◽

Study Type

Abstract The Gene Expression Database (GXD), an extensive community resource of curated expression information for the mouse, has developed an RNA-Seq and Microarray Experiment Search (http://www.informatics.jax.org/gxd/htexp_index). This tool allows users to quickly and reliably find specific experiments in ArrayExpress and the Gene Expression Omnibus (GEO) that study endogenous gene expression in wild-type and mutant mice. Standardized metadata annotations, curated by GXD, allow users to specify the anatomical structure, developmental stage, mutated gene, strain and sex of samples of interest, as well as the study type and key parameters of the experiment. These searches, powered by controlled vocabularies and ontologies, can be combined with free text searching of experiment titles and descriptions. Search result summaries include link-outs to ArrayExpress and GEO, providing easy access to the expression data itself. Links to the PubMed entries for accompanying publications are also included. More information about this tool and GXD can be found at the GXD home page (http://www.informatics.jax.org/expression.shtml). Database URL: http://www.informatics.jax.org/expression.shtml

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Integrated Quantitative Neuro-transcriptome Analysis of Several Brain Areas in Human Trisomy 21

10.21203/rs.3.rs-745690/v1 ◽

2021 ◽

Author(s):

Alejandra Rodriguez-Ortiz ◽

Julio Cesar Montoya-Villegas ◽

Felipe Garcia-Vallejo ◽

Yecid Mina-Paz

Keyword(s):

Gene Expression ◽

Gene Expression Pattern ◽

Microarray Experiment ◽

Chromosome 21 ◽

Brain Areas ◽

Chromosomal Disorder ◽

Brain Transcriptome ◽

Human Genes ◽

Geo Dataset ◽

Ratio Correlation

Abstract Background Although Down syndrome (DS) is a trisomy of chromosome 21 being the most frequent human chromosomal disorder mainly associated with variables dysfunctions. Objective In this context, we aimed to analyze and compare the disruption of transcriptome of several brain areas from individuals with DS and euploid controls as a new approach to consider a global systemic differential disruption of gene expression beyond of chromosome 21. Methods We used data from a DNA microarray experiment with ID GSE59630 previously deposited in the GEO DataSet of NCBI database. The array contained log2 values of 17,537 human genes expressed in several aeras of human brain. We calculated the differential gene expression (Z-ratio) of all genes. Results We found several differences in gene expression along the DS brain transcriptome, not only in the genes located at chromosome 21 but in other chromosomes. Moreover, we registered the lowest Z-ratio correlation between the age ranks of 16–22 weeks of gestation and 39–42 years (R 2 = 0.06) and the highest Z-ratio correlation between the age ranks of 30–39 years and 40–42 years (R 2 = 0.89). The analysis per brain areas showed that the hippocampus and the cerebellar cortex had the most different gene expression pattern when compared to the brain as a whole. Conclusions Our results support the hypothesis of a systemic imbalance of brain protein homeostasis, or proteostasis network of cognitive and neuroplasticity process as new model to explain the important effect on the neurophenotype of trisomy that occur not only in loci of chromosome 21 but also in genes located in other chromosomes.

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Cluster Analysis of Gene Expression Data

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch045 ◽

2011 ◽

pp. 289-296

Author(s):

Alan Wee-Chung Liew ◽

Ngai-Fong Law ◽

Hong Yan

Keyword(s):

Gene Expression ◽

Cluster Analysis ◽

Gene Expression Data ◽

Biological Networks ◽

Microarray Experiment ◽

Hybridization Experiment ◽

Cancer Prognosis ◽

Expression Data ◽

Functional Roles ◽

Exploratory Technique

Important insights into gene function can be gained by gene expression analysis. For example, some genes are turned on (expressed) or turned off (repressed) when there is a change in external conditions or stimuli. The expression of one gene is often regulated by the expression of other genes. A detail analysis of gene expression information will provide an understanding about the inter-networking of different genes and their functional roles. DNA microarray technology allows massively parallel, high throughput genome-wide profiling of gene expression in a single hybridization experiment [Lockhart & Winzeler, 2000]. It has been widely used in numerous studies over a broad range of biological disciplines, such as cancer classification (Armstrong et al., 2002), identification of genes relevant to a certain diagnosis or therapy (Muro et al., 2003), investigation of the mechanism of drug action and cancer prognosis (Kim et al., 2000; Duggan et al., 1999). Due to the large number of genes involved in microarray experiment study and the complexity of biological networks, clustering is an important exploratory technique for gene expression data analysis. In this article, we present a succinct review of some of our work in cluster analysis of gene expression data.

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Role of GSH in the modulation of NOS-2 expression in the weaned mammary gland

Biochemical Society Transactions ◽

10.1042/bst0331397 ◽

2005 ◽

Vol 33 (6) ◽

pp. 1397-1398

Author(s):

R. Zaragozá ◽

E.R. García-Trevijano ◽

V.J. Miralles ◽

M. Mata ◽

C. García ◽

...

Keyword(s):

Gene Expression ◽

Mammary Gland ◽

Mrna Expression ◽

Expression Patterns ◽

Microarray Experiment ◽

Oligonucleotide Microarray ◽

Lactating Mammary Gland ◽

Buthionine Sulphoximine

GSH delivery to the lactating mammary gland is essential for the maintenance of lactation as its decrease leads to apoptosis and involution of the mammary gland. In fact, it has already been demonstrated that some of the changes in gene expression found in the lactating mammary gland after forced weaning are reproduced in rats treated with buthionine sulphoximine to deplete GSH levels. An oligonucleotide microarray experiment would give us a better knowledge of the mRNA expression patterns during lactation and after weaning and the possible functions of GSH in the modulation of these events.

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Clustering expressed genes on the basis of their association with a quantitative phenotype

Genetics Research ◽

10.1017/s0016672305007822 ◽

2005 ◽

Vol 86 (3) ◽

pp. 193-207 ◽

Cited By ~ 39

Author(s):

ZHENYU JIA ◽

SHIZHONG XU

Keyword(s):

Gene Expression ◽

Linear Model ◽

Complex Traits ◽

Real Data ◽

Microarray Experiment ◽

Cluster Assignment ◽

Model Based Clustering ◽

Binary Phenotype ◽

Continuous Phenotype

Cluster analyses of gene expression data are usually conducted based on their associations with the phenotype of a particular disease. Many disease traits have a clearly defined binary phenotype (presence or absence), so that genes can be clustered based on the differences of expression levels between the two contrasting phenotypic groups. For example, cluster analysis based on binary phenotype has been successfully used in tumour research. Some complex diseases have phenotypes that vary in a continuous manner and the method developed for a binary trait is not immediately applicable to a continuous trait. However, understanding the role of gene expression in these complex traits is of fundamental importance. Therefore, it is necessary to develop a new statistical method to cluster expressed genes based on their association with a quantitative trait phenotype. We developed a model-based clustering method to classify genes based on their association with a continuous phenotype. We used a linear model to describe the relationship between gene expression and the phenotypic value. The model effects of the linear model (linear regression coefficients) represent the strength of the association. We assumed that the model effects of each gene follow a mixture of several multivariate Gaussian distributions. Parameter estimation and cluster assignment were accomplished via an Expectation-Maximization (EM) algorithm. The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease.

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Comparison and Identification of Estrogen-Receptor Related Gene Expression Profiles in Breast Cancer of Different Ethnic Origins

Breast Cancer Basic and Clinical Research ◽

10.4137/bcbcr.s626 ◽

2008 ◽

Vol 1 ◽

pp. BCBCR.S626 ◽

Cited By ~ 1

Author(s):

Hsiao-Wei Chen ◽

Hsuan-Cheng Huang ◽

Yi-Shing Lin ◽

King-Jen Chang ◽

Wen-Hung Kuo ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Estrogen Receptor ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Microarray Experiment ◽

Breast Cancer Patients ◽

Related Gene ◽

Increased Risk ◽

Ethnic Origins

The interactions between genetic variants in estrogen receptor (ER) have been identified to be associated with an increased risk of breast cancer. Available evidence indicates that genetic variance within a population plays a crucial role in the occurrence of breast cancer. Thus, the comparison and identification of ER-related gene expression profiles in breast cancer of different ethnic origins could be useful for the development of genetic variant cancer therapy. In this study, we performed microarray experiment to measure the gene expression profiles of 59 Taiwanese breast cancer patients; and through comparative bioinformatics analysis against published U.K. datasets, we revealed estrogen-receptor (ER) related gene expression between Taiwanese and British patients. In addition, SNP databases and statistical analysis were used to elucidate the SNPs associated with ER status. Our microarray results indicate that the expression pattern of the 65 genes in ER+ patients was dissimilar from that of the ER- patients. Seventeen mutually exclusive genes in ER-related breast cancer of the two populations with more than one statistically significant SNP in genotype and allele frequency were identified. These 17 genes and their related SNPs may be important in population-specific ER regulation of breast cancer. This study provides a global and feasible approach to study population-unique SNPs in breast cancer of different ethnic origins.

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Creation of gene expression database on preeclampsia-affected human placenta

10.1101/102012 ◽

2017 ◽

Author(s):

Oleksandr Lykhenko ◽

Alina Frolova ◽

Maria Obolenska

Keyword(s):

Gene Expression ◽

Human Placenta ◽

Gene Networks ◽

Mode Of Delivery ◽

Microarray Experiment ◽

Mesh Term ◽

Online Resources ◽

Control Groups ◽

Biological Phenomena ◽

Average Size

AbstractPublication of gene expression raw data in open access at online resources like NCBI or ArrayExpress made it possible to use these data for cross-experiment integrative analysis and make new insights into biological phenomena. However, most popular of the present online resources are meant to be archives rather than ready for immediate access and interpretation databases. Data uploaded by independent contributors is not standardized and sometimes incomplete and needs further processing before it is ready for the analysis. Hence, the need for a specialized database appears.Given in this article is the description of the database that was created after processing a collection of 33 relevant datasets on pre-eclampsia-affected human placenta. Data processing includes the choice of relevant experiments from ArrayExpress database, the experiment sample attributes standardization according to MeSH term dictionary and Experimental Factor Ontology and the completion of missing data using information from the corresponding articles and authors.A database of more than 1000 samples contains sufficient sample-wise metadata for them to be arranged into relevant case-control groups. Metadata includes information on biological specimen, donor’s diagnosis, gestational age, mode of delivery etc. The average size of these groups will be higher than it is in separate experiments. This will reduce experiment bias and enhance statistical accuracy of the subsequent analysis such as search for differentially expressed genes or inferring gene networks. The article concludes with the guidelines for the microarray experiment metadata uploading for future contributors.

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Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments

Cancer Informatics ◽

10.1177/1176935117705381 ◽

2017 ◽

Vol 16 ◽

pp. 117693511770538 ◽

Cited By ~ 2

Author(s):

Fenghai Duan ◽

Ye Xu

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Pathway Analysis ◽

Turning Points ◽

Breast Cancer Diagnosis ◽

Microarray Experiment ◽

Gene Expression Omnibus ◽

Affymetrix Microarray ◽

Before And After ◽

Network Relationship

Purpose: To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. Methods: A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. Results: In total, 1640 probe sets (genes) were reliably identified to have turning points along with the age at diagnosis in their expression profiling, of which 927 expressed lower after turning points and 713 expressed higher after the turning points. K-means clustered them into 3 groups with turning points centering at 54, 62.5, and 72, respectively. The pathway analysis showed that the identified genes were actively involved in various cancer-related functions or networks. Conclusions: In this article, we applied the nonparametric multivariate adaptive splines method to a publicly available gene expression data and successfully identified genes with expressions varying before and after breast cancer diagnosis.

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Multiclass Molecular Classification

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch209 ◽

2011 ◽

pp. 1352-1357

Author(s):

Chia Huey Ooi

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Microarray Experiment ◽

Molecular Classification ◽

Expression Data ◽

Microscopic Appearance ◽

Expression Levels ◽

Similar Appearance ◽

Cancerous Cells

Molecular classification involves the classification of samples into groups of biological phenotypes. Studies on molecular classification generally focus on cancer for the following reason: Molecular classification of tumor samples from patients into different molecular types or subtypes is vital for diagnosis, prognosis, and effective treatment of cancer (Slonim, Tamayo, Mesirov, Golub, and Lander, 2000). Traditionally, such classification relies on observations regarding the location (Slonim et al., 2000) and microscopic appearance of the cancerous cells (Garber et al., 2001). These methods have proven to be slow and ineffective; there is no way of predicting with reliable accuracy the progress of the disease, since tumors of similar appearance have been known to take different paths in the course of time. With the advent of the microarray technology, data regarding the gene expression levels in each tumor sample may now prove to be a useful tool in molecular classification. This is because gene expression data provide snapshots of the activities within the cells and thus, the profile of the state of the cells in the tissue. The use of microarrays for gene expression profiling was first published in 1995 (Schena, Shalon, Davis, and Brown, 1995). In a typical microarray experiment, the expression levels of up to 10,000 or more genes are measured in each sample. The high-dimensionality of the data means that feature selection (FS) plays a crucial role in aiding the classification process by reducing the dimensionality of the input to the classification process. In the context of FS, the terms gene and feature will be used interchangeably in the context of gene expression data.

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