scholarly journals Integrated gene expression analysis of multiple microarray data sets based on a normalization technique and on adaptive connectionist model

2004 ◽  
Author(s):  
Liang Goh ◽  
N. Kasabov
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Microarray Data ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Connectionist Model

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4133 ◽  
Author(s):  
Shirley Bikel ◽  
Leonor Jacobo-Albavera ◽  
Fausto Sánchez-Muñoz ◽  
Fernanda Cornejo-Granados ◽  
Samuel Canizales-Quinteros ◽  
...  
Keyword(s):  
Gene Expression ◽  
Y Chromosome ◽  
Expression Analysis ◽  
Microarray Data ◽  
Gene Expression Analysis ◽  
Transcriptome Profiling ◽  
Liver Cells ◽  
Background Fluorescence ◽  
Kegg Pathways ◽  
The Absolute

Background In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. Methods We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6–10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). Results From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Discussion Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

Paramaterless Clustering Techniques for Gene Expression Analysis

2011 ◽  
pp. 155-173
Author(s):  
Vincent S. Tseng ◽  
Ching-Pin Kao
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
In Silico Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
High Quality ◽  
Or Gene

In recent years, clustering analysis has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in meeting the requirements of automation, high quality, and high efficiency at the same time. In this chapter, we discuss the issue of parameterless clustering technique for gene expression analysis. We introduce two novel, parameterless and efficient clustering methods that fit for analysis of gene expression data. The unique feature of our methods is they incorporate the validation techniques into the clustering process so that high quality results can be obtained. Through experimental evaluation, these methods are shown to outperform other clustering methods greatly in terms of clustering quality, efficiency, and automation on both of synthetic and real data sets.

scholarly journals Gene Expression Analysis using Markov Chains extracted from RNNs

2007 ◽  
Vol 10 (2) ◽  
Author(s):  
Igor Lorenzato Almeida ◽  
Denise Regina Pechmann ◽  
Adelmo Luis Cechin
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Markov Chains ◽  
Microarray Data ◽  
Recurrent Neural Networks ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
New Approach ◽  
Several Variables ◽  
Gene Expression Levels

This paper present a new approach for the analysis of gene expres- sion, by extracting a Markov Chain from trained Recurrent Neural Networks (RNNs). A lot of microarray data is being generated, since array technologies have been widely used to monitor simultaneously the expression pattern of thou- sands of genes. Microarray data is highly specialized, involves several variables in which are complex to express and analyze. The challenge is to discover how to extract useful information from these data sets. So this work proposes the use of RNNs for data modeling, due to their ability to learn complex temporal non-linear data. Once a model is obtained for the data, it is possible to ex- tract the acquired knowledge and to represent it through Markov Chains model. Markov Chains are easily visualized in the form of states graphs, which show the influences among the gene expression levels and their changes in time

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