Deconstructing language by comparative gene expression: from neurobiology to microarray

Language is a defining characteristic of our species that has emerged quite recently on an evolutionary timescale. Understanding the neurobiological substrates and genetic underpinnings of language constitutes a basic challenge for both neuroscience and genetics. The functional localization of language in the brain has been progressively refined over the last century through studies of aphasics and more recently through neuroimaging. Concurrently, structural specializations in these brain regions have been identified by virtue of their lateralization in humans and also through comparisons with homologous brain regions in non-human primate species. Comparative genomics has revealed the genome of our closest living relative, the chimpanzee, to be astonishingly similar to our own. To explore the role that changes in the regulation of gene expression have had in recent human evolution, several groups have used microarrays to compare expression levels for thousands of genes in the brain between humans and chimpanzees. By applying this approach to the increasingly refined peri-sylvian network of brain regions involved in language, it may be possible to discern functionally significant changes in gene expression that are universal among humans but unique to our species, thus casting light on the molecular basis of language in the brain.     

Deconstructing language by comparative gene expression: from neurobiology to microarray

Language is a defining characteristic of our species that has emerged quite recently on an evolutionary timescale. Understanding the neurobiological substrates and genetic underpinnings of language constitutes a basic challenge for both neuroscience and genetics. The functional localization of language in the brain has been progressively refined over the last century through studies of aphasics and more recently through neuroimaging. Concurrently, structural specializations in these brain regions have been identified by virtue of their lateralization in humans and also through comparisons with homologous brain regions in non-human primate species. Comparative genomics has revealed the genome of our closest living relative, the chimpanzee, to be astonishingly similar to our own. To explore the role that changes in the regulation of gene expression have had in recent human evolution, several groups have used microarrays to compare expression levels for thousands of genes in the brain between humans and chimpanzees. By applying this approach to the increasingly refined peri-sylvian network of brain regions involved in language, it may be possible to discern functionally significant changes in gene expression that are universal among humans but unique to our species, thus casting light on the molecular basis of language in the brain.     

Integration of amplified differential gene expression (ADGE) and DNA microarray

Amplified Differential Gene Expression (ADGE) provides a new concept that the ratios of differentially expressed genes are magnified before detection in order to improve both sensitivity and accuracy. This technology is now implemented with integration of DNA reassociation and PCR. The ADGE technique can be used either as a stand-alone method or in series with DNA microarray. ADGE is used in sample preprocessing and DNA microarray is used as a displaying system in the series combination. These two techniques are mutually synergistic: the quadratic magnification of ratios of differential gene expression achieved by ADGE improves the detection sensitivity and accuracy; the PCR amplification of templates enhances the signal intensity and reduces the requirement for large amounts of starting material; the high throughput for DNA microarray is maintained.     

Visualization of microarray gene expression data

Microarray gene expression data is used in various biological and medical investigations. Processing of gene expression data requires algorithms in data mining, process automation and knowledge discovery. Available data mining algorithms exploits various visualization techniques. Here, we describe the merits and demerits of various visualization parameters used in gene expression analysis.     

A novel gene expression index (GEI) with software support for comparing microarray gene signatures

This study was aimed to examine the validity of commonly used statistical tests for comparison of expression data from simulated and real gene signatures as well as pathway-characterized gene sets. A novel algorithm based on 10 sub-gradations (5 for up- and 5 for down-regulation) of fold-changes has been designed and testified using an Excel add-in software support. Our findings showed the limitations of conventional statistics for comparing the microarray gene expression data. However, the newly introduced Gene Expression Index (GEI) appeared to be more robust and straightforward for two-group comparison of normalized data. The software automation simplifies the task and the results are displayed in a comprehensive format including a color-coded bar showing the intensity of cumulative gene expression.     

机械点样DNA微点阵技术及其在基因表达分析上的应用(II)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

机械点样DNA微点阵技术及其在基因表达分析上的应用(I)

从芯片制作、芯片杂交、芯片扫读与图像分析、基因表达数据分析等方面,详细介绍了机械点样DNA微点阵技术及其应用于多基因表达分析的基本步骤与原理。     

Classification across gene expression microarray studies

Background  

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.     

Technology for microarray analysis of gene expression

The past year has demonstrated the versatility of microarrays for the analysis of whole model-organism genomes and has seen the development of chips to measure the expression of 40,000 human genes. Microarray technology has also become considerably more robust and sensitive. Technology enhancements include the use of noncontact printing methods, improved 2-color sample preparation, and statistically based software for data analysis.     

FuGEFlow: data model and markup language for flow cytometry

Background  

Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.     

Integrated olfactory receptor and microarray gene expression databases

Background  

Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction.     

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/     

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.     

Cluster-Rasch models for microarray gene expression data

Background

We propose two different formulations of the Rasch statistical models to the problem of relating gene expression profiles to the phenotypes. One formulation allows us to investigate whether a cluster of genes with similar expression profiles is related to the observed phenotypes; this model can also be used for future prediction. The other formulation provides an alternative way of identifying genes that are over- or underexpressed from their expression levels in tissue or cell samples of a given tissue or cell type.

Results

We illustrate the methods on available datasets of a classification of acute leukemias and of 60 cancer cell lines. For tumor classification, the results are comparable to those previously obtained. For the cancer cell lines dataset, we found four clusters of genes that are related to drug response for many of the 90 drugs that we considered. In addition, for each type of cell line, we identified genes that are over- or underexpressed relative to other genes.

Conclusions

The cluster-Rasch model provides a probabilistic model for describing gene expression patterns across samples and can be used to relate gene expression profiles to phenotypes.     

Sample size for gene expression microarray experiments

MOTIVATION: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size. RESULTS: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays. CONTACT: jchen@nctr.fda.gov.     

Weighted analysis of microarray gene expression using maximum-likelihood

MOTIVATION: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance. RESULTS: This paper develops a maximum-likelihood method for estimating gene expression using spot mean, variance and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical maximum-likelihood estimate (MLE) is shown to be a more efficient estimator of the mean than the 'conventional' estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics that do not require the use of all pixel data.The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show that the hierarchical MLE method leads to improved detection of differential gene expression particularly when 'outlier' spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off P-values of interest.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.