Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of 5 steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5 minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.     

Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies

We are building an open-access database of regional human brain expression designed to allow the genome-wide assessment of genetic variability on expression. Array and RNA sequencing technologies make assessment of genome-wide expression possible. Human brain tissue is a challenging source for this work because it can only be obtained several and variable hours post-mortem and after varying agonal states. These variables alter RNA integrity in a complex manner. In this report, we assess the effect of post-mortem delay, agonal state and age on gene expression, and the utility of pH and RNA integrity number as predictors of gene expression as measured on 1266 Affymetrix Exon Arrays. We assessed the accuracy of the array data using QuantiGene, as an independent non-PCR-based method. These quality control parameters will allow database users to assess data accuracy. We report that within the parameters of this study post-mortem delay, agonal state and age have little impact on array quality, array data are robust to variable RNA integrity, and brain pH has only a small effect on array performance. QuantiGene gave very similar expression profiles as array data. This study is the first step in our initiative to make human, regional brain expression freely available.     

Binary analysis and optimization-based normalization of gene expression data

MOTIVATION: Most approaches to gene expression analysis use real-valued expression data, produced by high-throughput screening technologies, such as microarrays. Often, some measure of similarity must be computed in order to extract meaningful information from the observed data. The choice of this similarity measure frequently has a profound effect on the results of the analysis, yet no standards exist to guide the researcher. RESULTS: To address this issue, we propose to analyse gene expression data entirely in the binary domain. The natural measure of similarity becomes the Hamming distance and reflects the notion of similarity used by biologists. We also develop a novel data-dependent optimization-based method, based on Genetic Algorithms (GAs), for normalizing gene expression data. This is a necessary step before quantizing gene expression data into the binary domain and generally, for comparing data between different arrays. We then present an algorithm for binarizing gene expression data and illustrate the use of the above methods on two different sets of data. Using Multidimensional Scaling, we show that a reasonable degree of separation between different tumor types in each data set can be achieved by working solely in the binary domain. The binary approach offers several advantages, such as noise resilience and computational efficiency, making it a viable approach to extracting meaningful biological information from gene expression data.     

Some statistical issues in microarray gene expression data

In this paper we discuss some of the statistical issues that should be considered when conducting experiments involving microarray gene expression data. We discuss statistical issues related to preprocessing the data as well as the analysis of the data. Analysis of the data is discussed in three contexts: class comparison, class prediction and class discovery. We also review the methods used in two studies that are using microarray gene expression to assess the effect of exposure to radiofrequency (RF) fields on gene expression. Our intent is to provide a guide for radiation researchers when conducting studies involving microarray gene expression data.     

Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees

MOTIVATION: Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem. RESULTS: This paper describes a new framework for representing a set of multi-dimensional gene expression data as a Minimum Spanning Tree (MST), a concept from the graph theory. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXpression data Clustering Analysis and VisualizATiOn Resource (EXCAVATOR). To demonstrate its effectiveness, we have tested it on three data sets, i.e. expression data from yeast Saccharomyces cerevisiae, expression data in response of human fibroblasts to serum, and Arabidopsis expression data in response to chitin elicitation. The test results are highly encouraging. AVAILABILITY: EXCAVATOR is available on request from the authors.     

BlotBase: a northern blot database

With the availability of high-throughput gene expression analysis, multiple public expression databases emerged, mostly based on microarray expression data. Although these databases are of significant biomedical value, they do hold significant drawbacks, especially concerning the reliability of single gene expression profiles obtained by microarray data. Simultaneously, reliable data on an individual gene's expression are often published as single northern blots in individual publications. These data were not yet available for high-throughput screening. To reduce the gap between high-throughput expression data and individual highly reliable expression data, we designed a novel database "BlotBase", a freely and easily accessible database, currently containing approximately 700 published northern blots of human or mouse origin (http://www.medicalgenomics.org/Databases/BlotBase). As the database is open for public data submission, we expect this database to quickly become a large expression profiling resource, eventually providing higher reliability in high-throughput gene expression analysis. Realizing BlotBase, Pubmed was searched manually and by computer based text mining methods to obtain publications containing northern blot results. Subsequently, northern blots were extracted and expression values of different tissues calculated utilizing Image J. All data were made available through a user friendly web front end. The data may be searched by either full text search or list of available northern blots of a specific tissue. Northern blot expression profiles were displayed by three expression states as well as a bar chart, allowing for automated evaluation. Furthermore, we integrated additional features, e.g. instant access to the corresponding RNA sequence or primer design tools making further expression analysis more convenient. Finally, through a semiautomatic submission system this database was opened to the bioinformatics community.     

The Influence of the Global Gene Expression Shift on Downstream Analyses

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice

Predicting phenotypes using genome-wide genetic variation and gene expression data is useful in several fields, such as human biology and medicine, as well as in crop and livestock breeding. However, for phenotype prediction using gene expression data for mammals, studies remain scarce, as the available data on gene expression profiling are currently limited. By integrating a few sources of relevant data that are available in mice, this study investigated the accuracy of phenotype prediction for several physiological traits. Gene expression data from two tissues as well as single nucleotide polymorphisms (SNPs) were used. For the studied traits, the variance of the effects of the expression levels was more likely to differ among the genes than were the effects of SNPs. For the glucose concentration, the total cholesterol amount, and the total tidal volume, the accuracy by cross validation tended to be higher when the gene expression data rather than the SNP genotype data were used, and a statistically significant increase in the accuracy was obtained when the gene expression data from the liver were used alone or jointly with the SNP genotype data. For these traits, there were no additional gains in accuracy from using the gene expression data of both the liver and lung compared to that of individual use. The accuracy of prediction using genes that were selected differently was examined; the use of genes with a higher tissue specificity tended to result in an accuracy that was similar to or greater than that associated with the use of all of the available genes for traits such as the glucose concentration and total cholesterol amount. Although relatively few animals were evaluated, the current results suggest that gene expression levels could be used as explanatory variables. However, further studies are essential to confirm our findings using additional animal samples.     

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.     

Differential Expression and Network Inferences through Functional Data Modeling

Summary .  Time course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article, we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene-expression profile as a random functional transformation of the scale, amplitude, and phase of a common curve. Inferences about the gene-specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time-transformation functions allow us to examine gene networks while accounting for features of the gene-expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression.     

Missing value imputation for microRNA expression data by using a GO-based similarity measure

Missing values are commonly present in microarray data profiles. Instead of discarding genes or samples with incomplete expression level, missing values need to be properly imputed for accurate data analysis. The imputation methods can be roughly categorized as expression level-based and domain knowledge-based. The first type of methods only rely on expression data without the help of external data sources, while the second type incorporates available domain knowledge into expression data to improve imputation accuracy. In recent years, microRNA (miRNA) microarray has been largely developed and used for identifying miRNA biomarkers in complex human disease studies. Similar to mRNA profiles, miRNA expression profiles with missing values can be treated with the existing imputation methods. However, the domain knowledge-based methods are hard to be applied due to the lack of direct functional annotation for miRNAs. With the rapid accumulation of miRNA microarray data, it is increasingly needed to develop domain knowledge-based imputation algorithms specific to miRNA expression profiles to improve the quality of miRNA data analysis. We connect miRNAs with domain knowledge of Gene Ontology (GO) via their target genes, and define miRNA functional similarity based on the semantic similarity of GO terms in GO graphs. A new measure combining miRNA functional similarity and expression similarity is used in the imputation of missing values. The new measure is tested on two miRNA microarray datasets from breast cancer research and achieves improved performance compared with the expression-based method on both datasets. The experimental results demonstrate that the biological domain knowledge can benefit the estimation of missing values in miRNA profiles as well as mRNA profiles. Especially, functional similarity defined by GO terms annotated for the target genes of miRNAs can be useful complementary information for the expression-based method to improve the imputation accuracy of miRNA array data. Our method and data are available to the public upon request.