Identifying time-lagged gene clusters using gene expression data

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.     

Significance analysis of functional categories in gene expression studies: a structured permutation approach

MOTIVATION: In high-throughput genomic and proteomic experiments, investigators monitor expression across a set of experimental conditions. To gain an understanding of broader biological phenomena, researchers have until recently been limited to post hoc analyses of significant gene lists.Method: We describe a general framework, significance analysis of function and expression (SAFE), for conducting valid tests of gene categories ab initio. SAFE is a two-stage, permutation-based method that can be applied to various experimental designs, accounts for the unknown correlation among genes and enables permutation-based estimation of error rates. RESULTS: The utility and flexibility of SAFE is illustrated with a microarray dataset of human lung carcinomas and gene categories based on Gene Ontology and the Protein Family database. Significant gene categories were observed in comparisons of (1) tumor versus normal tissue, (2) multiple tumor subtypes and (3) survival times. AVAILABILITY: Code to implement SAFE in the statistical package R is available from the authors. SUPPLEMENTARY INFORMATION: http://www.bios.unc.edu/~fwright/SAFE.     

Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer

MOTIVATION: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. RESULTS: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. AVAILABILITY: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm     

Identifying highly conserved and highly differentiated gene ontology categories in human populations

Detecting and interpreting certain system-level characteristics associated with human population genetic differences is a challenge for human geneticists. In this study, we conducted a population genetic study using the HapMap genotype data to identify certain special Gene Ontology (GO) categories associated with high/low genetic difference among 11 Hapmap populations. Initially, the genetic differences in each gene region among these populations were measured using allele frequency, linkage disequilibrium (LD) pattern, and transferability of tagSNPs. The associations between each GO term and these genetic differences were then identified. The results showed that cellular process, catalytic activity, binding, and some of their sub-terms were associated with high levels of genetic difference, and genes involved in these functional categories displayed, on average, high genetic diversity among different populations. By contrast, multicellular organismal processes, molecular transducer activity, and some of their sub-terms were associated with low levels of genetic difference. In particular, the neurological system process under the multicellular organismal process category had low levels of genetic difference; the neurological function also showed high evolutionary conservation between species in some previous studies. These results may provide a new insight into the understanding of human evolutionary history at the system-level.     

Transient expression for functional gene analysis using Populus protoplasts

Despite the availability of the Populus genome sequence and the development of genetic, genomic, and transgenic approaches for its improvement, the lengthy life span of Populus and the cumbersome process required for its transformation have impeded rapid characterization of gene functions in Populus. Protoplasts provide a versatile and physiologically relevant cell system for high-throughput analysis and functional characterization of plant genes. Here, a highly efficient transient expression system using Populus mesophyll protoplasts was developed based on the following three steps. The first step involved formulating a new enzyme cocktail containing 2 % Cellulase C2605 and 0.5 % Pectinase P2611, which was shown to enable efficient large-scale isolation of homogenous Populus mesophyll protoplasts. The second step involved optimization of transfection conditions, such as the polyethylene glycol concentration and amount of plasmid DNA to ensure a >80 % transfection efficiency for Populus protoplasts. The third step involved using the Populus protoplast transient expression system to successfully determine the subcellular localizations of proteins, emulate signaling events during pathogen infection, and prepare protein extracts for Western blotting and protein–protein interaction assays. This rapid and highly efficient transient gene expression system in Populus mesophyll protoplasts will facilitate the rapid identification of gene functions and elucidation of signaling pathways in Populus.     

Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns

MOTIVATIONS AND RESULTS: Gene groups that are significantly related to a disease can be detected by conducting a series of gene expression experiments. This work is aimed at discovering special types of gene groups that satisfy the following property. In each group, its member genes are found to be one-to-one contained in pre-determined intervals of gene expression level with a large frequency in one class of cells but are never found unanimously in these intervals in the other class of cells. We call these gene groups emerging patterns, to emphasize the patterns' frequency changes between two classes of cells. We use effective discretization and gene selection methods to obtain the most discriminatory genes. We also use efficient algorithms to derive the patterns from these genes. According to our studies on the ALL/AML dataset and the colon tumor dataset, some patterns, which consist of one or more genes, can reach a high frequency of 90%, or even 100%. In other words, they nearly or fully dominate one class of cells, even though they rarely occur in the other class. The discovered patterns are used to classify new cells with a higher accuracy than other reported methods. Based on these patterns, we also conjecture the possibility of a personalized treatment plan which converts colon tumor cells into normal cells by modulating the expression levels of a few genes.     

Unifying gene expression measures from multiple platforms using factor analysis

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms.We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit.The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/.     

Identifying glucagon-like peptide-1 mimetics using a novel functional reporter gene high-throughput screening assay

Glucagon-like peptide-1 (GLP-1) stimulates insulin and inhibits glucagon secretion and therefore could potentially be used to treat diabetes type II. However, its therapeutic use is limited by its short half-life in vivo, due mainly to enzymatic degradation by dipeptidyl peptidase IV (DPP-IV). Developing GLP-1 analogs with greater bioactivity is therefore an important step toward using them therapeutically. Accordingly, we aimed to identify GLP-1 mimetic peptides by creating a high-throughput screening (HTS) assay of a phage displayed (PhD) peptide library. This assay was functionally based using the GLP-1 receptor (GLP-1R) gene. Rat GLP-1R cDNA was transfected into CHO/enhanced green fluorescent protein (EGFP) cells by lipofection. The resulting stable, recombinant cell line functionally expressed the GLP-1R and a cAMP-responsive EGFP reporter gene, to monitor receptor activation, and was used to screen a PhD dodecapeptide library. After four rounds of selection, 10 positive clones were selected based on functional evaluation and sequenced. Three sequences were obtained, corresponding to three different domains of GLP-1 (Group 1: 22-34; Group 2: 18-29; and Group 3: 6-17). The Group 3 peptide had the highest bioactivity, was synthesized, and designated KS-12. Importantly, KS-12 activated GLP-1R in vitro and reduced blood glucose levels in a dose-dependent manner when administered to Chinese Kunming mice. Although KS-12 was not as effective as GLP-1, it was significantly resistant to DPP-IV both in vitro and in vivo. Thus, this study provides a novel way to screen DPP-IV resistant agonist peptides of GLP-1 from a PhD peptide library using the functional reporter gene HTS assay.     

Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients

MOTIVATION: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. RESULTS: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis.     

Classification using functional data analysis for temporal gene expression data

MOTIVATION: Temporal gene expression profiles provide an important characterization of gene function, as biological systems are predominantly developmental and dynamic. We propose a method of classifying collections of temporal gene expression curves in which individual expression profiles are modeled as independent realizations of a stochastic process. The method uses a recently developed functional logistic regression tool based on functional principal components, aimed at classifying gene expression curves into known gene groups. The number of eigenfunctions in the classifier can be chosen by leave-one-out cross-validation with the aim of minimizing the classification error. RESULTS: We demonstrate that this methodology provides low-error-rate classification for both yeast cell-cycle gene expression profiles and Dictyostelium cell-type specific gene expression patterns. It also works well in simulations. We compare our functional principal components approach with a B-spline implementation of functional discriminant analysis for the yeast cell-cycle data and simulations. This indicates comparative advantages of our approach which uses fewer eigenfunctions/base functions. The proposed methodology is promising for the analysis of temporal gene expression data and beyond. AVAILABILITY: MATLAB programs are available upon request.     

Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray

The development of cancer is the result of a series of molecular changes occurring in the cell. These events lead to changes in the expression level of numerous genes that result in different phenotypic characteristics of tumors. In this report we describe the assembly and utilization of a 5766 member cDNA microarray to study the differences in gene expression between normal and neoplastic human ovarian tissues. Several genes that may have biological relevance in the process of ovarian carcinogenesis have been identified through this approach. Analyzing the results of microarray hybridizations may provides new leads for tumor diagnosis and intervention.     

Identifying set-wise differential co-expression in gene expression microarray data

Background  

Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.     

Multiple and interrelated functional asymmetries in rat brain.

Normal rats rotate (turn in circles) at night and in response to drugs (e.g. d-amphetamine) during the day. Rats with known circling biases were injected with [1,2-³H]-deoxy-d-glucose, decapitated and glucose utilization was assessed in several brain structures. Most structures showed evidence of functional brain asymmetry. Asymmetries were of three different kinds: (1) a difference in activity between sides of the brain contralateral and ipsilateral to the direction of rotation (midbrain, striatum); (2) a difference in activity between left and right sides (frontal cortex, hippocampus); and (3) an absolute difference in activity between sides that was correlated to the rate of either rotation (thalamus, hypothalamus) or random movement (cerebellum). Amphetamine, administered 15 minutes before a deoxyglucose injection in other rats, altered some asymmetries (striatum, frontal cortex, hippocampus) but not others (midbrain, thalamus, hypothalamus, cerebellum). Different asymmetries appear to be organized along different dimensions in both the rat and human brains.     

Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.     

Identifying functional links between genes using conserved chromosomal proximity

Conservation of proximity of a pair of genes across multiple genomes generally indicates that their functions could be linked. Here, we present a systematic evaluation using 42 complete microbial genomes from 25 phylogenetic groups to test the reliability of this observation in predicting function for genes. We find a relationship between the number of phylogenetic groups in which a gene pair is proximate and the probability that the pair belongs to a common pathway. Our method produces 1586 links between ortholog families substantiated by observed proximity in genomes representing at least three phylogenetic groups. Of the pairs annotated in the KEGG database, 80% are in the same biological pathway in KEGG.