Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

ABSTRACT: Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

SUBMITTER: Tan Q

PROVIDER: S-EPMC6042830 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

Tan Qihua Q Thomassen Mads M Burton Mark M Mose Kristian Fredløv KF Andersen Klaus Ejner KE Hjelmborg Jacob J Kruse Torben T

Journal of integrative bioinformatics 20170606 2

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature ...[more]

PMID: 28753536

Similar Datasets

Functional assessment of time course microarray data.

Project description:MotivationTime-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.MethodsWe present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.ResultsSynthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

| S-EPMC2697656 | biostudies-literature

Extracting binary signals from microarray time-course data.

Project description:This article presents a new method for analyzing microarray time courses by identifying genes that undergo abrupt transitions in expression level, and the time at which the transitions occur. The algorithm matches the sequence of expression levels for each gene against temporal patterns having one or two transitions between two expression levels. The algorithm reports a P-value for the matching pattern of each gene, and a global false discovery rate can also be computed. After matching, genes can be sorted by the direction and time of transitions. Genes can be partitioned into sets based on the direction and time of change for further analysis, such as comparison with Gene Ontology annotations or binding site motifs. The method is evaluated on simulated and actual time-course data. On microarray data for budding yeast, it is shown that the groups of genes that change in similar ways and at similar times have significant and relevant Gene Ontology annotations.

| S-EPMC1920252 | biostudies-other

Multivariate curve resolution of time course microarray data.

Project description:BackgroundModeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.ResultsIn this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data.ConclusionProfiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.

| S-EPMC1539028 | biostudies-literature

Generalized correlation coefficient for genome-wide association analysis of cognitive ability in twins.

Project description:Despite a strong genetic background in cognitive function only a limited number of single nucleotide polymorphisms (SNPs) have been found in genome-wide association studies (GWASs). We hypothesize that this is partially due to mis-specified modeling concerning phenotype distribution as well as the relationship between SNP dosage and the level of the phenotype. To overcome these issues, we introduced an assumption-free method based on generalized correlation coefficient (GCC) in a GWAS of cognitive function in Danish and Chinese twins to compare its performance with traditional linear models. The GCC-based GWAS identified two significant SNPs in Danish samples (rs71419535, p = 1.47e-08; rs905838, p = 1.69e-08) and two significant SNPs in Chinese samples (rs2292999, p = 9.27e-10; rs17019635, p = 2.50e-09). In contrast, linear models failed to detect any genome-wide significant SNPs. The number of top significant genes overlapping between the two samples in the GCC-based GWAS was higher than when applying linear models. The GCC model identified significant genetic variants missed by conventional linear models, with more replicated genes and biological pathways related to cognitive function. Moreover, the GCC-based GWAS was robust in handling correlated samples like twin pairs. GCC is a useful statistical method for GWAS that complements traditional linear models for capturing genetic effects beyond the additive assumption.

| S-EPMC7746382 | biostudies-literature

Significance analysis of time course microarray experiments.

Project description:Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.

| S-EPMC1201697 | biostudies-literature

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data.

Project description:BackgroundTime-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.ResultsIn this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C1 and C2). We model the expression at C1 using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C2 is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.ConclusionIn both cases, the proposed method identified biologically significant genes.

| S-EPMC2435549 | biostudies-literature

Identifying significant temporal variation in time course microarray data without replicates.

Project description:An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels. Until recently, available methods for performing such significance tests required replicates of individual time points. This paper describes a replicate-free method that was developed as part of a study of the estrous cycle in the rat mammary gland in which no replicate data was collected.A temporal test statistic is proposed that is based on the degree to which data are smoothed when fit by a spline function. An algorithm is presented that uses this test statistic together with a false discovery rate method to identify genes whose expression profiles exhibit significant temporal variation. The algorithm is tested on simulated data, and is compared with another recently published replicate-free method. The simulated data consists both of genes with known temporal dependencies, and genes from a null distribution. The proposed algorithm identifies a larger percentage of the time-dependent genes for a given false discovery rate. Use of the algorithm in a study of the estrous cycle in the rat mammary gland resulted in the identification of genes exhibiting distinct circadian variation. These results were confirmed in follow-up laboratory experiments.The proposed algorithm provides a new approach for identifying expression profiles with significant temporal variation without relying on replicates. When compared with a recently published algorithm on simulated data, the proposed algorithm appears to identify a larger percentage of time-dependent genes for a given false discovery rate. The development of the algorithm was instrumental in revealing the presence of circadian variation in the virgin rat mammary gland during the estrous cycle.

| S-EPMC2682797 | biostudies-literature

Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis.

Project description:Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been studied to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is summation form of variable sets, is used for enhancing analysis of different block's relationship. By identifying relationships through multi-block data form, we can understand the association between the blocks is effective in understanding the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from Korean Association Resource (KARE) project which has combination of the SNP blocks, phenotype blocks, and disease block.

| S-EPMC6440675 | biostudies-literature

Non-parametric frailty Cox models for hierarchical time-to-event data.

Project description:We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.

| S-EPMC6451633 | biostudies-literature

A time-course microarray data analysis reveals consistent dysregulated genes and upstream microRNAs in autoantibody-mediated arthritis.

Project description:The purpose of this study is to identify key genes and microRNAs (miRNAs) involved in autoantibody-mediated arthritis (AMA).A time-course microarray data (ID: GSE27492) of peripheral blood leukocytes, ankle tissue, and synovial fluid from K/BxN mouse serum-transferred mice were downloaded from Gene Expression Omnibus. Those samples were collected at days 0, 1, 3, 7, 12, and 18 after serum injection. Limma of R was employed to identify differentially expressed genes (DEGs) in samples collected at days 1-18 compared with those collected at day 0. Consistent DEGs were obtained by taking the interaction of DEGs from different time points, followed by functional enrichment analysis. MiRNAs were screened out and constructed into regulatory network with DEGs using Cytoscape.In total, 17 consistent DEGs were obtained, including downregulated Ephx1 and upregulated AF251705, Adam8, Arg1, Basp1, Ccl2, Ccl7, Ccl9, Ccr2, Clec4a2, Clec4d, Cxcl1, Fabp5, Fcgr1, Gp49a, Il1rn, and Saa3. Those DEGs were associated with biological processes of immune response, inflammatory response, and defense response; chemokine signaling pathway; cytokine-cytokine receptor interaction; and NOD-like receptor signaling pathway. Additionally, 202 miRNAs were identified to have a regulatory effect on 9 of the 17 DEGs. Notably, miR-944, miR-374a, and miR374b were found to regulate the expression of Cxcl1, Ccl7, and Ccl2. Clec4d was targeted by 78 miRNAs.Our study reveals that 17 DEGs and 202 miRNAs may be associated with autoimmune disorder in the progression of AMA, which could guide future researches.

| S-EPMC5712098 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data