12,854 research outputs found
Identifying set-wise differential co-expression in gene expression microarray data
<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>dCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets.</p> <p>Conclusion</p> <p>dCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.</p
Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53
Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets.
Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region.
Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes
Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression
Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression
Diverse correlation structures in gene expression data and their utility in improving statistical inference
It is well known that correlations in microarray data represent a serious
nuisance deteriorating the performance of gene selection procedures. This paper
is intended to demonstrate that the correlation structure of microarray data
provides a rich source of useful information. We discuss distinct correlation
substructures revealed in microarray gene expression data by an appropriate
ordering of genes. These substructures include stochastic proportionality of
expression signals in a large percentage of all gene pairs, negative
correlations hidden in ordered gene triples, and a long sequence of weakly
dependent random variables associated with ordered pairs of genes. The reported
striking regularities are of general biological interest and they also have
far-reaching implications for theory and practice of statistical methods of
microarray data analysis. We illustrate the latter point with a method for
testing differential expression of nonoverlapping gene pairs. While designed
for testing a different null hypothesis, this method provides an order of
magnitude more accurate control of type 1 error rate compared to conventional
methods of individual gene expression profiling. In addition, this method is
robust to the technical noise. Quantitative inference of the correlation
structure has the potential to extend the analysis of microarray data far
beyond currently practiced methods.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS120 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Qualitative Assessment of Gene Expression in Affymetrix Genechip Arrays
Affymetrix Genechip microarrays are used widely to determine the simultaneous
expression of genes in a given biological paradigm. Probes on the Genechip
array are atomic entities which by definition are randomly distributed across
the array and in turn govern the gene expression. In the present study, we make
several interesting observations. We show that there is considerable
correlation between the probe intensities across the array which defy the
independence assumption. While the mechanism behind such correlations is
unclear, we show that scaling behavior and the profiles of perfect match (PM)
as well as mismatch (MM) probes are similar and immune to background
subtraction. We believe that the observed correlations are possibly an outcome
of inherent non-stationarities or patchiness in the array devoid of biological
significance. This is demonstrated by inspecting their scaling behavior and
profiles of the PM and MM probe intensities obtained from publicly available
Genechip arrays from three eukaryotic genomes, namely: Drosophila Melanogaster,
Homo Sapiens and Mus musculus across distinct biological paradigms and across
laboratories, with and without background subtraction. The fluctuation
functions were estimated using detrended fluctuation analysis (DFA) with fourth
order polynomial detrending. The results presented in this study provide new
insights into correlation signatures of PM and MM probe intensities and
suggests the choice of DFA as a tool for qualitative assessment of Affymetrix
Genechip microarrays prior to their analysis. A more detailed investigation is
necessary in order to understand the source of these correlations.Comment: 22 Pages, 7 Figures, 1 Tabl
Multiple tests of association with biological annotation metadata
We propose a general and formal statistical framework for multiple tests of
association between known fixed features of a genome and unknown parameters of
the distribution of variable features of this genome in a population of
interest. The known gene-annotation profiles, corresponding to the fixed
features of the genome, may concern Gene Ontology (GO) annotation, pathway
membership, regulation by particular transcription factors, nucleotide
sequences, or protein sequences. The unknown gene-parameter profiles,
corresponding to the variable features of the genome, may be, for example,
regression coefficients relating possibly censored biological and clinical
outcomes to genome-wide transcript levels, DNA copy numbers, and other
covariates. A generic question of great interest in current genomic research
regards the detection of associations between biological annotation metadata
and genome-wide expression measures. This biological question may be translated
as the test of multiple hypotheses concerning association measures between
gene-annotation profiles and gene-parameter profiles. A general and rigorous
formulation of the statistical inference question allows us to apply the
multiple hypothesis testing methodology developed in [Multiple Testing
Procedures with Applications to Genomics (2008) Springer, New York] and related
articles, to control a broad class of Type I error rates, defined as
generalized tail probabilities and expected values for arbitrary functions of
the numbers of Type I errors and rejected hypotheses. The resampling-based
single-step and stepwise multiple testing procedures of [Multiple Testing
Procedures with Applications to Genomics (2008) Springer, New York] take into
account the joint distribution of the test statistics and provide Type I error
control in testing problems involving general data generating distributions
(with arbitrary dependence structures among variables), null hypotheses, and
test statistics.Comment: Published in at http://dx.doi.org/10.1214/193940307000000446 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
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