h-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data

<p>Abstract</p> <p>Background</p> <p>An ever increasing number of techniques are being used to find genes with similar profiles from microarray studies. Visualization of gene expression profiles can aid this process, potentially contributing to the identification of co-regulated genes and gene function as well as network development.</p> <p>Results</p> <p>We introduce the h-Profile plot to display gene expression profiles. Thumbnail versions of plots of gene expression profiles are plotted at coordinates such that profiles of similar shape are located in the same sector, with decreasing variance towards the origin. Negatively correlated profiles can easily be identified. A new method for selecting genes with fixed periodicity, but different phase and amplitude is described and used to demonstrate the use of the plots on cell cycle data.</p> <p>Conclusion</p> <p>Visualization tools for gene expression data are important and h-profile plots provide a timely contribution to the field. They allow the simultaneous visualization of many gene expression profiles and can be used for the identification of genes with similar or reversed profiles, the foundation step in many analyses.</p

Simpler Evaluation of Predictions and Signature Stability for Gene Expression Data

Scientific advances are raising expectations that patient-tailored treatment will soon be available. The development of resulting clinical approaches needs to be based on well-designed experimental and observational procedures that provide data to which proper biostatistical analyses are applied. Gene expression microarray and related technology are rapidly evolving. It is providing extremely large gene expression profiles containing many thousands of measurements. Choosing a subset from these gene expression measurements to include in a gene expression signature is one of the many challenges needing to be met. Choice of this signature depends on many factors, including the selection of patients in the training set. So the reliability and reproducibility of the resultant prognostic gene signature needs to be evaluated, in such a way as to be relevant to the clinical setting. A relatively straightforward approach is based on cross validation, with separate selection of genes at each iteration to avoid selection bias. Within this approach we developed two different methods, one based on forward selection, the other on genes that were statistically significant in all training blocks of data. We demonstrate our approach to gene signature evaluation with a well-known breast cancer data set

Impairment of organ-specific T cell negative selection by diabetes susceptibility genes: genomic analysis by mRNA profiling

BACKGROUND T cells in the thymus undergo opposing positive and negative selection processes so that the only T cells entering circulation are those bearing a T cell receptor (TCR) with a low affinity for self. The mechanism differentiating negative from positive selection is poorly understood, despite the fact that inherited defects in negative selection underlie organ-specific autoimmune disease in AIRE-deficient people and the non-obese diabetic (NOD) mouse strain RESULTS Here we use homogeneous populations of T cells undergoing either positive or negative selection in vivo together with genome-wide transcription profiling on microarrays to identify the gene expression differences underlying negative selection to an Aire-dependent organ-specific antigen, including the upregulation of a genomic cluster in the cytogenetic band 2F. Analysis of defective negative selection in the autoimmune-prone NOD strain demonstrates a global impairment in the induction of the negative selection response gene set, but little difference in positive selection response genes. Combining expression differences with genetic linkage data, we identify differentially expressed candidate genes, including Bim, Bnip3, Smox, Pdrg1, Id1, Pdcd1, Ly6c, Pdia3, Trim30 and Trim12. CONCLUSION The data provide a molecular map of the negative selection response in vivo and, by analysis of deviations from this pathway in the autoimmune susceptible NOD strain, suggest that susceptibility arises from small expression differences in genes acting at multiple points in the pathway between the TCR and cell death.This work was supported by grants from the NHMRC and the Juvenile Diabetes Research Foundation

Adsorption models of hybridization and post-hybridisation behaviour on oligonucleotide microarrays

Analysis of data from an Affymetrix Latin Square spike-in experiment indicates that measured fluorescence intensities of features on an oligonucleotide microarray are related to spike-in RNA target concentrations via a hyperbolic response function, generally identified as a Langmuir adsorption isotherm. Furthermore the asymptotic signal at high spike-in concentrations is almost invariably lower for a mismatch feature than for its partner perfect match feature. We survey a number of theoretical adsorption models of hybridization at the microarray surface and find that in general they are unable to explain the differing saturation responses of perfect and mismatch features. On the other hand, we find that a simple and consistent explanation can be found in a model in which equilibrium hybridization followed by partial dissociation of duplexes during the post-hybridization washing phase.Comment: 26 pages, 6 figures, some rearrangement of sections and some additions. To appear in J.Phys.(condensed matter

Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot

Visualisation methods for exploring microarray data are particularly important for gaining insight into data from gene expression experiments, such as those concerned with the development of an understanding of gene function and interactions. Further, good visualisation techniques are useful for outlier detection in microarray data and for aiding biological interpretation of results, as well as for presentation of overall summaries of the data. The biplot is particularly useful for the display of microarray data as both the genes and the chips can be simultaneously plotted. In this paper we describe several ordination techniques suitable for exploring microarray data, and we call these the GE-biplot, the Chip-plot and the Gene-plot. The general method is first evaluated on synthetic data simulated in accord with current biological interpretation of microarray data. Then it is applied to two well-known data sets, namely the colon data of Alon et al. (1999) and the leukaemia data of Golub et al. (1999). The usefulness of the approach for interpreting and comparing different analyses of the same data is demonstrated.

Detailed profiles for the genes which were within the sector shown in the previous h-profile plot

<p><b>Copyright information:</b></p><p>Taken from "h-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data"</p><p>http://www.biomedcentral.com/1471-2105/8/486</p><p>BMC Bioinformatics 2007;8():486-486.</p><p>Published online 20 Dec 2007</p><p>PMCID:PMC2257978.</p><p></p> The gene expression values are shown on the vertical axis on the left, and on the right, the mean centered gene expression profiles are shown. The profile of CLN1 is shown as a red solid bold line. All these genes have a correlation of .74 or higher with CLN1. The phase identification on the top axis identifies the first of the two samples which were identified to belong to the phase by Cho [12]

H-Profile plot using 352 genes selected from the Cho data set using equation 5 with a critical cutoff estimated using the third quartile of the simulated values of

<p><b>Copyright information:</b></p><p>Taken from "h-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data"</p><p>http://www.biomedcentral.com/1471-2105/8/486</p><p>BMC Bioinformatics 2007;8():486-486.</p><p>Published online 20 Dec 2007</p><p>PMCID:PMC2257978.</p><p></p> The profile plots show a time series plot of the transformed gene expression values, colored according to the phase in which the maximum gene expression value occurred. A number of gene profiles on the outer edge of the bulk of the thumbnails are identified, all of which were in de Lichtenberg 's top 300 list [11] and 9 in the Cho [12] list. By the geometry of the h-profile plot these genes can be expected to have the highest variance amongst the selected genes and therefore, in the sense of de Lichtenberg , to be strongly regulated

Statistical Analysis of Adsorption Models for Oligonucleotide Microarrays

Recent analyses have shown that the relationship between intensity measurements from high density oligonucleotide microarrays and known concentration is non linear. Thus many measurements of so-called gene expression are neither measures of transcript nor mRNA concentration as might be expected.Intensity as measured in such microarrays is a measurement of fluorescent dye attached to probe-target duplexes formed during hybridization of a sample to the probes on the microarray. We develop several dynamic adsorption models relating fluorescent dye intensity to target RNA concentration, the simplest of which is the equilibrium Langmuir isotherm, or hyperbolic response function. Using data from the Affymerix HG-U95A Latin Square experiment, we evaluate various physical models, including equilibrium and non-equilibrium models, by applying maximum likelihood methods. We show that for these data, equilibrium Langmuir isotherms with probe dependent parameters are appropriate. We describe how probe sequence information may then be used to estimate the parameters of the Langmuir isotherm in order to provide an improved measure of absolute target concentration.