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Microarray Data Set (microarray + data_set)

Distribution by Scientific Domains

Medical Sciences	40%
Mathematics and Statistics	40%

Selected Abstracts

Incorporating gene functional annotations in detecting differential gene expression

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2006
Wei Pan
Summary., The importance of incorporating existing biological knowledge, such as gene functional annotations in gene ontology, in analysing high throughput genomic and proteomic data is being increasingly recognized. In the context of detecting differential gene expression, however, the current practice of using gene annotations is limited primarily to validations. Here we take a direct approach to incorporating gene annotations into mixture models for analysis. First, in contrast with a standard mixture model assuming that each gene of the genome has the same distribution, we study stratified mixture models allowing genes with different annotations to have different distributions, such as prior probabilities. Second, rather than treating parameters in stratified mixture models independently, we propose a hierarchical model to take advantage of the hierarchical structure of most gene annotation systems, such as gene ontology. We consider a simplified implementation for the proof of concept. An application to a mouse microarray data set and a simulation study demonstrate the improvement of the two new approaches over the standard mixture model. [source]

Differential analysis of DNA microarray gene expression data

MOLECULAR MICROBIOLOGY, Issue 4 2003
G. Wesley Hatfield
Summary Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t -test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t -test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance. [source]

Nonparametric Inference for Local Extrema with Application to Oligonucleotide Microarray Data in Yeast Genome

BIOMETRICS, Issue 2 2006
Peter X.-K.
Summary Identifying local extrema of expression profiles is one primary objective in some cDNA microarray experiments. To study the replication dynamics of the yeast genome, for example, local peaks of hybridization intensity profiles correspond to putative replication origins. We propose a nonparametric kernel smoothing (NKS) technique to detect local hybridization intensity extrema across chromosomes. The novelty of our approach is that we base our inference procedures on equilibrium points, namely those locations at which the first derivative of the intensity curve is zero. The proposed smoothing technique provides both point and interval estimation for the location of local extrema. Also, this technique can be used to test for the hypothesis of either one or multiple suspected locations being the true equilibrium points. We illustrate the proposed method on a microarray data set from an experiment designed to study the replication origins in the yeast genome, in that the locations of autonomous replication sequence (ARS) elements are identified through the equilibrium points of the smoothed intensity profile curve. Our method found a few ARS elements that were not detected by the current smoothing methods such as the Fourier convolution smoothing. [source]

Two independent gene signatures in pediatric t(4;11) acute lymphoblastic leukemia patients

EUROPEAN JOURNAL OF HAEMATOLOGY, Issue 5 2009
Luca Trentin
Abstract Objective:, Gene expression profiles become increasingly more important for diagnostic procedures, allowing clinical predictions including treatment response and outcome. However, the establishment of specific and robust gene signatures from microarray data sets requires the analysis of large numbers of patients and the application of complex biostatistical algorithms. Especially in case of rare diseases and due to these constrains, diagnostic centers with limited access to patients or bioinformatic resources are excluded from implementing these new technologies. Method:, In our study we sought to overcome these limitations and for proof of principle, we analyzed the rare t(4;11) leukemia disease entity. First, gene expression data of each t(4;11) leukemia patient were normalized by pairwise subtraction against normal bone marrow (n = 3) to identify significantly deregulated gene sets for each patient. Result:, A ,core signature' of 186 commonly deregulated genes present in each investigated t(4;11) leukemia patient was defined. Linking the obtained gene sets to four biological discriminators (HOXA gene expression, age at diagnosis, fusion gene transcripts and chromosomal breakpoints) divided patients into two distinct subgroups: the first one comprised infant patients with low HOXA genes expression and the MLL breakpoints within introns 11/12. The second one comprised non-infant patients with high HOXA expression and MLL breakpoints within introns 9/10. Conclusion:, A yet homogeneous leukemia entity was further subdivided, based on distinct genetic properties. This approach provided a simplified way to obtain robust and disease-specific gene signatures even in smaller cohorts. [source]

Meta-Analyses Qualify Metzincins and Related Genes as Acute Rejection Markers in Renal Transplant Patients

AMERICAN JOURNAL OF TRANSPLANTATION, Issue 2 2010
S. Rödder
Definition of acute renal allograft rejection (AR) markers remains clinically relevant. Features of T-cell,mediated AR are tubulointerstitial and vascular inflammation associated with excessive extracellular matrix (ECM) remodeling, regulated by metzincins, including matrix metalloproteases (MMP). Our study focused on expression of metzincins (METS), and metzincins and related genes (MARGS) in renal allograft biopsies using four independent microarray data sets. Our own cases included normal histology (N, n = 20), borderline changes (BL, n = 4), AR (n = 10) and AR + IF/TA (n = 7). MARGS enriched in all data sets were further examined on mRNA and/or protein level in additional patients. METS and MARGS differentiated AR from BL, AR + IF/TA and N in a principal component analysis. Their expression changes correlated to Banff t- and i-scores. Two AR classifiers, based on METS (including MMP7, TIMP1), or on MARGS were established in our own and validated in the three additional data sets. Thirteen MARGS were significantly enriched in AR patients of all data sets comprising MMP7, -9, TIMP1, -2, thrombospondin2 (THBS2) and fibrillin1. RT-PCR using microdissected glomeruli/tubuli confirmed MMP7, -9 and THBS2 microarray results; immunohistochemistry showed augmentation of MMP2, -9 and TIMP1 in AR. TIMP1 and THBS2 were enriched in AR patient serum. Therefore, differentially expressed METS and MARGS especially TIMP1, MMP7/-9 represent potential molecular AR markers. [source]