DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.

/pdf/cluster-analysis-for-gene-expression-data-a-survey-n507v99tm6.pdf

Cluster analysis for gene expression data: a survey

Molecular and Functional Profiling of Memory CD8 T Cell Differentiation

Multiple sclerosis is a demyelinating disease, characterized by inflammation in the brain and spinal cord, possibly due to autoimmunity. Large-scale sequencing of cDNA libraries, derived from plaques dissected from brains of patients with multiple sclerosis (MS), indicated an abundance of transcripts for osteopontin (OPN). Microarray analysis of spinal cords from rats paralyzed by experimental autoimmune encephalomyelitis (EAE), a model of MS, also revealed increased OPN transcripts. Osteopontin-deficient mice were resistant to progressive EAE and had frequent remissions, and myelin-reactive T cells in OPN-/- mice produced more interleukin 10 and less interferon-gamma than in OPN+/+ mice. Osteopontin thus appears to regulate T helper cell-1 (TH1)-mediated demyelinating disease, and it may offer a potential target in blocking development of progressive MS.

The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease

The cell wall determines the shape of plant cells and is also the primary interface for pathogen interactions. The structure of the cell wall can be modified in response to developmental and environmental cues, for example to strengthen the wall and to create barriers to pathogen ingress. The ectopic lignin 1-1 and 1-2 (eli1-1 and eli1-2) mutations lead to an aberrant deposition of lignin, a complex phenylpropanoid polymer. We show that the eli1 mutants occur in the cellulose synthase gene CESA3 in Arabidopsis thaliana and cause reduced cellulose synthesis, providing further evidence for the function of multiple CESA subunits in cellulose synthesis. We show that reduced levels of cellulose synthesis, caused by mutations in cellulose synthase genes and in genes affecting cell expansion, activate lignin synthesis and defense responses through jasmonate and ethylene and other signaling pathways. These observations suggest that mechanisms monitoring cell wall integrity can activate lignification and defense responses.

Reduced cellulose synthesis invokes lignification and defense responses in Arabidopsis thaliana

Background
'Fold-change' cutoffs have been widely used in microarray assays to identify genes that are differentially expressed between query and reference samples. More accurate measures of differential expression and effective data-normalization strategies are required to identify high-confidence sets of genes with biologically meaningful changes in transcription. Further, the analysis of a large number of expression profiles is facilitated by a common reference sample, the construction of which must be carefully addressed.

/pdf/within-the-fold-assessing-differential-expression-measures-4juhhx0rjl.pdf

Within the fold: assessing differential expression measures and reproducibility in microarray assays

The cDNA microarray is one technological approach that has the potential to accurately measure changes in global mRNA expression levels. We report an assessment of an optimized cDNA microarray platform to generate accurate, precise and reliable data consistent with the objective of using microarrays as an acquisition platform to populate gene expression databases. The study design consisted of two independent evaluations with 70 arrays from two different manufactured lots and used three human tissue sources as samples: placenta, brain and heart. Overall signal response was linear over three orders of magnitude and the sensitivity for any element was estimated to be 2 pg mRNA. The calculated coefficient of variation for differential expression for all non-differentiated elements was 12–14% across the entire signal range and did not vary with array batch or tissue source. The minimum detectable fold change for differential expression was 1.4. Accuracy, in terms of bias (observed minus expected differential expression ratio), was less than 1 part in 10 000 for all non-differentiated elements. The results presented in this report demonstrate the reproducible performance of the cDNA microarray technology platform and the methods provide a useful framework for evaluating other technologies that monitor changes in global mRNA expression.

/pdf/an-evaluation-of-the-performance-of-cdna-microarrays-for-24snmwbahu.pdf

An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

Mice lacking a functional cyclooxygenase-2 (COX-2) gene develop abnormal kidneys that contain hypoplastic glomeruli and reduced proximal tubular mass, and they often die of renal failure A compari

https://www.physiology.org/doi/pdf/10.1152/ajprenal.00134.2004

Defective expression of Tamm-Horsfall protein/uromodulin in COX-2-deficient mice increases their susceptibility to urinary tract infections

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An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

Defective expression of Tamm-Horsfall protein/uromodulin in COX-2-deficient mice increases their susceptibility to urinary tract infections