Abstract: HGF, also known as “Scatter Factor”, is a mesenchymal cytokine that acts on epithelial and endothelial cells by promoting a highly integrated biological program, hereafter referred to as “invasive growth”. This program involves coordinated control of basic cellular functions including dissociation and migration (“scattering”), invasion of extracellular matrix, proliferation, prevention of apoptosis and polarization. As a consequence, complex developmental processes take place, such as branched morphogenesis of epithelia and angiogenesis. Oncogenic activation by overexpression or point mutation of the gene encoding the tyrosine kinase receptor for HGF, c-MET, is involved in the progression of tumors towards the invasive-metastatic phenotype. To identify genes involved in Met-driven invasive growth, we explored the transcriptional response of mouse liver cells to HGF at different time points. Two different microarray platforms were adopted, consisting respectively of high-density spotted cDNAs (Incyte) and in-situ synthesized oligonucleotides (Affymetrix). Global exploration of 25’000 gene transcripts yielded over 1500 transcriptionally regulated sequences, corresponding to genes involved in the control of the basic biological functions underlying the invasive growth program: transcription, signal transduction, apoptosis, proliferation, cytoskeleton organization, motility and adhesion. Joint analysis of the data obtained by the two platforms allowed identification of genes with more consistent and reproducible regulation. Meta-analysis on genomic expression datasets obtained from breast carcinoma showed that expression of genes belonging to the HGF signature is correlated to cancer progression.

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Abstract: As the microarray technology is emerging as a widely used tool to investigate gene expression and function, laboratories over the world have produced and are producing a huge amount of data, which demand advanced and specialized computational tools to process them. Clustering methods have been successfully applied to such data to reorganize the data and extract biological information from them. But the classical clustering methods [1] such as k-means and hierarchical clustering have some intrinsic limits such as the linear, pair-wise nature of the similarity metrics (which fail to highlight non-linear substructures of the data) and the univocal assignment of each gene to one cluster (which may fail to highlight cluster-to-cluster relationships) [2]. Here we introduce a novel method for clustering microarray data, named Fuzzy Map Clustering (FMC), which may partly overcome these limits. Basically, the clustering process of FMC starts from identification of an initial set of clusters by calculating the “density” around each data point (object), that is, the average proximity of its K nearest other objects (K neighbours) and choosing the ones that have the highest density among all their K neighbors. K can be a fixed number of choice or the number of neighbors within a distance threshold. Then, each object in the dataset is assigned a fuzzy membership to all the defined clusters (a vector containing a percentage of membership to all the clusters). Membership is assigned so that similar objects have similar fuzzy membership vectors. Membership assignment is optimized by measuring how the fuzzy membership vector of one object can be approximated by the vectors of its neighbors. Finally, a process based on the merging of adjacent clusters and fuzzy membership reassignment is reiterated until the number of clusters is reduced to a fixed one decided by the operator. Our computational experiments have shown that FMC can correctly reveal the true cluster structure of the dataset if such structure exists, even if the clusters contained in the dataset have arbitrary shape. And perhaps the basic idea underlying FMC points out a new way to develop novel clustering methods with good mathematical foundation.

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Abstract: The increasing pace at which DNA microarray-based genomic expression profiles are generated and published poses the issue of efficient and reliable comparison between datasets obtained by different laboratories and on different microarray platforms. Statistical analysis of microarray data is in continuous evolution, and several procedures have been described for detection and weighing of systematic and random errors coming from the highly parallel -but poorly replicated- microarray expression data. However, data obtained from different microarray platforms may be of substantially different nature. This is particularly evident when comparing two commonly used platforms, spotted cDNA microarrays and High-Density Oligonucleotide (HDO) microarrays of the Affymetrix type. cDNA microarrays yield a reproducible ratio between two signals, deriving respectively from the reference and from the sample. Conversely, absolute signals tend to vary across microarrays. Therefore, cDNA microarray data have to be analyzed with statistics handling repeated measurements or paired data, such as paired T-test. In the case of HDO microarrays, an absolute signal level is obtained from each single mRNA sample. As a consequence, non-paired statistics have to be applied to this type of data. Given the intrinsic differences between cDNA microarrays, data analysis procedures have generally been developed on one of the two platforms and only in some cases adapted to the other, however without a specific focus on systematic comparison and validation across platforms. It is still unclear whether data obtained in the two systems can be treated, compared and eventually merged under a common analysis framework. We addressed these issues by generating expression profiles from the same RNAs with both microarray platforms and by developing an analysis procedure in which inter-platform differences in data treatment are reduced to the minimum essential. We then developed a novel statistical test specifically designed to handle multiple comparisons against the same reference condition (eg many points of stimulation against one unstimulated control). In the Multiple Comparison (MulCom) test, regulated genes are identified by a ‘tunable’ statistic test weighing expression change in each stimulation point against replicate variability calculated across the whole set of stimulation points.