Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Measure of Global Specialization and Spatial Clustering for the Identification of "Specialized" Agglomeration

The intensity of regional specialization in specific activities, and conversely, the level of industrial concentration in specific locations, has been used as a complementary evidence for the existence and significance of externalities. Additionally, economists have mainly focused the debate on disentangling the sources of specialization and concentration processes according to three vectors: natural advantages, internal, and external scale economies. The arbitrariness of partitions plays a key role in capturing these effects, while the selection of the partition would have to reflect the actual characteristics of the economy. Thus, the identification of spatial boundaries to measure specialization becomes critical, since most likely the model will be adapted to different scales of distance, and be influenced by different types of externalities or economies of agglomeration, which are based on the mechanisms of interaction with particular requirements of spatial proximity. This work is based on the analysis of the spatial aspect of economic specialization supported by the manufacturing industry case. The main objective is to propose, for discrete and continuous space: i) a measure of global specialization; ii) a local disaggregation of the global measure; and iii) a spatial clustering method for the identification of specialized agglomerations.

Development of a new galaxy classification technique and its application to the zCOSMOS survey

The purpose of this Thesis is to develop a robust and powerful method to classify galaxies from large surveys, in order to establish and confirm the connections between the principal observational parameters of the galaxies (spectral features, colours, morphological indices), and help unveil the evolution of these parameters from $z \sim 1$ to the local Universe. Within the framework of zCOSMOS-bright survey, and making use of its large database of objects ($\sim 10\,000$ galaxies in the redshift range $0 < z \lesssim 1.2$) and its great reliability in redshift and spectral properties determinations, first we adopt and extend the \emph{classification cube method}, as developed by Mignoli et al. (2009), to exploit the bimodal properties of galaxies (spectral, photometric and morphologic) separately, and then combining together these three subclassifications. We use this classification method as a test for a newly devised statistical classification, based on Principal Component Analysis and Unsupervised Fuzzy Partition clustering method (PCA+UFP), which is able to define the galaxy population exploiting their natural global bimodality, considering simultaneously up to 8 different properties. The PCA+UFP analysis is a very powerful and robust tool to probe the nature and the evolution of galaxies in a survey. It allows to define with less uncertainties the classification of galaxies, adding the flexibility to be adapted to different parameters: being a fuzzy classification it avoids the problems due to a hard classification, such as the classification cube presented in the first part of the article. The PCA+UFP method can be easily applied to different datasets: it does not rely on the nature of the data and for this reason it can be successfully employed with others observables (magnitudes, colours) or derived properties (masses, luminosities, SFRs, etc.). The agreement between the two classification cluster definitions is very high. ``Early'' and ``late'' type galaxies are well defined by the spectral, photometric and morphological properties, both considering them in a separate way and then combining the classifications (classification cube) and treating them as a whole (PCA+UFP cluster analysis). Differences arise in the definition of outliers: the classification cube is much more sensitive to single measurement errors or misclassifications in one property than the PCA+UFP cluster analysis, in which errors are ``averaged out'' during the process. This method allowed us to behold the \emph{downsizing} effect taking place in the PC spaces: the migration between the blue cloud towards the red clump happens at higher redshifts for galaxies of larger mass. The determination of $M_{\mathrm{cross}}$ the transition mass is in significant agreement with others values in literature.

Comparing Different Approaches for Clustering Categorical Data

There are different ways to do cluster analysis of categorical data in the literature and the choice among them is strongly related to the aim of the researcher, if we do not take into account time and economical constraints. Main approaches for clustering are usually distinguished into model-based and distance-based methods: the former assume that objects belonging to the same class are similar in the sense that their observed values come from the same probability distribution, whose parameters are unknown and need to be estimated; the latter evaluate distances among objects by a defined dissimilarity measure and, basing on it, allocate units to the closest group. In clustering, one may be interested in the classification of similar objects into groups, and one may be interested in finding observations that come from the same true homogeneous distribution. But do both of these aims lead to the same clustering? And how good are clustering methods designed to fulfil one of these aims in terms of the other? In order to answer, two approaches, namely a latent class model (mixture of multinomial distributions) and a partition around medoids one, are evaluated and compared by Adjusted Rand Index, Average Silhouette Width and Pearson-Gamma indexes in a fairly wide simulation study. Simulation outcomes are plotted in bi-dimensional graphs via Multidimensional Scaling; size of points is proportional to the number of points that overlap and different colours are used according to the cluster membership.

Progettazione e sviluppo di una versione distribuita di un algoritmo di subspace clustering

Il task del data mining si pone come obiettivo l'estrazione automatica di schemi significativi da grandi quantità di dati. Un esempio di schemi che possono essere cercati sono raggruppamenti significativi dei dati, si parla in questo caso di clustering. Gli algoritmi di clustering tradizionali mostrano grossi limiti in caso di dataset ad alta dimensionalità, composti cioè da oggetti descritti da un numero consistente di attributi. Di fronte a queste tipologie di dataset è necessario quindi adottare una diversa metodologia di analisi: il subspace clustering. Il subspace clustering consiste nella visita del reticolo di tutti i possibili sottospazi alla ricerca di gruppi signicativi (cluster). Una ricerca di questo tipo è un'operazione particolarmente costosa dal punto di vista computazionale. Diverse ottimizzazioni sono state proposte al fine di rendere gli algoritmi di subspace clustering più efficienti. In questo lavoro di tesi si è affrontato il problema da un punto di vista diverso: l'utilizzo della parallelizzazione al fine di ridurre il costo computazionale di un algoritmo di subspace clustering.

adaptive capabilities of the pi3k/akt/mtor pathway in acute myeloid leukemia revealed by the use of selective inhibitors

Because of its aberrant activation, the PI3K/AKT/mTOR signaling pathway represents a pharmacological target in blast cells from patients with acute myelogenous leukemia (AML). Using Reverse Phase Protein Microarrays (RPMA), we have analyzed 20 phosphorylated epitopes of the PI3K/Akt/mTor signal pathway of peripheral blood and bone marrow specimens of 84 patients with newly diagnosed AML. Fresh blast cells were grown for 2 h, 4 h or 20 h untreated or treated with a panel of phase I or phase II Akt allosteric inhibitors, either alone or in combination with the mTOR kinase inhibitor Torin1 or the broad RTK inhibitor Sunitinib. By unsupervised hierarchical clustering a strong phosphorylation/activity of most of the sampled members of the PI3K/Akt/mTOR pathway was observed in 70% of samples from AML patients. Remarkably, however, we observed that inhibition of Akt phosphorylation, as well as of its substrates, was transient, and recovered or even increased far above basal level after 20 h in 60% samples. We demonstrated that inhibition of Akt induces FOXO-dependent insulin receptor expression and IRS-1 activation, attenuating the effect of drug treatment by reactivation of PI3K/Akt. Consistent with this model we found that combined inhibition of Akt and RTKs is much more effective than either alone, revealing the adaptive capabilities of signaling networks in blast cells and highliting the limations of these drugs if used as monotherapy.

Clustering di ammassi di galassie con cataloghi otticamente selezionati

In questo lavoro di tesi si è studiato il clustering degli ammassi di galassie e la determinazione della posizione del picco BAO per ottenere vincoli sui parametri cosmologici. A tale scopo si è implementato un codice per la stima dell'errore tramite i metodi di jackknife e bootstrap. La misura del picco BAO confrontata con i modelli cosmologici, grazie all'errore stimato molto piccolo, è risultato in accordo con il modelli LambdaCDM, e permette di ottenere vincoli su alcuni parametri dei modelli cosmologici.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Sviluppo di algoritmi di grid data clustering basati su metodi statistici

Lo scopo del clustering è quindi quello di individuare strutture nei dati significative, ed è proprio dalla seguente definizione che è iniziata questa attività di tesi , fornendo un approccio innovativo ed inesplorato al cluster, ovvero non ricercando la relazione ma ragionando su cosa non lo sia. Osservando un insieme di dati ,cosa rappresenta la non relazione? Una domanda difficile da porsi , che ha intrinsecamente la sua risposta, ovvero l’indipendenza di ogni singolo dato da tutti gli altri. La ricerca quindi dell’indipendenza tra i dati ha portato il nostro pensiero all’approccio statistico ai dati , in quanto essa è ben descritta e dimostrata in statistica. Ogni punto in un dataset, per essere considerato “privo di collegamenti/relazioni” , significa che la stessa probabilità di essere presente in ogni elemento spaziale dell’intero dataset. Matematicamente parlando , ogni punto P in uno spazio S ha la stessa probabilità di cadere in una regione R ; il che vuol dire che tale punto può CASUALMENTE essere all’interno di una qualsiasi regione del dataset. Da questa assunzione inizia il lavoro di tesi, diviso in più parti. Il secondo capitolo analizza lo stato dell’arte del clustering, raffrontato alla crescente problematica della mole di dati, che con l’avvento della diffusione della rete ha visto incrementare esponenzialmente la grandezza delle basi di conoscenza sia in termini di attributi (dimensioni) che in termini di quantità di dati (Big Data). Il terzo capitolo richiama i concetti teorico-statistici utilizzati dagli algoritimi statistici implementati. Nel quarto capitolo vi sono i dettagli relativi all’implementazione degli algoritmi , ove sono descritte le varie fasi di investigazione ,le motivazioni sulle scelte architetturali e le considerazioni che hanno portato all’esclusione di una delle 3 versioni implementate. Nel quinto capitolo gli algoritmi 2 e 3 sono confrontati con alcuni algoritmi presenti in letteratura, per dimostrare le potenzialità e le problematiche dell’algoritmo sviluppato , tali test sono a livello qualitativo , in quanto l’obbiettivo del lavoro di tesi è dimostrare come un approccio statistico può rivelarsi un’arma vincente e non quello di fornire un nuovo algoritmo utilizzabile nelle varie problematiche di clustering. Nel sesto capitolo saranno tratte le conclusioni sul lavoro svolto e saranno elencati i possibili interventi futuri dai quali la ricerca appena iniziata del clustering statistico potrebbe crescere.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

Premature Osteoblast Clustering by Enamel Matrix Proteins Induces Osteoblast Differentiation through Up-Regulation of Connexin 43 and N-Cadherin

In recent years, enamel matrix derivative (EMD) has garnered much interest in the dental field for its apparent bioactivity that stimulates regeneration of periodontal tissues including periodontal ligament, cementum and alveolar bone. Despite its widespread use, the underlying cellular mechanisms remain unclear and an understanding of its biological interactions could identify new strategies for tissue engineering. Previous in vitro research has demonstrated that EMD promotes premature osteoblast clustering at early time points. The aim of the present study was to evaluate the influence of cell clustering on vital osteoblast cell-cell communication and adhesion molecules, connexin 43 (cx43) and N-cadherin (N-cad) as assessed by immunofluorescence imaging, real-time PCR and Western blot analysis. In addition, differentiation markers of osteoblasts were quantified using alkaline phosphatase, osteocalcin and von Kossa staining. EMD significantly increased the expression of connexin 43 and N-cadherin at early time points ranging from 2 to 5 days. Protein expression was localized to cell membranes when compared to control groups. Alkaline phosphatase activity was also significantly increased on EMD-coated samples at 3, 5 and 7 days post seeding. Interestingly, higher activity was localized to cell cluster regions. There was a 3 fold increase in osteocalcin and bone sialoprotein mRNA levels for osteoblasts cultured on EMD-coated culture dishes. Moreover, EMD significantly increased extracellular mineral deposition in cell clusters as assessed through von Kossa staining at 5, 7, 10 and 14 days post seeding. We conclude that EMD up-regulates the expression of vital osteoblast cell-cell communication and adhesion molecules, which enhances the differentiation and mineralization activity of osteoblasts. These findings provide further support for the clinical evidence that EMD increases the speed and quality of new bone formation in vivo.