Clustering Scatter Plots Using Data Depth Measures.

Clustering is rapidly becoming a powerful data mining technique, and has been broadly applied to many domains such as bioinformatics and text mining. However, the existing methods can only deal with a data matrix of scalars. In this paper, we introduce a hierarchical clustering procedure that can handle a data matrix of scatter plots. To more accurately reflect the nature of data, we introduce a dissimilarity statistic based on "data depth" to measure the discrepancy between two bivariate distributions without oversimplifying the nature of the underlying pattern. We then combine hypothesis testing with hierarchical clustering to simultaneously cluster the rows and columns of the data matrix of scatter plots. We also propose novel painting metrics and construct heat maps to allow visualization of the clusters. We demonstrate the utility and power of our new clustering method through simulation studies and application to a microbe-host-interaction study

Leveraging expression and network data for protein function prediction

2012 Summer.Includes bibliographical references.Protein function prediction is one of the prominent problems in bioinformatics today. Protein annotation is slowly falling behind as more and more genomes are being sequenced. Experimental methods are expensive and time consuming, which leaves computational methods to fill the gap. While computational methods are still not accurate enough to be used without human supervision, this is the goal. The Gene Ontology (GO) is a collection of terms that are the standard for protein function annotations. Because of the structure of GO, protein function prediction is a hierarchical multi-label classification problem. The classification method used in this thesis is GOstruct, which performs structured predictions that take into account all GO terms. GOstruct has been shown to work well, but there are still improvements to be made. In this thesis, I work to improve predictions by building new kernels from the data that are used by GOstruct. To do this, I find key representations of the data that help define what kernels perform best on the variety of data types. I apply this methodology to function prediction in two model organisms, Saccharomyces cerevisiae and Mus musculus, and found better methods for interpreting the data

Utilizing gene co-expression networks for comparative transcriptomic analyses

The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We begin with a review of currently utilized techniques available that can be used or adapted to compare gene co-expression networks. We also work to systematically determine the appropriate number of samples needed to construct reproducible gene co-expression networks for comparison purposes. In order to systematically compare these replicate networks, software to visualize the relationship between replicate networks was created to determine when the consistency of the networks begins to plateau and if this is affected by factors such as tissue type and sample size. Finally, we developed a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. A set of transcriptome datasets were utilized from publicly available sources as well as from collaborators. GTEx and Gene Expression Omnibus (GEO) RNA-seq datasets were used for the evaluation of the techniques proposed in this research. Skeletal cell datasets of closely related species and more evolutionarily distant organisms were also analyzed to investigate the evolutionary relationships of several skeletal cell types. We found evidence that data characteristics such as tissue origin, as well as the method used to construct gene co-expression networks, can substantially impact the number of samples required to generate reproducible networks. In particular, if a threshold is used to construct a gene co-expression network for downstream analyses, the number of samples used to construct the networks is an important consideration as many samples may be required to generate networks that have a reproducible edge order when sorted by edge weight. We also demonstrated the capabilities of our proposed method for comparing GCNs, Juxtapose, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. Finally, we applied our proposed method to skeletal cell networks and find evidence of conserved gene relationships within skeletal GCNs from the same species and identify modules of genes with similar embeddings across species that are enriched for biological processes involved in cartilage and osteoblast development. Furthermore, smaller sub-networks of genes reflect the phylogenetic relationships of the species analyzed using our gene embedding strategy to compare the GCNs. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species

Data Mining Using the Crossing Minimization Paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis

Anthropometry: An R Package for Analysis of Anthropometric Data

The development of powerful new 3D scanning techniques has enabled the generation of large up-to-date anthropometric databases which provide highly valued data to improve the ergonomic design of products adapted to the user population. As a consequence, Ergonomics and Anthropometry are two increasingly quantitative fields, so advanced statistical methodologies and modern software tools are required to get the maximum benefit from anthropometric data. This paper presents a new R package, called Anthropometry, which is available on the Comprehensive R Archive Network. It brings together some statistical methodologies concerning clustering, statistical shape analysis, statistical archetypal analysis and the statistical concept of data depth, which have been especially developed to deal with anthropometric data. They are proposed with the aim of providing effective solutions to some common anthropometric problems, such as clothing design or workstation design (focusing on the particular case of aircraft cockpits). The utility of the package is shown by analyzing the anthropometric data obtained from a survey of the Spanish female population performed in 2006 and from the 1967 United States Air Force survey. This manuscript is also contained in Anthropometry as a vignette

Statistical Algorithms and Bioinformatics Tools Development for Computational Analysis of High-throughput Transcriptomic Data

Next-Generation Sequencing technologies allow for a substantial increase in the amount of data available for various biological studies. In order to effectively and efficiently analyze this data, computational approaches combining mathematics, statistics, computer science, and biology are implemented. Even with the substantial efforts devoted to development of these approaches, numerous issues and pitfalls remain. One of these issues is mapping uncertainty, in which read alignment results are biased due to the inherent difficulties associated with accurately aligning RNA-Sequencing reads. GeneQC is an alignment quality control tool that provides insight into the severity of mapping uncertainty in each annotated gene from alignment results. GeneQC used feature extraction to identify three levels of information for each gene and implements elastic net regularization and mixture model fitting to provide insight in the severity of mapping uncertainty and the quality of read alignment. In combination with GeneQC, the Ambiguous Reads Mapping (ARM) algorithm works to re-align ambiguous reads through the integration of motif prediction from metabolic pathways to establish coregulatory gene modules for re-alignment using a negative binomial distribution-based probabilistic approach. These two tools work in tandem to address the issue of mapping uncertainty and provide more accurate read alignments, and thus more accurate expression estimates. Also presented in this dissertation are two approaches to interpreting the expression estimates. The first is IRIS-EDA, an integrated shiny web server that combines numerous analyses to investigate gene expression data generated from RNASequencing data. The second is ViDGER, an R/Bioconductor package that quickly generates high-quality visualizations of differential gene expression results to assist users in comprehensive interpretations of their differential gene expression results, which is a non-trivial task. These four presented tools cover a variety of aspects of modern RNASeq analyses and aim to address bottlenecks related to algorithmic and computational issues, as well as more efficient and effective implementation methods

Development of statistical methodologies applied to anthropometric data oriented towards the ergonomic design of products

Ergonomics is the scientific discipline that studies the interactions between human beings and the elements of a system and presents multiple applications in areas such as clothing and footwear design or both working and household environments. In each of these sectors, knowing the anthropometric dimensions of the current target population is fundamental to ensure that products suit as well as possible most of the users who make up the population. Anthropometry refers to the study of the measurements and dimensions of the human body and it is considered a very important branch of Ergonomics because its considerable influence on the ergonomic design of products. Human body measurements have usually been taken using rules, calipers or measuring tapes. These procedures are simple and cheap to carry out. However, they have one major drawback: the body measurements obtained and consequently, the human shape information, is imprecise and inaccurate. Furthermore, they always require interaction with real subjects, which increases the measure time and data collecting. The development of new three-dimensional (3D) scanning techniques has represented a huge step forward in the way of obtaining anthropometric data. This technology allows 3D images of human shape to be captured and at the same time, generates highly detailed and reproducible anthropometric measurements. The great potential of these new scanning systems for the digitalization of human body has contributed to promoting new anthropometric studies in several countries, such as United Kingdom, Australia, Germany, France or USA, in order to acquire accurate anthropometric data of their current population. In this context, in 2006 the Spanish Ministry of Health commissioned a 3D anthropometric survey of the Spanish female population, following the agreement signed by the Ministry itself with the Spanish associations and companies of manufacturing, distribution, fashion design and knitted sectors. A sample of 10415 Spanish females from 12 to 70 years old, randomly selected from the official Postcode Address File, was measured. The two main objectives of this study, which was conducted by the Biomechanics Institute of Valencia, were the following: on the one hand, to characterize the shape and body dimensions of the current Spanish women population to develop a standard sizing system that could be used by all clothing designers. On the other hand, to promote a healthy image of beauty through the representation of suited mannequins. In order to tackle both objectives, Statistics plays an essential role. Thus, the statistical methodologies presented in this PhD work have been applied to the database obtained from the Spanish anthropometric study. Clothing sizing systems classify the population into homogeneous groups (size groups) based on some key anthropometric dimensions. All members of the same group are similar in body shape and size, so they can wear the same garment. In addition, members of different groups are very different with respect to their body dimensions. An efficient and optimal sizing system aims at accommodating as large a percentage of the population as possible, in the optimum number of size groups that better describes the shape variability of the population. Besides, the garment fit for the accommodated individuals must be as good as possible. A very valuable reference related to sizing systems is the book Sizing in clothing: Developing effective sizing systems for ready-to-wear clothing, by Susan Ashdown. Each clothing size is defined from a person whose body measurements are located toward the central value for each of the dimensions considered in the analysis. The central person, which is considered as the size representative (the size prototype), becomes the basic pattern from which the clothing line in the same size is designed. Clustering is the statistical tool that divides a set of individuals in groups (clusters), in such a way that subjects of the same cluster are more similar to each other than to those in other groups. In addition, clustering defines each group by means of a representative individual. Therefore, it arises in a natural way the idea of using clustering to try to define an efficient sizing system. Specifically, four of the methodologies presented in this PhD thesis aimed at segmenting the population into optimal sizes, use different clustering methods. The first one, called trimowa, has been published in Expert Systems with Applications. It is based on using an especially defined distance to examine differences between women regarding their body measurements. The second and third ones (called biclustAnthropom and TDDclust, respectively) will soon be submitted in the same paper. BiclustAnthropom adapts to the field of Anthropometry a clustering method addressed in the specific case of gene expression data. Moreover, TDDclust uses the concept of statistical depth for grouping according to the most central (deep) observation in each size. As mentioned, current sizing systems are based on using an appropriate set of anthropometric dimensions, so clustering is carried out in the Euclidean space. In the three previous proposals, we have always worked in this way. Instead, in the fourth and last approach, called kmeansProcrustes, a clustering procedure is proposed for grouping taking into account the women shape, which is represented by a set of anatomical markers (landmarks). For this purpose, the statistical shape analysis will be fundamental. This contribution has been submitted for publication. A sizing system is intended to cover the so-called standard population, discarding the individuals with extreme sizes (both large and small). In mathematical language, these individuals can be considered outliers. An outlier is an observation point that is distant from other observations. In our case, a person with extreme anthopometric measurements would be considered as a statistical outlier. Clothing companies usually design garments for the standard sizes so that their market share is optimal. Nevertheless, with their foreign expansion, a lot of brands are spreading their collection and they already have a special sizes section. In last years, Internet shopping has been an alternative for consumers with extreme sizes looking for clothes that follow trends. The custom-made fabrication is other possibility with the advantage of making garments according to the customers' preferences. The four aforementioned methodologies (trimowa, biclustAnthropom, TDDclust and kmeansProcrustes) have been adapted to only accommodate the standard population. Once a particular garment has been designed, the assessing and analysis of fit is performed using one or more fit models. The fit model represents the body dimensions selected by each company to define the proportional relationships needed to achieve the fit the company has determined. The definition of an efficient sizing system relies heavily on the accuracy and representativeness of the fit models regarding the population to which it is addressed. In this PhD work, a statistical approach is proposed to identify representative fit models. It is based on another clustering method originally developed for grouping gene expression data. This method, called hipamAnthropom, has been published in Decision Support Systems. From well-defined fit models and prototypes, representative and accurate mannequins of the population can be made. Unlike clothing design, where representative cases correspond with central individuals, in the design of working and household environments, the variability of human shape is described by extreme individuals, which are those that have the largest or smallest values (or extreme combinations) in the dimensions involved in the study. This is often referred to as the accommodation problem. A very interesting reference in this area is the book entitled Guidelines for Using Anthropometric Data in Product Design, published by The Human Factors and Ergonomics Society. The idea behind this way of proceeding is that if a product fits extreme observations, it will also fit the others (less extreme). To that end, in this PhD thesis we propose two methodological contributions based on the statistical archetypal analysis. An archetype in Statistics is an extreme individual that is obtained as a convex combination of other subjects of the sample. The first of these methodologies has been published in Computers and Industrial Engineering, whereas the second one has been submitted for publication. The outline of this PhD report is as follows: Chapter 1 reviews the state of the art of Ergonomics and Anthropometry and introduces the anthropometric survey of the Spanish female population. Chapter 2 presents the trimowa, biclustAnthropom and hipamAnthropom methodologies. In Chapter 3 the kmeansProcrustes proposal is detailed. The TDDclust methodology is explained in Chapter 4. Chapter 5 presents the two methodologies related to the archetypal analysis. Since all these contributions have been programmed in the statistical software R, Chapter 6 presents the Anthropometry R package, that brings together all the algorithms associated with each approach. In this way, from Chapter 2 to Chapter 6 all the methodologies and results included in this PhD thesis are presented. At last, Chapter 7 provides the most important conclusions

Genomic integrative analysis to improve fusion transcript detection, liquid association and biclustering

More data provide more possibilities. Growing number of genomic data provide new perspectives to understand some complex biological problems. Many algorithms for single-study have been developed, however, their results are not stable for small sample size or overwhelmed by study-specific signals. Taking the advantage of high throughput genomic data from multiple cohorts, in this dissertation, we are able to detect novel fusion transcripts, explore complex gene regulations and discovery disease subtypes within an integrative analysis framework. In the first project, we evaluated 15 fusion transcript detection tools for paired-end RNA-seq data. Though no single method had distinguished performance over the others, several top tools were selected according to their F-measures. We further developed a fusion meta-caller algorithm by combining top methods to re-prioritize candidate fusion transcripts. The results showed that our meta-caller can successfully balance precision and recall compared to any single fusion detection tool. In the second project, we extended liquid association to two meta-analytic frameworks (MetaLA and MetaMLA). Liquid association is the dynamic gene-gene correlation depending on the expression level of a third gene. Our MetaLA and MetaMLA provided stronger detection signals and more consistent and stable results compared to single-study analysis. When applied our method to five Yeast datasets related to environmental changes, genes in the top triplets were highly enriched in fundamental biological processes corresponding to environmental changes. In the third project, we extended the plaid model from single-study analysis to multiple cohorts for bicluster detection. Our meta-biclustering algorithm can successfully discovery biclusters with higher Jaccard accuracy toward large noise and small sample size. We also introduced the concept of gap statistic for pruning parameter estimation. In addition, biclusters detected from five breast cancer mRNA expression cohorts can successfully select genes highly associated with many breast cancer related pathways and split samples with significantly different survival behaviors. In conclusion, we improved the fusion transcripts detection, liquid association analysis and bicluster discovery through integrative-analysis frameworks. These results provided strong evidence of gene fusion structure variation, three-way gene regulation and disease subtype detection, and thus contribute to better understanding of complex disease mechanism ultimately

A bioinfomatic analysis of the role of mitochondrial biogenesis in human pathologies

Disease states are often associated with radical rearrangements of cellular metabolism; suggesting the transcriptome underlying these changes follows a distinctive pattern. Identification of these patterns is complicated by the hugely heterogeneous nature of these diseases, such as cancer, and the patterns remain hidden within noise of large datasets. A new biclustering algorithm called Massively Correlating Biclustering (MCbiclust) was developed to identify these patterns. Taking a large gene set such as those known to be associated with the mitochondria, samples are selected in which these genes are highly correlated. Rigorous benchmarking of this method with other biclustering methods on synthetic gene expression data and an E. coli data set show it to be superior in finding these patterns. This method was used to identify the role mitochondrial biogenesis plays in cancer; applied on the Cancer Cell Line Encyclopedia (CCLE) it identified differences in mitochondrial function based on the different tissue of origin of the cell line. In patient breast tumour samples a change in mitochondrial function was identified and linked to differences in known breast cancer subtypes. Breast cancer cell lines were identified that matched this pattern. Experimentally testing these cell lines confirming the significant difference in gene expression expected and also showed significant changes in mitochondrial function demonstrated by measurements in oxygen consumption, proteomics and metabolomics. MCbiclust has been developed into an R package. Using this method, new cancer subtypes can be identified, based on fundamental changes to known pathways. The benefit is twofold: first to increase understanding of these complex systems and second to guide treatment using drug compounds known to target these pathways. The methods described here while applied to cancer and mitochondria, are versatile and can be applied to any large dataset of gene expression measurements

Data mining using the crossing minimization paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo