Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

Detecting multivariate differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.</p> <p>Results</p> <p>We have developed a novel algorithm for detecting multivariate expression patterns, named Recursive Independence Test (RIT). This algorithm generalizes differential expression testing to more complex expression patterns, while still including genes found by the univariate approach. We prove that RIT is consistent and controls error rates for small sample sizes. Simulation studies confirm that RIT offers more power than univariate differential expression analysis when multivariate effects are present. We apply RIT to gene expression data sets from diabetes and cancer studies, revealing several putative disease genes that were not detected by univariate differential expression analysis.</p> <p>Conclusion</p> <p>The proposed RIT algorithm increases the power of gene expression analysis by considering multivariate effects while retaining error rate control, and may be useful when conventional differential expression tests yield few findings.</p

M-BISON: Microarray-based integration of data sources using networks

BACKGROUND: The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes. RESULTS: M-BISON improves signal detection on a range of simulated data, particularly when using very noisy microarray data. We also applied the method to the task of predicting heat shock-related differentially expressed genes in S. cerevisiae, using an hsf1 mutant microarray dataset and conserved yeast DNA sequence motifs. Our results demonstrate that M-BISON improves the analysis quality and makes predictions that are easy to interpret in concert with incorporated knowledge. Specifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a method using only microarray data, and M-BISON outperforms a related method, GeneRank. Furthermore, by analyzing M-BISON predictions in the context of the background knowledge, we identified YHR124W as a potentially novel player in the yeast heat shock response. CONCLUSION: This work provides a solid foundation for the principled integration of imperfect biological knowledge with gene expression data and other high-throughput data sources

Searching for differentially expressed gene combinations

We propose 'CorScor', a novel approach for identifying gene pairs with joint differential expression. This is defined as a situation with good phenotype discrimination in the bivariate, but not in the two marginal distributions. CorScor can be used to detect phenotype-related dependencies and interactions among genes. Our easily interpretable approach is scalable to current microarray dimensions and yields promising results on several cancer-gene-expression datasets

Testing for treatment effects on gene ontology

In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, ) because this facilitates the interpretation of effects and may markedly increase significance. A modified meta-analysis method to combine p-values was developed to measure the significance of an overall treatment effect on such functionally-defined groups of genes, taking into account the correlation structure among genes. For hypothesis testing that allows gene expression to change in both directions, p-values are calculated under the null distribution generated by a Monte Carlo method

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Personalized identification of altered pathway using accumulated data

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2014. 8. 박태성.유전자 네트웍의 이상을 탐지하는 것은 질병의 기작을 이해하고 나아가 개인의 유전자 결함에 맞춤 치료를 선정하는 일에 중요하다. 현재 존재하는 유전자 조절/생체 대사 경로 분석 알고리즘은 대부분 정상과 대조군 집단에서의 차이를 판별하는 데에 초점이 맞추어져 있다. 이러한 방법은 한 개인에 초점을 맞추어 분석을 하는 용도로는 적합하지 못하다. 한 개인의 유전자 네트웍의 이상을 분석함에 있어 가장 이상적인 방법은 같은 환자의 정상 조직과 질병 조직의 정보를 비교하는 것이다. 하지만, 임상적인 이유에서 환자의 정상 조직의 정보는 항상 가용한 것은 아니다. 정상 조직을 채취 하는 것은 임상적인 위험이 따르며, 특별하고 명확한 이유가 없는 한 권장되지 않는다. 따라서 질병 시료의 개인 맞춤 분석에 있어서, 같은 사람의 정상 조직 정보는 가용하지 않은 경우가 많다. 본 논문에서는 개인 분석이라는 측면과 해당 환자의 정상 조직 정보가 가용하지 않을 때 유전자 네트웍을 분석하는 것에 초점을 맞추었다. 본 논문의 방법의 철학은 한 사람의 암 환자 유전자 정보를 많은 수의 집적된 정상 조직의 유전자 정보와 비교하여 이상 유무를 판단하는 것에 있다. 본 논문은 Over-Representation Analysis (ORA), Functional Class Score (FCS) 등의 기존에 알려진 그룹 대 그룹에서의 유전자 네트웍 분석법의 개인향 분석법을 제공한다. 이 방법을 사용하여 본 논문에서는 개인의 유전자 네트웍 이상 점수 (individualized pathway aberrance score : iPAS)를 제시 한다. 본 논문의 방법을 두가지 종류의 암종 (폐 선암종, 대장암) 유전자 발현 데이터에 적용하여 유용성을 보였다. 페 정상 조직과 대장 점막 정상 조직의 유전자 발현 데이터를 참조 표준으로 삼고, 각 암 환자 한 사람씩의 유전자 네트웍의 이상을 분석 하였다. 본 논문의 방법은 기존의 연구에서 밝혀진 환자 생존률과 관련된 유전자 네트웍 이상을 정확히 탐지 하였다. 본 논문의 방법은 기존에 방법이라고 할 수 있는, 환자 한명의 정보를 해당 환자가 속한 코호트의 정보를 참조 표준으로 사용하여 해석하는 것 보다, 더 높은 재현성을 보였다. 재현성 측정은 서로 다른 데이터군을 사용하여, 유전자 네트웍 발굴군에서 발굴한 생존 관련 유전자 네트웍이, 발굴에 사용되지 않았던 데이터군에서도 생존에 유의한 영향을 미치는지 측정하였다. 또한 해당 방법은 유전자 네트웍의 특징을 기반으로 환자와 정상을 구분할 수 있다. 특별히 amino acid synthesis and interconversion pathway의 경우 폐 선암을 독립적인 검증을 위한 데이터군에서도 AUC 0.982로 잘 구분할 수 있다. 또한 본 논문에서 제시한 방법은 돌연변이가 유전자 발현 네트웍에 미치는 영향을 정량화 할 수 있는 방법으로 사용될 수 있다. 본 방법을 사용하였을 때 유방암의 유전자 발현 네트웍에 통계적으로 유의한 영향을 미치는 PI3KCA, TP53, RB1 의 세 유전자를 찾을 수 있었고, 이는 알려진 유방암의 지식과 일치한다. 본 논문의 임상적인 의의는 환자 한 사람에서 정상 조직 정보가 없을 때, 한 사람의 암을 유전자 네트웍 측면에서 해석 할 수 있도록 한 것이다. 이러한 방법은 데이터에 기반한 것으로서, 축적되고 있는 정상 조직 데이터를 사용하여, 더욱 정확한 데이터 기반 의사 결정을 하는 데에 기여할 수 있다. 본 논문의 방법은 유전자 발현 뿐 아니라 돌연 변이 분석과도 연계되어, 환자의 암을 유발하는 유전자 네트웍을 발굴하고, 맞춤 치료제를 선정하는 일에 기여할 수 있다.Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individuals pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual is often unavailable in clinical situation. We therefore suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patients matched normal data is unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it to accumulated normal samples. We propose and examine personalized extensions of pathway statistics, Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), to generate individualized pathway aberrance score (iPAS). Collected microarray data of normal tissue of lung and colon mucosa is served as reference to investigate a number of cancer individuals of lung adenocarcinoma and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified amino acid synthesis and interconversion pathway is a good indicator of lung adenocarcinoma (AUC 0.982 at independent validation). We also suggest a new approach for discovering rare mutations that have functional impact in the context of pathway by iteratively combining rare mutations until no more mutations with pathway impact can be added. The approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data. Clinical importance of the method is providing pathway interpretation of single cancer even though its matched normal data is unavailable.Abstract 1 List of Figures 5 List of Tables 6 1. Introduction 7 1.1 Existing pathway analysis approaches (Group to group) 7 1.1.1 Importance of pathway analysis 8 1.1.2 Component of pathway analysis 9 1.1.3 Classification of existing pathway analysis approaches 17 1.2 Personalized pathway analysis 32 1.3 Purpose and novelty of this study 36 1.4 Outline of thesis 37 2. Methods and materials 39 2.1 Gene expression data 39Docto

Automated weighted outlier detection technique for multivariate data

In the chemical and petrochemical industries, spectroscopy-based online analysers are becoming common for process monitoring and control applications. A significant challenge in using these analysers as part of process monitoring and control loops is the large amount of personnel time required for calibration and maintenance of models which involve decision inputs such as whether an observation is an outlier, the number of latent variables in a model, type of pre-processing and when a calibration model has to be updated. Since no one measure works well for all applications, supervision by the process data analyst is required which invariably involves some level of subjectivity. In this paper, we focus on the detection of multivariate outliers in a calibration set. We propose a method which combines multiple outlier detection techniques to identify a set of outlying observations without operator input. Apart from the overall methodology, this work introduces several novelties. The system uses partial least squares (PLS) instead of principal component analysis (PCA) which is normally used for detecting multivariate outliers. A simple modification to the Mahalanobis distance was also proposed which appears to be more sensitive to outliers than the conventional Mahalanobis distance. The methodology also introduces the concept of a desirability function to enable automatic decision making based on multiple statistical measures for outlier detection. The methodology is demonstrated using Raman spectroscopy data collected from an industrial distillation process

Nutritional Systems Biology of Fat : integration and modeling of transcriptomics datasets related to lipid homeostasis

Fatty acids, in the form of triglycerides, are the main constituent of the class of dietary lipids. They not only serve as a source of energy but can also act as potent regulators of gene transcription. It is well accepted that an energy rich diet characterized by high intakes of dietary fat is linked to the dramatic increase in the prevalence of obesity in both developed and developing countries in the last several decades. Obese individuals are at increased risk of developing the metabolic syndrome, a cluster of metabolic abnormalities that ultimately increase the risk of developing vascular diseases and type 2 diabetes. Many studies have been performed to uncover the role of fatty acids on gene expression in different organs, but integrative studies in different organs over time driven by high throughput data are lacking. Therefore, we first aimed to develop integrative approaches on the level of individual genes but also pathways using genome-wide transcriptomics datasets of mouse liver and small intestine that are related to fatty acid sensing transcription factor peroxisome proliferator activated receptor alpha (PPARα). We also aimed to uncover the behavior of PPARαtarget genes and their corresponding biological functions in a short time series experiment, and integrated and modeled the influence of different levels of dietary fat and the time dependency on transcriptomics datasets obtained from several organs by developing system level approaches. We developed an integrative statistical approach that properly adjusted for multiple testing while integrating data from two experiments, and was driven by biological inference. By quantifying pathway activities in different mouse tissues over time and subsequent integration by partial least squares path model, we found that the induced pathways at early time points are the main drivers for the induced pathways at late time points. In addition, using a time course microarray study of rat hepatocytes, we found that most of the PPARα target genes at early stage are involved in lipid metabolism-related processes and their expression level could be modeled using a quadratic regression function. In this study, we also found that the transcription factorsNR2F, CREB, EREF and RXR might work together with PPARα in the regulation of genes involved in lipid metabolism. By integrating time and dose dependent gene expression data of mouse liver and white adipose tissue (WAT), we found a set of time-dose dependent genes in liver and WAT including potential signaling proteinssecreted from WAT that may induce metabolic changes in liver, thereby contributing to the pathogenesis of obesity. Taken together, in this thesis integrative statistical approaches are presented that were applied to a variety of datasets related to metabolism of fatty acids. Results that were obtained provide a better understanding of the function of the fatty acid-sensor PPARa, and identified a set of secreted proteins that may be important for organ cross talk during the development of diet induced obesity. </p

Metabolic impact of vitamin D on U937 cells during TLR2/1 activation with Pam3CysSerLys4, a bacterial lipoprotein mimic

Abstract: Background: Bacterial infections remain one of the top causes of death worldwide despite the continuous development of conventional methods to eradicate this challenge. Mycobacterium tuberculosis (M.tb), a causative agent of tuberculosis (TB), is amongst the leading causes of mycobacterial mortality worldwide. Given the constant increase in TB incidence rates, there is a great need for better diagnostic and treatment methods for M.tb. Several studies have proposed the possible therapeutic role of vitamin D in antimycobacterial immunity. Vitamin D has been shown to boost the immune system against several ailments including TB, however, the exact mechanism through which vitamin D functions in antimycobacterial immunity remains elusive. In addition, the current conventional methods used to study the metabolism of vitamin D in the presence of mycobacteria are limited in terms of efficiency. As such, applying metabolomics to elucidate bacterial activity and vitamin D supplementation effects, at cellular level, could provide insight into the metabolic reprogramming associated with vitamin D during mycobacterial infection. Metabolomics is a multidisciplinary ‘omics’ science that deals with the identification and quantification of the metabolic changes in a biological system under specific conditions...M.Sc. (Biochemistry