Les technologies expérimentales à haut débit produisent de grandes quantités de données sur les niveaux d'expression des gènes dans les bactéries à l'état d'équilibre ou lors des transitions de croissance.Un défi important dans l'interprétation biologique de ces données consiste à en déduire la topologie du réseau de régulation ainsi que les fonctions de régulation quantitatives des gènes.Un grand nombre de méthodes d'inférence a été proposé dans la littérature. Ces méthodes ont été utilisées avec succès dans une variété d'applications, bien que plusieurs problèmes persistent.Nous nous intéressons ici à l'amélioration de deux aspects des méthodes d'inférence.Premièrement, les données transcriptomiques reflètent l'abondance de l'ARNm, tandis que, le plus souvent, les composants régulateurs sont les protéines codées par les ARNm.Bien que les concentrations de l'ARNm et de protéines soient raisonnablement corrélées à l'état stationnaire, cette corrélation devient beaucoup moins évidente dans les données temporelles acquises lors des transitions de croissance à cause des demi-vies très différentes des protéines et des ARNm.Deuxièmement, la dynamique de l'expression génique n'est pas uniquement contrôlée par des facteurs de transcription et d'autres régulateurs spécifiques, mais aussi par des effets physiologiques globaux qui modifient l'activité de tous les gènes. Par exemple, les concentrations de l'ARN polymérase (libre) et les concentrations des ribosomes (libres) varient fortement avec le taux de croissance. Nous devons donc tenir compte de ces effets lors de la reconstruction d'un réseau de régulation à partir de données d'expression génique.Nous proposons ici une approche expérimentale et computationnelle combinée pour répondre à ces deux problèmes fondamentaux dans l'inférence de modèles quantitatifs de promoteurs bactériens à partir des données temporelles d'expression génique.Nous nous intéressons au cas où la dynamique de l'expression génique est mesurée in vivo et en temps réel par l'intermédiaire de gènes rapporteurs fluorescents. Notre approche d'inférence de réseaux de régulation tient compte des différences de demi-vie entre l'ARNm et les protéines et prend en compte les effets physiologiques globaux.Lorsque les demi-vies des protéines sont connues, les modèles expérimentaux utilisés pour dériver les activités des gènes à partir de données de fluorescence sont intégrés pour estimer les concentrations des protéines.L'état physiologique global de la cellule est estimé à partir de l'activité d'un promoteur de phage, dont l'expression n'est contrôlée par aucun des facteurs de transcription et ne dépend que de l'activité de la machinerie d'expression génique.Nous appliquons l'approche à un module central dans le réseau de régulation contrôlant la motilité et le système de chimiotactisme chez Escherichia coli.Ce module est composé des gènes FliA, FlgM et tar.FliA est un facteur sigma qui dirige l'ARN polymérase vers les opérons codant pour des composants de l'assemblage des flagelles.Le troisième composant du réseau, tar, code pour la protéine récepteur chimiotactique de l'aspartate, Tar, et est directement transcrit par FliA associé à l' holoenzyme ARN polymérase. Le module FliA-FlgM est particulièrement bien adapté pour l'étude des problèmes d'inférence considérés ici, puisque le réseau a été bien étudié et les démivies des protéines jouent un rôle important dans son fonctionnement.Nos résultats montrent que, pour la reconstruction fiable de réseaux de régulation transcriptionelle chez les bactéries, il est nécessaire d'inclure les effets globaux dans le modèle de réseau et d'en déduire de manière explicite les concentrations des protéines à partir des profils d'expression observés, car la demi-vie de l'ARNm et des protéines sont très différentes. Notre approche reste généralement applicable à une grande variété de problèmes d'inférence de réseaux et nous discutons les limites et les extensions possibles de la méthode. / High-throughput technologies yield large amounts of data about the steady-state levels and the dynamical changes of gene expression in bacteria. An important challenge for the biological interpretation of these data consists in deducing the topology of the underlying regulatory network as well as quantitative gene regulation functions from such data. A large number of inference methods have been proposed in the literature and have been successful in a variety of applications, although several problems remain. We focus here on improving two aspects of the inference methods. First, transcriptome data reflect the abundance of mRNA, whereas the components that regulate are most often the proteins coded by the mRNAs. Although the concentrations of mRNA and protein correlate reasonably during steady-state growth, this correlation becomes much more tenuous in time-series data acquired during growth transitions in bacteria because of the very different half-lives of proteins and mRNA. Second, the dynamics of gene expression is not only controlled by transcription factors and other specific regulators, but also by global physiological effects that modify the activity of all genes. For example, the concentrations of (free) RNA polymerase and the concentration of ribosomes vary strongly with growth rate. We therefore have to take into account such effects when trying to reconstruct a regulatory network from gene expression data. We propose here a combined experimental and computational approach to address these two fundamental problems in the inference of quantitative models of the activity of bacterial promoters from time-series gene expression data. We focus on the case where the dynamics of gene expression is measured in vivo and in real time by means of fluorescent reporter genes. Our network reconstruction approach accounts for the differences between mRNA and protein half-lives and takes into account global physiological effects. When the half-lives of the proteins are available, the measurement models used for deriving the activities of genes from fluorescence data are integrated to yield estimates of protein concentrations. The global physiological state of the cell is estimated from the activity of a phage promoter, whose expression is not controlled by any transcription factor and depends only on the activity of the transcriptional and translational machinery. We apply the approach to a central module in the regulatory network controlling motility and the chemotaxis system in Escherichia coli. This module comprises the FliA, FlgM and tar genes. FliA is a sigma factor that directs RNA polymerase to operons coding for components of the flagellar assembly. The effect of FliA is counteracted by the antisigma factor FlgM, itself transcribed by FliA. The third component of the network, tar, codes for the aspartate chemoreceptor protein Tar and is directly transcribed by the FliA-containing RNA polymerase holoenzyme. The FliA-FlgM module is particularly well-suited for studying the inference problems considered here, since the network has been well-studied and protein half-lives play an important role in its functioning. We stimulated the FliA-FlgM module in a variety of wild-type and mutant strains and different growth media. The measured transcriptional response of the genes was used to systematically test the information required for the reliable inference of the regulatory interactions and quantitative predictive models of gene regulation. Our results show that for the reliable reconstruction of transcriptional regulatory networks in bacteria it is necessary to include global effects into the network model and explicitly deduce protein concentrations from the observed expression profiles. Our approach should be generally applicable to a large variety of network inference problems and we discuss limitations and possible extensions of the method.

Indiana University-Purdue University Indianapolis (IUPUI) / In systems biology study, biological networks were used to gain insights into biological systems. While the traditional approach to studying biological networks is based on the identification of interactions among genes or the identification of a gene set ranking according to differentially expressed gene lists, little is known about interactions between higher order biological systems, a network of gene sets. Several types of gene set network have been proposed including co-membership, linkage, and co-enrichment human gene set networks. However, to our knowledge, none of them contains directionality information. Therefore, in this study we proposed a method to construct a regulatory gene set network, a directed network, which reveals novel relationships among gene sets. A regulatory gene set network was constructed by using publicly available gene regulation data. A directed edge in regulatory gene set networks represents a regulatory relationship from one gene set to the other gene set. A regulatory gene set network was compared with another type of gene set network to show that the regulatory network provides additional information. In order to show that a regulatory gene set network is useful for understand the underlying mechanism of a disease, an Alzheimer's disease (AD) regulatory gene set network was constructed. In addition, we developed Pathway and Annotated Gene-set Electronic Repository (PAGER), an online systems biology tool for constructing and visualizing gene and gene set networks from multiple gene set collections. PAGER is available at http://discern.uits.iu.edu:8340/PAGER/. Global regulatory and global co-membership gene set networks were pre-computed. PAGER contains 166,489 gene sets, 92,108,741 co-membership edges, 697,221,810 regulatory edges, 44,188 genes, 651,586 unique gene regulations, and 650,160 unique gene interactions. PAGER provided several unique features including constructing regulatory gene set networks, generating expanded gene set networks, and constructing gene networks within a gene set. However, tissue specific or disease specific information was not considered in the disease specific network constructing process, so it might not have high accuracy of presenting the high level relationship among gene sets in the disease context. Therefore, our framework can be improved by collecting higher resolution data, such as tissue specific and disease specific gene regulations and gene sets. In addition, experimental gene expression data can be applied to add more information to the gene set network. For the current version of PAGER, the size of gene and gene set networks are limited to 100 nodes due to browser memory constraint. Our future plans is integrating internal gene or proteins interactions inside pathways in order to support future systems biology study.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / Recent work has shown that bidirectional genes (genes located on opposite strands of DNA, whose transcription start sites are not more than 1000 basepairs apart) are often co-expressed and have similar biological functions. Identification of such genes can be useful in the process of constructing gene regulatory networks. Furthermore, analysis of the intergenic regions corresponding to bidirectional genes can help to identify regulatory elements, such as transcription factor binding sites. Approximately 2500 bidirectional gene pairs have been identified in Arabidopsis thaliana and the corresponding intergenic regions have been shown to be rich in regulatory elements that are essential for the initiation of transcription. Identifying such elements is especially important, as simply searching for known transcription factor binding sites in the promoter of a gene can result in many hits that are not always important for transcription initiation. Encouraged by the findings about the presence of essential regulatory elements in the intergenic regions corresponding to bidirectional genes, in this thesis, we explore a motif-based machine learning approach to identify intergenic regulatory elements. More precisely, we consider the problem of predicting the transcription pattern for pairs of consecutive genes in Arabidopsis thaliana using motifs from AthaMap and PLACE. We use machine learning algorithms to learn models that can predict the direction of transcription for pairs of consecutive genes. To identify the most predictive motifs and, therefore, the most significant regulatory elements, we perform feature selection based on mutual information and feature abstraction based on family or sequence similarity. Preliminary results demonstrate the feasibility of our approach.

Network growing mechanisms are used to construct random networks that have structural behaviors similar to existing networks such as genetic networks, in efforts of understanding the evolution of complex topologies. Popular mechanisms, such as preferential attachment, are capable of preserving network features such as the degree distribution. However, little is known about such randomly grown structures regarding robustness to disturbances (e.g., edge deletions). Moreover, preferential attachment does not target optimizing the network's functionality, such as information flow. Here, we consider a network to be optimal if it's natural functionality is relatively high in addition to possessing some degree of robustness to disturbances. Specifically, a robust network would continue to (1) transmit information, (2) preserve it's connectivity and (3) preserve internal clusters post failures. In efforts to pinpoint features that would possibly replace or collaborate with the degree of a node as criteria for preferential attachment, we present a case study on both; undirected and directed networks. For undirected networks, we make a case study on wireless sensor networks in which we outline a strategy using Support Vector Regression. For Directed networks, we formulate an Integer Linear Program to gauge the exact transcriptional regulatory network optimal structures, from there on we can identify variations in structural features post optimization.

A representação do Sistema de Regulação Gênica por meio de uma Rede de Regulação Gênica (GRN) pode facilitar a compreensão dos processos biológicos no nível molecular, auxiliando no entendimento do comportamento dos genes, a descoberta da causa de doenças e o desenvolvimento de novas drogas. Através das GRNs pode-se avaliar quais genes estão ativos e quais são suas influências no sistema. Nos últimos anos, vários métodos computacionais foram desenvolvidos para realizar a inferência de redes a partir de dados de expressão gênica. Esta pesquisa apresenta uma análise comparativa de métodos de inferência de GRNs, realizando uma revisão do modelo experimental descrito na literatura atual aplicados a conjuntos de dados contendo poucas amostras. Apresenta também o uso comitês de especialistas (ensemble) para agregar o resultado dos métodos a fim de melhorar a qualidade da inferência. Como resultado obteve-se que o uso de poucas amostras de dados (abaixo de 50) não fornecem resultados interessantes para a inferência de redes. Demonstrou-se também que o uso de comitês de especialistas melhoram os resultados de inferência. Os resultados desta pesquisa podem auxiliar em pesquisas futuras baseadas em GRNs. / The representation of the gene regulation system by means of a Gene Regulatory Network (GRN) can help the understanding of biological processes at the molecular level, elucidating the behavior of genes and leading to the discovery of disease causes and the development of new drugs. GRNs allow to evaluate which genes are active and how they influence the system. In recent years, many computational methods have been developed for networks inference from gene expression data. This study presents a comparative analysis of GRN inference methods, reviewing the experimental modeling present in the state-of-art scientific publications applied to datasets with small data samples. The use of ensembles was proposed to improve the quality of the network inference. As results, we show that the use of small data samples (less than 50 samples) do not show a good result in the network inference problem. We also show that the use of ensemble improve the network inference.

Na busca por soluções que maximizem a produção de etanol, o melhoramento genético de diferentes linhagens de levedura tornou-se foco de investigação em diversos centros de pesquisa. Com o recente sequenciamento de uma linhagem selvagem utilizada nas usinas sucroalcooleiras brasileiras, a linhagem PE-2 da espécie Saccharomyces cerevisiae, surgiu o interesse em estudar sua dinâmica durante o processo de fermentação a fim de encontrar aspectos que possam explicar como estas se tornaram mais adaptadas às dornas de fermentação mantendo a alta produtividade de bioetanol. A partir da análise transcricional da linhagem PE-2, Buscamos por métodos de inferência de redes que possam representar a dinâmica dessa levedura. Propomos nesse trabalho a modelagem de dados experimentais temporais das linhagens PE-2 e S288c (utilizada como referência) baseado em um modelo de Redes Booleanas. Trata-se de um modelo onde convertemos dados contínuos em dados discretos (0 or 1) no qual, de acordo com restrições ditadas pelo modelo, são inferidas redes que representem interações gênicas ao longo do tempo baseados nas amostras temporais. Conseguimos modelar, com sucesso, algumas redes utilizando conjuntos com 11 e 12 genes relacionados a genes pertencentes à via da glicólise e fermentação da levedura. / Ethanol production improvements give rise to the breeding of yeast strains, that became the investigation focus in several research centers. Recently, a wild strain used in Brazilian sugarcane industry was sequenced, the PE-2 strain of Saccharomyces cerevisiae, and this event brought an interest in studying the dynamics of the fermentation of this strain in order to understand which aspects this strain become more adapted to the fermentation conditions, maintaining a high capacity to produce bioethanol. From the analysis of transcriptional strain PE-2, we seek for inference networks methods that can represent the dynamics of this yeast.In this work, we model an experimental temporal data of strain PE-2 and strain S288c (used as a reference) based on Boolean networks model. In this model, the data are converted from continuous into discrete data (0 or 1) and, based on constraints rules of Boolean Network model, networks are inferred to represent gene interactions over time based on temporal data. We successfully model networks using a set with 11 and 12 genes related to yeast glycolysis and fermentation pathways.

A análise da expressão gênica através de dados gerados em experimentos de microarrays de DNA vem possibilitando uma melhor compreensão da dinâmica e dos mecanismos envolvidos nos processos celulares ao nível molecular. O aprimoramento desta análise é crucial para o avanço do conhecimento sobre as bases moleculares das neoplasias e para a identificação de marcadores moleculares para uso em diagnóstico, desenho de novos medicamentos em terapias anti-tumorais. Este trabalho tem como objetivos o desenvolvimento de modelos de análise desses dados, propondo uma nova forma de normalização de dados provenientes de microarrays e dois modelos para a construção de redes regulatórias de expressão gênica, sendo uma baseada na conectividade dinâmica entre diversos genes ao longo do ciclo celular e a outra que resolve o problema da dimensionalidade, em que o número de experimentos de microarrays é menor que o número de genes. Apresenta-se, ainda, um pacote de ferramentas com uma interface gráfica de fácil uso contendo diversas técnicas de análise de dados já conhecidas como também as abordagens propostas neste trabalho. / The analyses of DNA microarrays gene expression data are allowing a better comprehension of the dynamics and mechanisms involved in cellular processes at the molecular level. In the cancer field, the improvement of gene expression interpretation is crucial to better understand the molecular basis of the neoplasias and to identify molecular markers to be used in diagnosis and in the design of new anti-tumoral drugs. The main goals of this work were to develop a new method to normalize DNA microarray data and two models to construct gene expression regulatory networks. One method analyses the dynamic connectivity between genes through the cell cycle and the other solves the dimensionality problem in regulatory networks, meaning that the number of experiments is lower than the number of genes. We also developed a toolbox with a user-friendly interface, displaying several established statistical methods implemented to analyze gene expression data as well as the new approaches presented in this work.

As convulsões febris complexas durante a infância representam um fator de risco relevante para o desenvolvimento da epilepsia. Apesar desse fato, as alterações moleculares induzidas por essas crises febris, que tornam o cérebro susceptível ao processo de epileptogênese, ainda são pouco conhecidas. Nesse contexto, a utilização de modelos animais de crises febris induzidas por hipertermia (HS) permite o estudo das alterações moleculares a partir de uma análise temporal desse processo. Assim, neste trabalho foram investigadas as alterações temporais nos perfis de microRNAs e de expressão gênica em explantes da região CA3 hipocampal de ratos Wistar obtidas em quatro intervalos de tempo após o insulto hipertérmico no décimo primeiro dia pós-natal (P11). Os intervalos temporais foram selecionados para avaliar as fases aguda (P12), latente (P30 e P60) e crônica (P120). A análise transcriptômica consistiu na construção de redes de co-expressão gênica, permitindo a identificação de módulos de genes e sua relação com os grupos experimentais e intervalos de tempo selecionados. Os genes também foram caracterizados hierarquicamente, identificando-se genes que conferem robustez às redes de co-expressão gênica (hubs). Além disso, foram avaliados o perfil de expressão diferencial de microRNAs e feita a análise integrada da expressão de microRNAs e expressão gênica dos hubs. Os resultados deste trabalho mostraram que: i) o insulto hipertérmico leva a alterações importantes no desenvolvimento e funcionamento cerebral ii) essas alterações estão associadas a uma assinatura temporal, presumivelmente da epileptogênese à readaptação do cérebro frente ao insulto precipitante inicial; iii) isso envolve um mecanismo de regulação das redes de co-expressão gênica por microRNAs. Esses resultados sugerem que as alterações transcricionais desencadeadas pelo insulto febril podem levar à reprogramação neuronal e ao remodelamento da cromatina, tornando o cérebro susceptível ao processo epiléptico crônico. Como nas epilepsias humanas por insulto febril, o modelo em rato reflete um processo que vai da epileptogênese à cronificação na fase adulta. Como muitos dos casos de epilepsia por insulto febril são refratários a drogas anticonvulsivantes, o entendimento temporal dos mecanismos moleculares envolvidos nesse tipo de epilepsia é relevante para se identificar alvos terapêuticos e desenvolver drogas anti-epileptogênicas / Complex febrile seizures during childhood represent a relevant risk factor for the development of epilepsy. Despite this fact, the molecular alterations induced by febrile seizures that make the brain susceptible to the process of epileptogenesis are still poorly understood. In this context, the animal models of febrile seizures induced by hyperthermia (HS) allow the study of the molecular alterations from a temporal perspective. Thus, we investigated the temporal alterations in the profiles of gene expression and microRNAs in explants of the hippocampal CA3 region of Wistar rats, here obtained at four-time intervals after the hyperthermal insult on the eleventh postnatal day (P11). Time intervals were selected to evaluate the acute (P12), latent (P30 and P60) and chronic (P120) phases. Transcriptomic analysis consisted of constructing gene co-expression networks, allowing the identification of gene modules related to selected time intervals. Genes were also characterized hierarchically identifying those that control the robustness of gene co-expression networks (hubs). In addition, the differential expression profile of microRNA and the integrated analysis of microRNA expression and hub\'s gene expression were evaluated. The results of this work showed that: i) hyperthermic insults lead to important changes in cerebral development and functioning related to febrile seizures; ii) each time interval shows a transcriptomic signature, probably reflecting the process from epileptogenesis to brain readaptation after the initial precipitating insult; iii) this process involves a mechanism of regulation of gene co-expression networks by microRNAs. These results suggest that transcriptional changes triggered by febrile insults may lead to neuronal reprogramming and chromatin remodeling, making the brain susceptible to the chronic epileptic process. Human epilepsy triggered by febrile insults in childhood is related to resistance to antiepileptic drugs and no anti-epileptogenic drug was developed so far. Therefore, a better understanding of the temporal mechanisms involved in the development of chronic epilepsy is mandatory in order to discover new therapeutic targets and, eventually, anti-epileptogenic drugs

As convulsões febris complexas durante a infância representam um fator de risco relevante para o desenvolvimento da epilepsia. Apesar desse fato, as alterações moleculares induzidas por essas crises febris, que tornam o cérebro susceptível ao processo de epileptogênese, ainda são pouco conhecidas. Nesse contexto, a utilização de modelos animais de crises febris induzidas por hipertermia (HS) permite o estudo das alterações moleculares a partir de uma análise temporal desse processo. Assim, neste trabalho foram investigadas as alterações temporais nos perfis de microRNAs e de expressão gênica em explantes da região CA3 hipocampal de ratos Wistar obtidas em quatro intervalos de tempo após o insulto hipertérmico no décimo primeiro dia pós-natal (P11). Os intervalos temporais foram selecionados para avaliar as fases aguda (P12), latente (P30 e P60) e crônica (P120). A análise transcriptômica consistiu na construção de redes de co-expressão gênica, permitindo a identificação de módulos de genes e sua relação com os grupos experimentais e intervalos de tempo selecionados. Os genes também foram caracterizados hierarquicamente, identificando-se genes que conferem robustez às redes de co-expressão gênica (hubs). Além disso, foram avaliados o perfil de expressão diferencial de microRNAs e feita a análise integrada da expressão de microRNAs e expressão gênica dos hubs. Os resultados deste trabalho mostraram que: i) o insulto hipertérmico leva a alterações importantes no desenvolvimento e funcionamento cerebral ii) essas alterações estão associadas a uma assinatura temporal, presumivelmente da epileptogênese à readaptação do cérebro frente ao insulto precipitante inicial; iii) isso envolve um mecanismo de regulação das redes de co-expressão gênica por microRNAs. Esses resultados sugerem que as alterações transcricionais desencadeadas pelo insulto febril podem levar à reprogramação neuronal e ao remodelamento da cromatina, tornando o cérebro susceptível ao processo epiléptico crônico. Como nas epilepsias humanas por insulto febril, o modelo em rato reflete um processo que vai da epileptogênese à cronificação na fase adulta. Como muitos dos casos de epilepsia por insulto febril são refratários a drogas anticonvulsivantes, o entendimento temporal dos mecanismos moleculares envolvidos nesse tipo de epilepsia é relevante para se identificar alvos terapêuticos e desenvolver drogas anti-epileptogênicas / Complex febrile seizures during childhood represent a relevant risk factor for the development of epilepsy. Despite this fact, the molecular alterations induced by febrile seizures that make the brain susceptible to the process of epileptogenesis are still poorly understood. In this context, the animal models of febrile seizures induced by hyperthermia (HS) allow the study of the molecular alterations from a temporal perspective. Thus, we investigated the temporal alterations in the profiles of gene expression and microRNAs in explants of the hippocampal CA3 region of Wistar rats, here obtained at four-time intervals after the hyperthermal insult on the eleventh postnatal day (P11). Time intervals were selected to evaluate the acute (P12), latent (P30 and P60) and chronic (P120) phases. Transcriptomic analysis consisted of constructing gene co-expression networks, allowing the identification of gene modules related to selected time intervals. Genes were also characterized hierarchically identifying those that control the robustness of gene co-expression networks (hubs). In addition, the differential expression profile of microRNA and the integrated analysis of microRNA expression and hub\'s gene expression were evaluated. The results of this work showed that: i) hyperthermic insults lead to important changes in cerebral development and functioning related to febrile seizures; ii) each time interval shows a transcriptomic signature, probably reflecting the process from epileptogenesis to brain readaptation after the initial precipitating insult; iii) this process involves a mechanism of regulation of gene co-expression networks by microRNAs. These results suggest that transcriptional changes triggered by febrile insults may lead to neuronal reprogramming and chromatin remodeling, making the brain susceptible to the chronic epileptic process. Human epilepsy triggered by febrile insults in childhood is related to resistance to antiepileptic drugs and no anti-epileptogenic drug was developed so far. Therefore, a better understanding of the temporal mechanisms involved in the development of chronic epilepsy is mandatory in order to discover new therapeutic targets and, eventually, anti-epileptogenic drugs

The reconstruction of gene regulatory networks is one of the milestones of computational system biology. We introduce a new implementation of ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) to reverse engineer transcriptional regulatory networks with improved mutual information estimators and significant improvement in performance. In the context of data driven network inference we identify two major confounding biases and introduce solutions to remove some of the discussed biases. First we identify prevalent spatial biases in gene expression studies derived from plate based designs. We investigate the gene expression profiles of a million samples from the LINCS dataset and find that the vast majority (96%) of the tested plates is affected by significant spatial bias. We can show that our proposed method to correct these biases results in a significant improvement of similarity between biological replicates assayed in different plates. Lastly we discuss the effect of CNV on gene expression and its confounding effect on the correlation landscape of genes in the context of cancer samples. We propose a method that removes the variance in gene expression explained by CNV and show that TF target predictions can be significantly improved.