Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell

Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics

Recent progress in high-throughput genomic technologies has revolutionized the field of human genetics and promises to lead to important scientific advances. With new improvements in massively parallel biotechnologies, it is becoming increasingly more efficient to generate vast amounts of information at the genomics, transcriptomics, proteomics, metabolomics etc. levels, opening up as yet unexplored opportunities in the search for the genetic causes of complex traits. Despite this tremendous progress in data generation, it remains very challenging to analyze, integrate and interpret these data. The resulting data are high-dimensional and very sparse, and efficient statistical methods are critical in order to extract the rich information contained in these data. The major focus of the mini-workshop, entitled “Recent Developments in Statistical Methods with Applications to Genetics and Genomics”, has been on integrative methods. Relevant research questions included the optimal study design for integrative genomic analyses; appropriate handling and pre-processing of different types of omics data; statistical methods for integration of multiple types of omics data; adjustment for confounding due to latent factors such as cell or tissue heterogeneity; the optimal use of omics data to enhance or make sense of results identified through genetic studies; and statistical and computational strategies for analysis of multiple types of high-dimensional data

Inference of Gaussian graphical models and ordinary differential equations

Netwerken vormen een handig instrument bij het visualiseren van systemen bestaand uit elementen die onderling interactie aangaan. Genregulatienetwerken, bijvoorbeeld, zijn complexe systemen die bestaan uit genen, eiwitten en andere moleculen. De elementen van een dergelijk systeem worden weergegeven door knooppunten, die door lijnen worden verbonden op het moment dat de bijbehorende elementen met elkaar in interactie zijn.In veel wetenschappelijke disciplines vormt het blootleggen van de structuur van een netwerk een belangrijk en ingewikkeld probleem. Vaak is er weinig bekend over een systeem en moet men uitgaan van meetgegevens uit knooppunten om een inschatting te kunnen maken van de structuur van het bijbehorende netwerk. Deze meetgegevens zijn echter aan ruis onderhevig. Wanneer de structuur van de interacties bekend is, staan we voor een andere uitdaging: of het nou gaat om het beschrijving van bruggen die een hevige wind moeten weerstaan of om de verspreiding van een infectieziekte, de vraag is hoe we op basis van dezelfde aan ruis onderhevige data kunnen bepalen hoe de fijne dynamica van het systeem in elkaar zit.In dit proefschrift stellen we enkele aanpassingen voor op bestaande methodes, om het schatten van de structuur van en interacties binnen netwerken en dynamische systemen te verbeteren.Enkele toepassingen van de methodes die we ontwikkelen zijn: het voorspellen van het aantal individuen dat tijdens de kindertijd mazelen krijgt, en inferentie van de interactie tussen genen en eiwitten in de E. colibacterie.Networks provide a simple way to visualize a system of interacting elements. For example, gene regulatory networks are complex systems whose elements are genes, proteins and other molecules. The elements of this system are represented by nodes and lines are drawn between them if they interact with each other. In many sciences uncovering the network structure is an important and difficult problem. With a limited knowledge about the system noisy measurements on the nodes should be used to estimate the network. When we know the structure of the interactions, another major obstacle is to learn the fine dynamics of the system using the same noisy data, from describing bridges subject to strong winds to the spread of an infectious disease.In this thesis we propose modifications of existing methods to improve the estimation of networks and dynamical systems.Some applications of methods we develop include: predicting the number of individuals that get infected by childhood disease measles, reconstructing transcription factor activities in streptomyces coelicolor bacterium, and inferring the interaction between genes and proteins in Escherichia coli bacterium

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen