Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

Synthetic recording and in situ readout of lineage information in single cells

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here, we describe a new synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out in single cells in situ. This system, termed Memory by Engineered Mutagenesis with Optical In situ Readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by Cas9-based targeted mutagenesis, and read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). To demonstrate a proof of principle of MEMOIR, we engineered mouse embryonic stem (ES) cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of the lineage trees of cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which ES cells switch between two gene expression states. Finally, using simulations, we showed how parallel MEMOIR systems operating in the same cell can enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single cell readout across diverse biological systems

Overlap Removal of Dimensionality Reduction Scatterplot Layouts

Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional data items with presence in different areas. Despite its popularity, scatterplots suffer from occlusion, especially when markers convey information, making it troublesome for users to estimate items' groups' sizes and, more importantly, potentially obfuscating critical items for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts, lacking the powerful capabilities of contemporary DR techniques in uncover interesting data patterns, or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, the best methods typically expand or distort the scatterplot area, thus reducing markers' size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This paper presents a novel post-processing strategy to remove DR layouts' overlaps that faithfully preserves the original layout's characteristics and markers' sizes. We show that the proposed strategy surpasses the state-of-the-art in overlap removal through an extensive comparative evaluation considering multiple different metrics while it is 2 or 3 orders of magnitude faster for large datasets.Comment: 11 pages and 9 figure

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.

MOTIVATION: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets. RESULTS: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. AVAILABILITY AND IMPLEMENTATION: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by the Turkish State Planning Organization [DPT09K120520 to B.K.]; the Bogazici University Research Fund [10A05D4 to B.K., 08A506 to B.K., 6882-12A01D5 to A.T.C.]; TUBITAK [106M444 to B.K., 110E292 to A.T.C.], Biotechnology and Biological Sciences Research Council [BRIC2.2 grant BB/K011138/1 to S.G.O.]; and EU 7th Framework Programme [BIOLEDGE Contract No: 289126 to S.G.O.].This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btv53

jMOSAiCS: joint analysis of multiple ChIP-seq datasets

The ChIP-seq technique enables genome-wide mapping of in vivo protein-DNA interactions and chromatin states. Current analytical approaches for ChIP-seq analysis are largely geared towards single-sample investigations, and have limited applicability in comparative settings that aim to identify combinatorial patterns of enrichment across multiple datasets. We describe a novel probabilistic method, jMOSAiCS, for jointly analyzing multiple ChIP-seq datasets. We demonstrate its usefulness with a wide range of data-driven computational experiments and with a case study of histone modifications on GATA1-occupied segments during erythroid differentiation. jMOSAiCS is open source software and can be downloaded from Bioconductor [1]

Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images

Automatic segmentation and recognition of semantic classes in natural images is an important open problem in computer vision. In this work, we investigate three different approaches to recognition: without supervision, with supervision on level of images, and with supervision on the level of pixels. The thesis comprises three parts. The first part introduces a clustering algorithm that optimizes a novel information-theoretic objective function. We show that the proposed algorithm has clear advantages over standard algorithms from the literature on a wide array of datasets. Clustering algorithms are an important building block for higher-level computer vision applications, in particular for semantic segmentation. The second part of this work proposes an algorithm for automatic segmentation and recognition of object classes in natural images, that learns a segmentation model solely from annotation in the form of presence and absence of object classes in images. The third and main part of this work investigates one of the most popular approaches to the task of object class segmentation and semantic segmentation, based on conditional random fields and structured prediction. We investigate several learning algorithms, in particular in combination with approximate inference procedures. We show how structured models for image segmentation can be learned exactly in practical settings, even in the presence of many loops in the underlying neighborhood graphs. The introduced methods provide results advancing the state-of-the-art on two complex benchmark datasets for semantic segmentation, the MSRC-21 Dataset of RGB images and the NYU V2 Dataset or RGB-D images of indoor scenes. Finally, we introduce a software library that al- lows us to perform extensive empirical comparisons of state-of-the-art structured learning approaches. This allows us to characterize their practical properties in a range of applications, in particular for semantic segmentation and object class segmentation.Methoden zum Lernen von Strukturierter Vorhersage in Semantischer Segmentierung von Natürlichen Bildern Automatische Segmentierung und Erkennung von semantischen Klassen in natür- lichen Bildern ist ein wichtiges offenes Problem des maschinellen Sehens. In dieser Arbeit untersuchen wir drei möglichen Ansätze der Erkennung: ohne Überwachung, mit Überwachung auf Ebene von Bildern und mit Überwachung auf Ebene von Pixeln. Diese Arbeit setzt sich aus drei Teilen zusammen. Im ersten Teil der Arbeit schlagen wir einen Clustering-Algorithmus vor, der eine neuartige, informationstheoretische Zielfunktion optimiert. Wir zeigen, dass der vorgestellte Algorithmus üblichen Standardverfahren aus der Literatur gegenüber klare Vorteile auf vielen verschiedenen Datensätzen hat. Clustering ist ein wichtiger Baustein in vielen Applikationen des machinellen Sehens, insbesondere in der automatischen Segmentierung. Der zweite Teil dieser Arbeit stellt ein Verfahren zur automatischen Segmentierung und Erkennung von Objektklassen in natürlichen Bildern vor, das mit Hilfe von Supervision in Form von Klassen-Vorkommen auf Bildern in der Lage ist ein Segmentierungsmodell zu lernen. Der dritte Teil der Arbeit untersucht einen der am weitesten verbreiteten Ansätze zur semantischen Segmentierung und Objektklassensegmentierung, Conditional Random Fields, verbunden mit Verfahren der strukturierten Vorhersage. Wir untersuchen verschiedene Lernalgorithmen des strukturierten Lernens, insbesondere im Zusammenhang mit approximativer Vorhersage. Wir zeigen, dass es möglich ist trotz des Vorhandenseins von Kreisen in den betrachteten Nachbarschaftsgraphen exakte strukturierte Modelle zur Bildsegmentierung zu lernen. Mit den vorgestellten Methoden bringen wir den Stand der Kunst auf zwei komplexen Datensätzen zur semantischen Segmentierung voran, dem MSRC-21 Datensatz von RGB-Bildern und dem NYU V2 Datensatz von RGB-D Bildern von Innenraum-Szenen. Wir stellen außerdem eine Software-Bibliothek vor, die es erlaubt einen weitreichenden Vergleich der besten Lernverfahren für strukturiertes Lernen durchzuführen. Unsere Studie erlaubt uns eine Charakterisierung der betrachteten Algorithmen in einer Reihe von Anwendungen, insbesondere der semantischen Segmentierung und Objektklassensegmentierung