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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O4 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O4. LATEBICLUSTERING: EFFICIENT DISCOVERY OF TEMPORAL LOCAL PATTERNS WITH POTENTIAL DELAYS Joana P. Gonçalves 1,2* & Sara C. Madeira 3,4 . Pattern Recognition and Bioinformatics Group, Department of Intelligent Systems, Delft University of Technology 1 ; Division of Molecular Carcinogenesis, The Netherlands Cancer Institute 2 ; Department of Computer Science and Engineering, Instituto Superior Técnico, Universidade de Lisboa 3 ; INESC-ID 4 . * research@joanagoncalves.org Temporal transcriptomes can provide valuable insight into the dynamics of transcriptional response and gene regulation. In particular, many studies seek to uncover functional biological units by identifying and grouping genes with common expression patterns. Nevertheless, most analytical tools available for this purpose fall short in their ability to consider biologically reasonable models and adequately incorporate the temporal dimension. Each biological task is likely to occur within a time period that does not necessarily span the whole time course of the experiment, and genes involved in such a task are expected to coordinate only while the task is ongoing. LateBiclustering is an efficient algorithm to identify this type of coordinated activity, while allowing genes to participate in distinct biological tasks with multiple partners over time. Additionally, LateBiclustering is able to capture temporal delays suggestive of transcriptional cascades: one of the hallmarks of gene expression and regulation. INTRODUCTION The discovery of patterns in temporal transcriptomes exposes gene expression dynamics and contributes to understand the machinery involved in its modulation. Various analytical tools are employed in this regard. Differential expression summarizes an entire time course into one feature, thus lacking detail. Clustering maintains respects the chronological order, but focuses on global similarities and tends to identify rather broad patterns, associated with unspecific functions. Biclustering offers increased granularity by additionally searching for local patterns, but allows for arbitrary jumps in time, eventually leading to patterns that are incoherent from a temporal perspective. METHODS LateBiclustering is an efficient algorithm for the identification of transcriptional modules, here termed LateBiclusters. Each LateBicluster is a group of genes showing a similar expression pattern with potential delays, within a particular time frame that does not necessarily span the whole time course of the transciptome. LateBiclustering only reports maximal LateBiclusters, that is, those that cannot be extended and are not fully contained in any other LateBicluster. LateBiclustering takes as input a gene-time expression matrix of real values. Each gene expression profile is first normalized to zero mean and unit standard deviation. A discretization is further applied to discern variations between consecutive time points into three levels: downtrend, no-change and up-trend. Upon discretization each gene profile can be seen as a string. A generalized suffix tree is built to find common patterns in the gene profiles. Internal nodes satisfying certain properties are marked for their potential to denote LateBiclusters. When an internal node does not satisfy the basic conditions for LateBicluster maximality, a procedure is applied to remove occurrences leading to non-maximal LateBiclusters. For this purpose, LateBiclustering uses a bit array representing the occurrences underlying each internal node. During the maximality update procedure, the bit array of the inspected node is compared against those of internal children nodes (right-max) and nodes from which the inspected node receives suffix links (left-max). Finally, LateBiclustering comes with different heuristics to report a single pattern occurrence per gene in each maximal LateBicluster. A heuristic is necessary because there may be multiple occurrences of a pattern in the profile of a given gene, which is a direct consequence of allowing the discovery of delayed patterns. RESULTS & DISCUSSION LateBiclustering is the first efficient algorithm suitable for the discovery of biclusters with temporal delays. It runs in polynomial time, while previous methods yielded exponential time complexity. LateBiclustering was able to find planted biclusters in synthetic data. It also identified biologically relevant LateBiclusters associated with Saccharomyces cerevisiae’s response to heat stress, and interesting time-lagged responses. FIGURE 1. Schematic of the LateBiclustering algorithm. REFERENCES Gonçalves JP & Madeira SC. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(5), 801–813 (2014). 24
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O5 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O5. INFERRING DEVELOPMENTAL CHRONOLOGIES FROM SINGLE CELL RNA Robrecht Cannoodt 1,2,3* , Katleen De Preter 3 & Yvan Saeys 1,2 . Data Mining and Modelling for Biomedicine group, VIB Inflammation Research Center, Ghent 1 ; Department of Respiratory Medicine, Ghent University Hospital, Ghent 2 ; Center of Medical Genetics, Ghent University Hospital, Ghent 3 . * robrecht.cannoodt@ugent.be With the advent of single cell RNA sequencing, it is now possible to analyse the transcriptomes of hundreds of individual cells in an unbiased manner. Reconstructing the developmental chronology of differentiating cells is a challenging task, and doing so in a unsupervised and robust manner is a hitherto untackled problem. We developed a truly unsupervised developmental chronology inference technique, and evaluated its performance and robustness using multiple datasets. INTRODUCTION Early attempts at inferring the chronologies of single cells are MONOCLE (Trapnell et al., 2014) and NBOR (Schlitzer et al., 2015). However, these techniques are not unsupervised as they require knowledge of the cell type of each cell prior to analysis, which biases the results to prior knowledge and possibly obstructs the discovery of novel subpopulations. METHODS Our approach consists of four steps. In the first step, the feature space (~30000 genes) is reduced to three dimensions. Secondly, outliers are detected and removed, using a K- nearest neighbour approach. After outlier removal, the original feature space is again reduced to three dimensions. Next, a nonparametric nonlinear curve is iteratively fitted to the data. Finally, each cell is projected onto the curve, thus resulting in a cell chronology. RESULTS & DISCUSSION A single-cell RNAseq dataset (Schlitzer et al., 2015) contains profilings of DC progenitor cells. These cells are expected to differentiate from MDP to CDP to PreDC. Our method is able to intuitively visualise known population groups (Figure 1), as well as infer the developmental chronology of the individual cells (Figure 2). We evaluated our method on four datasets (Shalek et al., 2014; Trapnell et al., 2014; Buettner et al., 2015 and Schlitzer et al., 2015), and found it to perform better and more robustly than existing methods MONOCLE and NBOR. This approach opens opportunities to further study known mechanisms or investigate unknown key regulatory structures in cell differentiation, or detect novel subpopulations in a truly unsupervised manner. REFERENCES Buettner F et al. Nature Biotechnology 33, 155-160 (2015). Schlitzer A et al. Nature Immunology 16, 718-726 (2015). Shalek A et al. Nature 509, 363-369 (2014). Trapnell C et al. Nature Biotechnology 32, 381-386 (2014). FIGURE 1. After feature space reduction and outlier detection of 244 DC progenitor cells (Schlitzer et al., 2015), our method can intuitively visualise known populations. FIGURE 2. An iterative curve fitting results in a smooth curve reflecting the developmental chronology. After projecting each cell to the curve, regulatory patterns in expression which correlate with this timeline can be investigated. 25
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