460 research outputs found
A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series
<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p
Profile Likelihood Biclustering
Biclustering, the process of simultaneously clustering the rows and columns
of a data matrix, is a popular and effective tool for finding structure in a
high-dimensional dataset. Many biclustering procedures appear to work well in
practice, but most do not have associated consistency guarantees. To address
this shortcoming, we propose a new biclustering procedure based on profile
likelihood. The procedure applies to a broad range of data modalities,
including binary, count, and continuous observations. We prove that the
procedure recovers the true row and column classes when the dimensions of the
data matrix tend to infinity, even if the functional form of the data
distribution is misspecified. The procedure requires computing a combinatorial
search, which can be expensive in practice. Rather than performing this search
directly, we propose a new heuristic optimization procedure based on the
Kernighan-Lin heuristic, which has nice computational properties and performs
well in simulations. We demonstrate our procedure with applications to
congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at
https://github.com/patperry/biclustp
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Traditional medicine typically applies one-size-fits-all treatment for the
entire patient population whereas precision medicine develops tailored
treatment schemes for different patient subgroups. The fact that some factors
may be more significant for a specific patient subgroup motivates clinicians
and medical researchers to develop new approaches to subgroup detection and
analysis, which is an effective strategy to personalize treatment. In this
study, we propose a novel patient subgroup detection method, called Supervised
Biclustring (SUBIC) using convex optimization and apply our approach to detect
patient subgroups and prioritize risk factors for hypertension (HTN) in a
vulnerable demographic subgroup (African-American). Our approach not only finds
patient subgroups with guidance of a clinically relevant target variable but
also identifies and prioritizes risk factors by pursuing sparsity of the input
variables and encouraging similarity among the input variables and between the
input and target variable
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