2,111 research outputs found
Clustering gene expression data with a penalized graph-based metric
<p>Abstract</p> <p>Background</p> <p>The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.</p> <p>Results</p> <p>In this work we introduce the Penalized k-Nearest-Neighbor-Graph (PKNNG) based metric, a new tool for evaluating distances in such cases. The new metric can be used in combination with most clustering algorithms. The PKNNG metric is based on a two-step procedure: first it constructs the k-Nearest-Neighbor-Graph of the dataset of interest using a low k-value and then it adds edges with a highly penalized weight for connecting the subgraphs produced by the first step. We discuss several possible schemes for connecting the different sub-graphs as well as penalization functions. We show clustering results on several public gene expression datasets and simulated artificial problems to evaluate the behavior of the new metric.</p> <p>Conclusions</p> <p>In all cases the PKNNG metric shows promising clustering results. The use of the PKNNG metric can improve the performance of commonly used pairwise-distance based clustering methods, to the level of more advanced algorithms. A great advantage of the new procedure is that researchers do not need to learn a new method, they can simply compute distances with the PKNNG metric and then, for example, use hierarchical clustering to produce an accurate and highly interpretable dendrogram of their high-dimensional data.</p
Defining a robust biological prior from Pathway Analysis to drive Network Inference
Inferring genetic networks from gene expression data is one of the most
challenging work in the post-genomic era, partly due to the vast space of
possible networks and the relatively small amount of data available. In this
field, Gaussian Graphical Model (GGM) provides a convenient framework for the
discovery of biological networks. In this paper, we propose an original
approach for inferring gene regulation networks using a robust biological prior
on their structure in order to limit the set of candidate networks.
Pathways, that represent biological knowledge on the regulatory networks,
will be used as an informative prior knowledge to drive Network Inference. This
approach is based on the selection of a relevant set of genes, called the
"molecular signature", associated with a condition of interest (for instance,
the genes involved in disease development). In this context, differential
expression analysis is a well established strategy. However outcome signatures
are often not consistent and show little overlap between studies. Thus, we will
dedicate the first part of our work to the improvement of the standard process
of biomarker identification to guarantee the robustness and reproducibility of
the molecular signature.
Our approach enables to compare the networks inferred between two conditions
of interest (for instance case and control networks) and help along the
biological interpretation of results. Thus it allows to identify differential
regulations that occur in these conditions. We illustrate the proposed approach
by applying our method to a study of breast cancer's response to treatment
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
A Cluster Elastic Net for Multivariate Regression
We propose a method for estimating coefficients in multivariate regression
when there is a clustering structure to the response variables. The proposed
method includes a fusion penalty, to shrink the difference in fitted values
from responses in the same cluster, and an L1 penalty for simultaneous variable
selection and estimation. The method can be used when the grouping structure of
the response variables is known or unknown. When the clustering structure is
unknown the method will simultaneously estimate the clusters of the response
and the regression coefficients. Theoretical results are presented for the
penalized least squares case, including asymptotic results allowing for p >> n.
We extend our method to the setting where the responses are binomial variables.
We propose a coordinate descent algorithm for both the normal and binomial
likelihood, which can easily be extended to other generalized linear model
(GLM) settings. Simulations and data examples from business operations and
genomics are presented to show the merits of both the least squares and
binomial methods.Comment: 37 Pages, 11 Figure
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