195,142 research outputs found
Analysis of a Gibbs sampler method for model based clustering of gene expression data
Over the last decade, a large variety of clustering algorithms have been
developed to detect coregulatory relationships among genes from microarray gene
expression data. Model based clustering approaches have emerged as
statistically well grounded methods, but the properties of these algorithms
when applied to large-scale data sets are not always well understood. An
in-depth analysis can reveal important insights about the performance of the
algorithm, the expected quality of the output clusters, and the possibilities
for extracting more relevant information out of a particular data set. We have
extended an existing algorithm for model based clustering of genes to
simultaneously cluster genes and conditions, and used three large compendia of
gene expression data for S. cerevisiae to analyze its properties. The algorithm
uses a Bayesian approach and a Gibbs sampling procedure to iteratively update
the cluster assignment of each gene and condition. For large-scale data sets,
the posterior distribution is strongly peaked on a limited number of
equiprobable clusterings. A GO annotation analysis shows that these local
maxima are all biologically equally significant, and that simultaneously
clustering genes and conditions performs better than only clustering genes and
assuming independent conditions. A collection of distinct equivalent
clusterings can be summarized as a weighted graph on the set of genes, from
which we extract fuzzy, overlapping clusters using a graph spectral method. The
cores of these fuzzy clusters contain tight sets of strongly coexpressed genes,
while the overlaps exhibit relations between genes showing only partial
coexpression.Comment: 8 pages, 7 figure
Partial mixture model for tight clustering of gene expression time-course
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to
this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.
Results: In this paper we show the inherent robustness of the minimum distance estimator that makes it a powerful tool for parameter estimation in model-based time-course clustering. To apply minimum distance estimation, a partial mixture model that can naturally incorporate replicate
information and allow scattered genes is formulated. We provide experimental results of simulated data fitting, where the minimum distance estimator demonstrates superior performance to the maximum likelihood estimator. Both biological and statistical validations are conducted on a
simulated dataset and two real gene expression datasets. Our proposed partial regression clustering algorithm scores top in Gene Ontology driven evaluation, in comparison with four other popular clustering algorithms.
Conclusion: For the first time partial mixture model is successfully extended to time-course data analysis. The robustness of our partial regression clustering algorithm proves the suitability of the ombination of both partial mixture model and minimum distance estimator in this field. We show that tight clustering not only is capable to generate more profound understanding of the dataset
under study well in accordance to established biological knowledge, but also presents interesting new hypotheses during interpretation of clustering results. In particular, we provide biological evidences that scattered genes can be relevant and are interesting subjects for study, in contrast to prevailing opinion
Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy
Epistatic or genetic interactions, representing the effects of mutations on the phenotypes caused by other mutations, can be very helpful for uncovering functional relationships between genes. Recently, the Epistasis Miniarray Profile (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. As part of this approach, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles. Here we present an original biclustering algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function, a goal that cannot be achieved with hierarchical clustering. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast S. cerevisiae. In addition to identifying the majority of the functional modules reported in these studies, our algorithm uncovers many recently documented and novel multi-functional relationships between genes and gene groups
Inferring meta-covariates in classification
This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
Comparing large covariance matrices has important applications in modern
genomics, where scientists are often interested in understanding whether
relationships (e.g., dependencies or co-regulations) among a large number of
genes vary between different biological states. We propose a computationally
fast procedure for testing the equality of two large covariance matrices when
the dimensions of the covariance matrices are much larger than the sample
sizes. A distinguishing feature of the new procedure is that it imposes no
structural assumptions on the unknown covariance matrices. Hence the test is
robust with respect to various complex dependence structures that frequently
arise in genomics. We prove that the proposed procedure is asymptotically valid
under weak moment conditions. As an interesting application, we derive a new
gene clustering algorithm which shares the same nice property of avoiding
restrictive structural assumptions for high-dimensional genomics data. Using an
asthma gene expression dataset, we illustrate how the new test helps compare
the covariance matrices of the genes across different gene sets/pathways
between the disease group and the control group, and how the gene clustering
algorithm provides new insights on the way gene clustering patterns differ
between the two groups. The proposed methods have been implemented in an
R-package HDtest and is available on CRAN.Comment: The original title dated back to May 2015 is "Bootstrap Tests on High
Dimensional Covariance Matrices with Applications to Understanding Gene
Clustering
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