4,163 research outputs found
Reverse-engineering transcriptional modules from gene expression data
"Module networks" are a framework to learn gene regulatory networks from
expression data using a probabilistic model in which coregulated genes share
the same parameters and conditional distributions. We present a method to infer
ensembles of such networks and an averaging procedure to extract the
statistically most significant modules and their regulators. We show that the
inferred probabilistic models extend beyond the data set used to learn the
models.Comment: 5 pages REVTeX, 4 figure
Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli
The set of regulatory interactions between genes, mediated by transcription
factors, forms a species' transcriptional regulatory network (TRN). By
comparing this network with measured gene expression data one can identify
functional properties of the TRN and gain general insight into transcriptional
control. We define the subnet of a node as the subgraph consisting of all nodes
topologically downstream of the node, including itself. Using a large set of
microarray expression data of the bacterium Escherichia coli, we find that the
gene expression in different subnets exhibits a structured pattern in response
to environmental changes and genotypic mutation. Subnets with less changes in
their expression pattern have a higher fraction of feed-forward loop motifs and
a lower fraction of small RNA targets within them. Our study implies that the
TRN consists of several scales of regulatory organization: 1) subnets with more
varying gene expression controlled by both transcription factors and
post-transcriptional RNA regulation, and 2) subnets with less varying gene
expression having more feed-forward loops and less post-transcriptional RNA
regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog
Techniques for clustering gene expression data
Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered
Recommended from our members
Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits.
Spontaneous and sensory-evoked activity propagates across varying spatial scales in the mammalian cortex, but technical challenges have limited conceptual links between the function of local neuronal circuits and brain-wide network dynamics. We present a method for simultaneous cellular-resolution two-photon calcium imaging of a local microcircuit and mesoscopic widefield calcium imaging of the entire cortical mantle in awake mice. Our multi-scale approach involves a microscope with an orthogonal axis design where the mesoscopic objective is oriented above the brain and the two-photon objective is oriented horizontally, with imaging performed through a microprism. We also introduce a viral transduction method for robust and widespread gene delivery in the mouse brain. These approaches allow us to identify the behavioral state-dependent functional connectivity of pyramidal neurons and vasoactive intestinal peptide-expressing interneurons with long-range cortical networks. Our imaging system provides a powerful strategy for investigating cortical architecture across a wide range of spatial scales
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
Optimal Kullback-Leibler Aggregation via Information Bottleneck
In this paper, we present a method for reducing a regular, discrete-time
Markov chain (DTMC) to another DTMC with a given, typically much smaller number
of states. The cost of reduction is defined as the Kullback-Leibler divergence
rate between a projection of the original process through a partition function
and a DTMC on the correspondingly partitioned state space. Finding the reduced
model with minimal cost is computationally expensive, as it requires an
exhaustive search among all state space partitions, and an exact evaluation of
the reduction cost for each candidate partition. Our approach deals with the
latter problem by minimizing an upper bound on the reduction cost instead of
minimizing the exact cost; The proposed upper bound is easy to compute and it
is tight if the original chain is lumpable with respect to the partition. Then,
we express the problem in the form of information bottleneck optimization, and
propose using the agglomerative information bottleneck algorithm for searching
a sub-optimal partition greedily, rather than exhaustively. The theory is
illustrated with examples and one application scenario in the context of
modeling bio-molecular interactions.Comment: 13 pages, 4 figure
- âŚ