27,009 research outputs found
Semantic distillation: a method for clustering objects by their contextual specificity
Techniques for data-mining, latent semantic analysis, contextual search of
databases, etc. have long ago been developed by computer scientists working on
information retrieval (IR). Experimental scientists, from all disciplines,
having to analyse large collections of raw experimental data (astronomical,
physical, biological, etc.) have developed powerful methods for their
statistical analysis and for clustering, categorising, and classifying objects.
Finally, physicists have developed a theory of quantum measurement, unifying
the logical, algebraic, and probabilistic aspects of queries into a single
formalism. The purpose of this paper is twofold: first to show that when
formulated at an abstract level, problems from IR, from statistical data
analysis, and from physical measurement theories are very similar and hence can
profitably be cross-fertilised, and, secondly, to propose a novel method of
fuzzy hierarchical clustering, termed \textit{semantic distillation} --
strongly inspired from the theory of quantum measurement --, we developed to
analyse raw data coming from various types of experiments on DNA arrays. We
illustrate the method by analysing DNA arrays experiments and clustering the
genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence,
Springer-Verla
Infinite factorization of multiple non-parametric views
Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale of classical canonical correlation analysis into a flexible, generative and non-parametric clustering
setting, by introducing a novel non-parametric hierarchical
mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the views. The commonalities between the sources are modeled by an infinite block
model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views.
Cluster analysis of co-expression is a standard simple way of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation
Evaluating Overfit and Underfit in Models of Network Community Structure
A common data mining task on networks is community detection, which seeks an
unsupervised decomposition of a network into structural groups based on
statistical regularities in the network's connectivity. Although many methods
exist, the No Free Lunch theorem for community detection implies that each
makes some kind of tradeoff, and no algorithm can be optimal on all inputs.
Thus, different algorithms will over or underfit on different inputs, finding
more, fewer, or just different communities than is optimal, and evaluation
methods that use a metadata partition as a ground truth will produce misleading
conclusions about general accuracy. Here, we present a broad evaluation of over
and underfitting in community detection, comparing the behavior of 16
state-of-the-art community detection algorithms on a novel and structurally
diverse corpus of 406 real-world networks. We find that (i) algorithms vary
widely both in the number of communities they find and in their corresponding
composition, given the same input, (ii) algorithms can be clustered into
distinct high-level groups based on similarities of their outputs on real-world
networks, and (iii) these differences induce wide variation in accuracy on link
prediction and link description tasks. We introduce a new diagnostic for
evaluating overfitting and underfitting in practice, and use it to roughly
divide community detection methods into general and specialized learning
algorithms. Across methods and inputs, Bayesian techniques based on the
stochastic block model and a minimum description length approach to
regularization represent the best general learning approach, but can be
outperformed under specific circumstances. These results introduce both a
theoretically principled approach to evaluate over and underfitting in models
of network community structure and a realistic benchmark by which new methods
may be evaluated and compared.Comment: 22 pages, 13 figures, 3 table
Simcluster: clustering enumeration gene expression data on the simplex space
Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.

Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster.

Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data
Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects
Time course microarray data provide insight about dynamic biological
processes. While several clustering methods have been proposed for the analysis
of these data structures, comparison and selection of appropriate clustering
methods are seldom discussed. We compared probabilistic based clustering
methods and distance based clustering methods for time course microarray
data. Among probabilistic methods, we considered: smoothing spline clustering
also known as model based functional data analysis (MFDA), functional
clustering models for sparsely sampled data (FCM) and model-based clustering
(MCLUST). Among distance based methods, we considered: weighted gene
co-expression network analysis (WGCNA), clustering with dynamic time warping
distance (DTW) and clustering with autocorrelation based distance (ACF). We
studied these algorithms in both simulated settings and case study data. Our
investigations showed that FCM performed very well when gene curves were short
and sparse. DTW and WGCNA performed well when gene curves were medium or long
( observations). SSC performed very well when there were clusters of gene
curves similar to one another. Overall, ACF performed poorly in these
applications. In terms of computation time, FCM, SSC and DTW were considerably
slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more
accurate and biological meaningful clustering results. WGCNA and MCLUST are the
best methods among the 6 methods compared, when performance and computation
time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides
model based inference and uncertainty measure of clustering results
Validating module network learning algorithms using simulated data
In recent years, several authors have used probabilistic graphical models to
learn expression modules and their regulatory programs from gene expression
data. Here, we demonstrate the use of the synthetic data generator SynTReN for
the purpose of testing and comparing module network learning algorithms. We
introduce a software package for learning module networks, called LeMoNe, which
incorporates a novel strategy for learning regulatory programs. Novelties
include the use of a bottom-up Bayesian hierarchical clustering to construct
the regulatory programs, and the use of a conditional entropy measure to assign
regulators to the regulation program nodes. Using SynTReN data, we test the
performance of LeMoNe in a completely controlled situation and assess the
effect of the methodological changes we made with respect to an existing
software package, namely Genomica. Additionally, we assess the effect of
various parameters, such as the size of the data set and the amount of noise,
on the inference performance. Overall, application of Genomica and LeMoNe to
simulated data sets gave comparable results. However, LeMoNe offers some
advantages, one of them being that the learning process is considerably faster
for larger data sets. Additionally, we show that the location of the regulators
in the LeMoNe regulation programs and their conditional entropy may be used to
prioritize regulators for functional validation, and that the combination of
the bottom-up clustering strategy with the conditional entropy-based assignment
of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio
Motif Discovery through Predictive Modeling of Gene Regulation
We present MEDUSA, an integrative method for learning motif models of
transcription factor binding sites by incorporating promoter sequence and gene
expression data. We use a modern large-margin machine learning approach, based
on boosting, to enable feature selection from the high-dimensional search space
of candidate binding sequences while avoiding overfitting. At each iteration of
the algorithm, MEDUSA builds a motif model whose presence in the promoter
region of a gene, coupled with activity of a regulator in an experiment, is
predictive of differential expression. In this way, we learn motifs that are
functional and predictive of regulatory response rather than motifs that are
simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model
of the transcriptional control logic that can predict the expression of any
gene in the organism, given the sequence of the promoter region of the target
gene and the expression state of a set of known or putative transcription
factors and signaling molecules. Each motif model is either a -length
sequence, a dimer, or a PSSM that is built by agglomerative probabilistic
clustering of sequences with similar boosting loss. By applying MEDUSA to a set
of environmental stress response expression data in yeast, we learn motifs
whose ability to predict differential expression of target genes outperforms
motifs from the TRANSFAC dataset and from a previously published candidate set
of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed
binding sites associated with environmental stress response from the
literature.Comment: RECOMB 200
Deep generative modeling for single-cell transcriptomics.
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task
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