5,634 research outputs found
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses
Incomplete graphical model inference via latent tree aggregation
Graphical network inference is used in many fields such as genomics or
ecology to infer the conditional independence structure between variables, from
measurements of gene expression or species abundances for instance. In many
practical cases, not all variables involved in the network have been observed,
and the samples are actually drawn from a distribution where some variables
have been marginalized out. This challenges the sparsity assumption commonly
made in graphical model inference, since marginalization yields locally dense
structures, even when the original network is sparse. We present a procedure
for inferring Gaussian graphical models when some variables are unobserved,
that accounts both for the influence of missing variables and the low density
of the original network. Our model is based on the aggregation of spanning
trees, and the estimation procedure on the Expectation-Maximization algorithm.
We treat the graph structure and the unobserved nodes as missing variables and
compute posterior probabilities of edge appearance. To provide a complete
methodology, we also propose several model selection criteria to estimate the
number of missing nodes. A simulation study and an illustration flow cytometry
data reveal that our method has favorable edge detection properties compared to
existing graph inference techniques. The methods are implemented in an R
package
From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique
For reasoning under uncertainty the Bayesian network has become the representation of choice.
However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual
information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity.
Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some
researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world
assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal
Pain, a group of exclusion
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
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