3 research outputs found
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
Bayesian correlated clustering to integrate multiple datasets
Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets.
Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods
Probabilistic analysis of the human transcriptome with side information
Understanding functional organization of genetic information is a major
challenge in modern biology. Following the initial publication of the human
genome sequence in 2001, advances in high-throughput measurement technologies
and efficient sharing of research material through community databases have
opened up new views to the study of living organisms and the structure of life.
In this thesis, novel computational strategies have been developed to
investigate a key functional layer of genetic information, the human
transcriptome, which regulates the function of living cells through protein
synthesis. The key contributions of the thesis are general exploratory tools
for high-throughput data analysis that have provided new insights to
cell-biological networks, cancer mechanisms and other aspects of genome
function.
A central challenge in functional genomics is that high-dimensional genomic
observations are associated with high levels of complex and largely unknown
sources of variation. By combining statistical evidence across multiple
measurement sources and the wealth of background information in genomic data
repositories it has been possible to solve some the uncertainties associated
with individual observations and to identify functional mechanisms that could
not be detected based on individual measurement sources. Statistical learning
and probabilistic models provide a natural framework for such modeling tasks.
Open source implementations of the key methodological contributions have been
released to facilitate further adoption of the developed methods by the
research community.Comment: Doctoral thesis. 103 pages, 11 figure