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1.  Probabilistic analysis of gene expression measurements from heterogeneous tissues 
Bioinformatics  2010;26(20):2571-2577.
Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e.g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content.
Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches.
Availability and Software: All codes are written in Matlab, and are freely available upon request as well as at the project web page∼erkkila2/. Furthermore, a web-application for DSection exists at
PMCID: PMC2951082  PMID: 20631160
2.  A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data 
BMC Bioinformatics  2009;10:165.
Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible.
This paper presents a novel Beta-Gaussian mixture model (BGMM) for clustering genes based on Gaussian distributed and beta distributed data. The proposed BGMM can be viewed as a natural extension of the beta mixture model (BMM) and the Gaussian mixture model (GMM). The proposed BGMM method differs from other mixture model based methods in its integration of two different data types into a single and unified probabilistic modeling framework, which provides a more efficient use of multiple data sources than methods that analyze different data sources separately. Moreover, BGMM provides an exceedingly flexible modeling framework since many data sources can be modeled as Gaussian or beta distributed random variables, and it can also be extended to integrate data that have other parametric distributions as well, which adds even more flexibility to this model-based clustering framework. We developed three types of estimation algorithms for BGMM, the standard expectation maximization (EM) algorithm, an approximated EM and a hybrid EM, and propose to tackle the model selection problem by well-known model selection criteria, for which we test the Akaike information criterion (AIC), a modified AIC (AIC3), the Bayesian information criterion (BIC), and the integrated classification likelihood-BIC (ICL-BIC).
Performance tests with simulated data show that combining two different data sources into a single mixture joint model greatly improves the clustering accuracy compared with either of its two extreme cases, GMM or BMM. Applications with real mouse gene expression data (modeled as Gaussian distribution) and protein-DNA binding probabilities (modeled as beta distribution) also demonstrate that BGMM can yield more biologically reasonable results compared with either of its two extreme cases. One of our applications has found three groups of genes that are likely to be involved in Myd88-dependent Toll-like receptor 3/4 (TLR-3/4) signaling cascades, which might be useful to better understand the TLR-3/4 signal transduction.
PMCID: PMC2717092  PMID: 19480678

Results 1-2 (2)