Timo Erkkilä

Probabilistic analysis of gene expression measurements from heterogeneous tissues

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 http://www.cs.tut.fi/∼erkkila2/. Furthermore, a web-application for DSection exists at http://informatics.systemsbiology.net/DSection. Contact: timo.p.erkkila@tut.fi; harri.lahdesmaki@tut.fi

Chemical castration and anti-androgens induce differential gene expression in prostate cancer.

Endocrine therapy by castration or anti‐androgens is the gold standard treatment for advanced prostate cancer. Although it has been used for decades, the molecular consequences of androgen deprivation are incompletely known and biomarkers of its resistance are lacking. In this study, we studied the molecular mechanisms of hormonal therapy by comparing the effect of bicalutamide (anti‐androgen), goserelin (GnRH agonist) and no therapy, followed by radical prostatectomy. For this purpose, 28 men were randomly assigned to treatment groups. Freshly frozen specimens were used for gene expression profiling for all known protein‐coding genes. An in silico Bayesian modelling tool was used to assess cancer‐specific gene expression from heterogeneous tissue specimens. The expression of 128 genes was > two‐fold reduced by the treatments. Only 16% of the altered genes were common in both treatment groups. Of the 128 genes, only 24 were directly androgen‐regulated genes, according to re‐analysis of previous data on gene expression, androgen receptor‐binding sites and histone modifications in prostate cancer cell line models. The tumours containing TMPRSS2–ERG fusion showed higher gene expression of genes related to proliferation compared to the fusion‐negative tumours in untreated cases. Interestingly, endocrine therapy reduced the expression of one‐half of these genes and thus diminished the differences between the fusion‐positive and ‐negative samples. This study reports the significantly different effects of an anti‐androgen and a GnRH agonist on gene expression in prostate cancer cells. TMPRSS2‐ERG fusion seems to bring many proliferation‐related genes under androgen regulation. Copyright

Efficient automated method for image-based classification of microbial cells

Monitoring of bacterial populations requires automated analysis tools that provide accurate cell type quantification results. Here, methods for automated image analysis and bacteria type classification are presented. The classification method employs several discriminative features, calculated from automatically segmented images, for class determination. The performance of the algorithm is evaluated with a case study where three different bacterial types are present. Moreover, the accuracy of the method is demonstrated by generating experiments of synthetic bacterial population images.

A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data

BackgroundCluster 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.ResultsThis 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).ConclusionPerformance 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.

Tampere Verification Tool

Tampere Verification Tool (TVT) is a collection of programs for automated verification of concurrent and reactive systems. TVT has its roots in process algebras and explicit state space exploration, but in addition to actions, our formalism allows use of state-based information in the form of truth-valued state propositions. Furthermore, it contains three types of state proposition-like notions to support on-the-fly verification, and one state proposition to exploit partially defined processes. TVT supports compositional state space construction, stubborn sets and visual verification.

Goserelin and bicalutamide treatments alter the expression of microRNAs in the prostate.

Although endocrine therapy has been used for decades, its influence on the expression of microRNAs (miRNAs) in clinical tissue specimens has not been analyzed. Moreover, the effects of the TMPRSS2:ERG fusion on the expression of miRNAs in hormone naïve and endocrine‐treated prostate cancers are poorly understood.

Inference of Boolean Networks from Time Series Data with Realistic Characteristics

In this paper, we infer Boolean networks based on simulated data. Depending on the approach, the simulated data is either extracted from Boolean networks or from a biochemical network model. Noise was added using the existing hierarchical error model (HEM). We use Boolean inference algorithms based on best-fit to find the connections between nodes. In order to validate the inference processes, we compare the ground truth and inferred connections. The results show that the best-fit inference algorithm is suitable for Boolean approach, but it fails when using the biochemical network model.

Microarray Simulator as Educational Tool

As many real-world applications, microarray measurements are inapplicable for large-scale teaching purposes due to their laborious preparation process and expense. Fortunately, many phases of the array preparation process can be efficiently demonstrated by using a software simulator tool. Here we propose the use of microarray simulator as an aiding tool in teaching of computational biology. Three case studies on educational use of the simulator are presented, which demonstrate the effect of gene knock-out, synthetic time series, and effect of noise sources. We conclude that the simulator, used for teaching the principles of microarray measurement technology, proved to be a useful tool in education.

The eighth annual MLSP competition: Second place team

This paper describes our submission to the eighth annual MLSP competition organized by Amazon during the 2012 IEEE MLSP workshop. Our approach is based on a nearest-neighbor-like classifier with a distance metric learned from samples. The method was second in the final standings with prediction accuracy of 81 %, while the winning submission was 87 % accurate.