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.

Bioinformatics – recent issues

ACT

Arabidopsis Co-expression Tool (ACT) is a resource for investigating the co-expression of genes in the NASC/GARNet microarray-based gene expression dataset from Arabidopsis.
: gene expression

Global analysis of microarray data reveals intrinsic properties in gene expression and tissue selectivity.

Publication Date: 2010 May 28 PMID: 20511364
Authors: Kim, C. – Choi, J. – Park, H. – Park, Y. – Park, J. – Park, T. – Cho, K. – Yang, Y. – Yoon, S.
Journal: Bioinformatics

MOTIVATION: It is expected that individual genes have intrinsically different variability in the global expressional trend among them. Thus the consideration of gene-specific expressional properties will help us to distinguish target-selective gene expression over non-selective overexpression. RESULTS: The re-standardization and integration of heterogeneous microarray datasets available from public databases have enabled us to determine the global expression properties of individual genes across a wide variety of experimental conditions and samples. The global averages and standard deviations of expression for each gene in the integrated microarray datasets were found to be intrinsic properties that were consistent among independent collections of datasets using different microarray platforms. Using the gene-specific intrinsic parameters to rescale the microarray data, we were able to distinguish novel selective gene expression (COMP, Collagen X) in breast cancer tissues from non-selective over-expression, a difference that has not been detectable by conventional methods. Availability and Implementation: The web-based tool for GS-LAGE is available at http://lage.sookmyung.ac.kr. CONTACT: yoonsj@sookmyung.ac.kr.

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Integrative Platform to Translate Gene Sets to Networks.

Publication Date: 2010 May 27 PMID: 20507894
Authors: Laakso, M. – Hautaniemi, S.
Journal: Bioinformatics

SUMMARY: We have implemented a computational platform (Moksiskaan) that integrates pathway, protein-protein interaction, genome and literature mining data to result in comprehensive networks for a list of genes or proteins. Moksiskaan is able to generate hypothetical pathways for these genes or proteins as well as estimate their activation statuses using regulation information in pathway repositories. An automatically generated result document provides a detailed description of the query genes, biological processes and drug targets. Moksiskaan networks can be downloaded to Cytoscape for further analysis. To demonstrate the utility of Moksiskaan, we use gene microarray and clinical data from > 200 glioblastoma multiforme primary tumor samples and translate the resulting set of 124 survival associated genes to a network. Availability and Implementation: Moksiskaan and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/moksiskaan/. CONTACT: Sampsa.Hautaniemi@Helsinki.FI SUPPLEMENTARY INFORMATION: Supplementary information: Automatically generated report of the case study is available at Bioinformatics online.

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Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms.

Publication Date: 2010 May 28 PMID: 20507637
Authors: Hui, C. K. – Karuturi, R. K.
Journal: Algorithms Mol Biol

ABSTRACT: BACKGROUND: Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking. RESULTS: In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking. CONCLUSIONS: Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.

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