Modular network construction using eQTL data: an analysis of computational costs and benefits

Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest. Our focus is the systematic evaluation of the trade-off between network size and network search efficiency in the construction of these networks. Results: We developed a modular approach to network construction, building from smaller networks to larger ones, thereby reducing the search space while including more variables in the analysis. The goal is achieving a lower computational cost while maintaining high confidence in the resulting networks. As demonstrated in our simulation results, networks built in this way have low node/edge false discovery rate (FDR) and high edge sensitivity comparing to greedy search. We further demonstrate our method in a data set of cellular responses to two chemotherapeutic agents: docetaxel and 5-fluorouracil (5-FU), and identify biologically plausible networks that might describe resistances to these drugs. Conclusion: In this study, we suggest that guided comprehensive searches for parsimonious networks should be considered as an alternative to greedy network searches

The Integrative Correlation Coefficient: a Measure of Cross-study Reproducibility for Gene Expressionea Array Data

Multi-study analysis adds value to microarray experiments. However, because of significant technical differences between microarray platforms, and because of differences in study design, it can be difficult to combine data. We have developed a statistical measure of reproducibility that can be applied to individual genes, measured in two different studies. This statistic, which we call the Integrative Correlation Coefficient or Correlation of Correlations, borrows strength across many genes to estimate the strength of the relationship between expression values in the two studies

Analysis of Affymetrix GeneChip Data Using Amplified RNA

The standard method of target synthesis for hybridization to Affymetrix GeneChip® expression microarrays requires a relatively large amount of input total RNA (1-15 micrograms). When small biological samples are collected by microdissection or other methods, amplification techniques are required to provide sufficient target for hybridization to expression arrays. One amplification technique used is to perform two successive rounds of T7-based in vitro transcription. However, the use of random primers required to re-generate cDNA from the first round transcription reaction results in shortened copies of the cDNA, and ultimately the cRNA, transcripts from which the 5\u27 end is missing. In this paper we describe an experiment designed to compare the quality of data obtained from labeling small RNA samples using the Affymetrix Small Sample Target Labeling Protocol V 2 to that of data obtained using the standard protocol. We utilized different preprocessing algorithms to compare the data generated using both labeling methods and present a new algorithm that improves upon existing ones in this setting

Monitoring of Serum DNA Methylation as an Early Independent Marker of Response and Survival in Metastatic Breast Cancer: TBCRC 005 Prospective Biomarker Study

Epigenetic alterations measured in blood may help guide breast cancer treatment. The multisite prospective study TBCRC 005 was conducted to examine the ability of a novel panel of cell-free DNA methylation markers to predict survival outcomes in metastatic breast cancer (MBC) using a new quantitative multiplex assay (cMethDNA)

How predation and landscape fragmentation affect vole population dynamics

Background: Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings: By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance: There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics

Важливе історико-географічне дослідження

Рец. на кн. Темушева В.Н. "Гомельская земля в конце XV первой половине XVI в. Территориальные трансформации в пограничном регионе". — М.: "Квадрига", 2009. — 190 с.Review of the book: Temushev V.N. "Gomel Land in the Late 15th — the 1st half of the 16th Centuries. Territorial Transformations in the Frontier Area". — Moscow: "Kvadriga", 2009. — 190 p

Identifying differential correlation in gene/pathway combinations

<p>Abstract</p> <p>Background</p> <p>An important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level "summaries" for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.</p> <p>Results</p> <p>We present a novel method for the analysis of microarray data that identifies joint differential expression in gene-pathway pairs. This method takes advantage of known gene pathway memberships to compute a summary expression level for each pathway as a whole. Correlations between the pathway expression summary and the expression levels of genes not already known to be associated with the pathway provide clues to gene interaction dependencies that are not already accounted for through known pathway identification, and statistically significant differences between gene-pathway correlations in phenotypically different cells (e.g., where the expression level of a single gene and a given pathway summary correlate strongly in normal cells but weakly in tumor cells) may indicate biologically relevant gene-pathway interactions. Here, we detail the methodology and present the results of this method applied to two gene-expression datasets, identifying gene-pathway pairs which exhibit differential joint expression by phenotype.</p> <p>Conclusion</p> <p>The method described herein provides a means by which interactions between large numbers of genes may be identified by incorporating known pathway information to reduce the dimensionality of gene interactions. The method is efficient and easily applied to data sets of ~10²arrays. Application of this method to two publicly-available cancer data sets yields suggestive and promising results. This method has the potential to complement gene-at-a-time analysis techniques for microarray analysis by indicating relationships between pathways and genes that have not previously been identified and which may play a role in disease.</p

Modeling precision treatment of breast cancer

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified