Joanna Zyla

Seeking genetic signature of radiosensitivity - a novel method for data analysis in case of small sample sizes

BackgroundThe identification of polymorphisms and/or genes responsible for an organisms radiosensitivity increases the knowledge about the cell cycle and the mechanism of the phenomena themselves, possibly providing the researchers with a better understanding of the process of carcinogenesis.AimThe aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity in the case of small sample size studies.ResultsAmong many indirect measures of radiosensitivity known, the level of radiation-induced chromosomal aberrations was used in the study. Mathematical modelling allowed the transformation of the yield-time curve of radiation-induced chromosomal aberrations into the exponential curve with limited number of parameters, while Gaussian mixture models applied to the distributions of these parameters provided the criteria for mouse strain classification. A detailed comparative analysis of genotypes between the obtained subpopulations of mice followed by functional validation provided a set of candidate polymorphisms that might be related to radiosensitivity. Among 1857 candidate relevant SNPs, that cluster in 28 genes, eight SNPs were detected nonsynonymous (nsSNP) on protein function. Two of them, rs48840878 (gene Msh3) and rs5144199 (gene Cc2d2a), were predicted as having increased probability of a deleterious effect. Additionally, rs48840878 is capable of disordering phosphorylation with 14 PKs. In silico analysis of candidate relevant SNP similarity score distribution among 60 CGD mouse strains allowed for the identification of SEA/GnJ and ZALENDE/EiJ mouse strains (95.26% and 86.53% genetic consistency respectively) as the most similar to radiosensitive subpopulatioConclusionsA complete step-by-step strategy for seeking the genetic signature of radiosensitivity in the case of small sample size studies conducted on mouse models was proposed. It is shown that the strategy, which is a combination of mathematical modelling, statistical analysis and data mining methodology, allows for the discovery of candidate polymorphisms which might be responsible for radiosensitivity phenomena.

Ranking metrics in gene set enrichment analysis: do they matter?

BackgroundThere exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results.Methods and resultsIn this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA.ConclusionsChoosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner-Weiss-Schindler test statistic gives better outcomes. Also, it finds more enriched pathways than other tested metrics, which may induce new biological discoveries.

Sensitivity, Specificity and Prioritization of Gene Set Analysis When Applying Different Ranking Metrics

Microarrays were a trigger to develop new methods which can allow to estimate disturbances in signal cascades, characterized by sets of genes, in various biological conditions. Existing approaches of gene set analysis take information if genes are differentially expressed or are based on some gene ranking. The most commonly used method is Gene Set Enrichment Analysis (GSEA), where an assumption of uniform distribution of genes in some gene set is tested by weighted Kolmogorov-Smirnov test. Many studies present different gene set analysis methods and their comparison, however none of them focus on basic but crucial parameters, like the rank metric. In this paper we compare nine ranking metrics in terms of sensitivity, specificity and prioritization of identification of functional gene sets using a collection of 34 annotated microarray datasets. We show that absolute value of default GSEA measure is the best ranking metric, while the Baumgartner-Weiss-Schindler test statistic is the best statistical-based metrics, which can be used in Gene Set Enrichment Analysis.

Investigation for Genetic Signature of Radiosensitivity - Data Analysis

The aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity. Radiosensitivity is the relative susceptibility of cells, tissues, organs or organisms to the harmful effect of radiation. Effects of radiation include the mutation of DNA specialy in genes responsible for DNA repair. Identification of polymorphisms and genes responsible for an organisms’ radiosensitivity increases the knowledge about the cell cycle and the mechanism of radiosensitivity, possibly providing the researchers with a better understanding of the process of carcinogenesis. To obtain this results, mathematical modeling and data mining methods were used.

Reproducibility of Finding Enriched Gene Sets in Biological Data Analysis

Introducing the high-throughput measurement methods into molecular biology was a trigger to develop the algorithms for searching disorders in complex signalling systems, like pathways or gene ontologies. In recent years, there appeared many new solutions, but the results obtained with these techniques are ambiguous. In this work, five different algorithms for pathway enrichment analysis were compared using six microarray datasets covering cases with the same disease. The number of enriched pathways at different significance level and false positive rate of finding enrichment pathways was estimated, and reproducibility of obtained results between datasets was checked. The best performance was obtained for PLAGE method. However, taking into consideration the biological knowledge about analyzed disease condition, many findings may be false positives. Out of the other methods GSVA algorithm gave the most reproducible results across tested datasets, which was also validated in biological repositories. Similarly, good outcomes were given by GSEA method. ORA and PADOG gave poor sensitivity and reproducibility, which stand in contrary to previous research.

Multigene P-value Integration Based on SNPs Investigation for Seeking Radiosensitivity Signatures

Dysregulation of apoptosis is a key attribute of cancer, especially the one induced by p53 expression disruption. Radiotherapy, sometimes supported by chemotherapy and/or pre-surgery, is recommended in majority of cases, but despite of the very well defined treatment protocols and high quality irradiation procedure, the huge dispersion in response to the radiotherapy is observed among cancer patients. Patient radiosensitivity, according to up-to-date knowledge, is at least partially responsible for different reactions to ionising radiation. Here we concentrate on investigation of single nucleotide polymorphisms (SNP) which can possibly explain the radiation response phenomena. To reach this goal dependent and independent methods of p-value integrations are presented and compared. Both statistical and molecular function domains are used in comparison study. We propose a novel method of p-value integration which includes the control of gene expression trend and introduces the adaptive significance level. What is more the multigene approach is proposed in contrary to classical single gene investigation. As a result, set of statistically significant polymorphisms was obtained, among which some were identified as possible deleterious for KRAS signalling pathway.

Is the Identification of SNP-miRNA Interactions Supporting the Prediction of Human Lymphocyte Transcriptional Radiation Responses?

Genome-Wide Association Studies (GWAS) are of great importance in identifying the genetic variants associated with traits/diseases. Due to the high number of candidate SNPs some filtering techniques are necessary to be applied. The aim of the study was to develop the comprehensive approach allowing for detailed analysis of both SNP-gene and SNP-miRNA-gene relations. We elaborated and optimized the novel signal analysis pipeline improving significantly the results of the analysis on genotype-phenotype interplay. Direct links between genotype results and gene expression levels were enriched by detailed analysis of SNP-miRNA-gene interactions at both mature miRNA structure/seed region and target binding site level. The proposed technique was applied to the data on lymphocyte radiation response and increased by almost 100% number of potential

Modelling of Genetic Interactions in GWAS Reveals More Complex Relations between Genotype and Phenotype

The aim of this work is to present the complete methodology useful in GWAS analysis with small sample size, where comprehension of interaction between the genotype and phenotype is a main issue. By including all possible models of interaction into the process of model building, we were able to significantly increase the number of candidate polymorphisms and decrease the false discovery ratio.