European identity at the beginning of the 21 century

This bachelor thesis deals with European identity and the role of the European Union in its formation at the beginning of the 21st century. The individual chapters speak about identity in general and its types with an emphasis on collective identity, next topic is European identity, its models and interpretations in official EU documents. Last chapter focuses on the concept of European identity in the expressions of politicians and experts, the concrete identity-making tools of the EU and the feeling of European identity by EU citizens. The thesis aims to identify the features of contemporary European identity at the beginning of the 21st century, to analyze the current EU´s identity-making strategy and evaluate its success. Based on the analysis, it was found that the European Union is successful in the creation and encouragement of European identity

Additional File 4: Figure S3.

Ranking performance at increasing effect size for the remaining methods. The effect size varies from 3, 5 to 7 and the group size is fixed at 6 + 6. Panels a-c show results for the first data set and panels d-f show results for the second data set. The receiver operating characteristic curves are averaged over 100 realizations of resampled data. The included methods are the t-test using the square root transform (sqrtT), the t-test using log transform (logT), the non-transformed pooled t-test (tTest), Welch’s test (Welch), Wilcoxon-Mann–Whitney test (WMW), the binomial test (binomial), the non-overdispersed Poisson generalized linear model (GLM) and Fisher’s exact test (Fisher). (PDF 135 kb

Online Comparators in Insurance Business

This bachelor thesis deals with problems of on-line insurance comparators. The theoretical part is devoted to the importance of insurance products, the pricing of insurance products, distribution channels of insurance products, on-line comparators and insurance products that can be arranged on them. Practical part deals with depth analysis of clients' awareness, behaviour and needs on the market of on-line comparators and the experience of clients with comparators, evaluates priorities, why they arrange insurance in this way and maps benefits to clients. The results of the questionnaire survey are formulated in the hypothesis evaluation. The conclusion summarizes its own findings and evaluation

Additional file 1 of Cross-validation of correlation networks using modular structure

Additional file 1. Gene expression data retrieved from the Sequence Read Archive, https://www.ncbi.nlm.nih.gov/sra

Additional file 9 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Figure S6. True false discovery rate for p-values adjusted using Storey q-values method at an estimated false discovery rate of 0.05 (y-axis) for different distribution of effects between groups (x-axis): balanced (‘B’) with 10% of effects divided equally between the two groups, lightly-unbalanced (‘LU’) with effects added 75–25% in each group, unbalanced (‘U’) with all effects added to only one group, and heavily-unbalanced (‘HU’) with 20% of effects added to only one group. The results were based on resampled data consisting of two groups with 10 samples in each, and an average fold-change of 3. Three metagenomic datasets were used Human gut I, Human gut II and Marine. The following methods are included in the figure trimmed mean of M-values (TMM), relative log expression (RLE), cumulative sum scaling (CSS), reversed cumulative sum scaling (RCSS), quantile-quantile (Quant), upper quartile (UQ), median (Med), total count (TC) and rarefying (Rare). (PDF 132 kb

Additional file 8 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Figure S5. True false discovery rate for p-values adjusted using Benjamini-Yekutieli method at an estimated false discovery rate of 0.05 (y-axis) for different distribution of effects between groups (x-axis): balanced (‘B’) with 10% of effects divided equally between the two groups, lightly-unbalanced (’LU’) with effects added 75%-25% in each group, unbalanced (‘U’) with all effects added to only one group, and heavily-unbalanced (’HU’) with 20% of effects added to only one group. The results were based on resampled data consisting of two groups with 10 samples in each, and an average fold-change of 3. Three metagenomic datasets were used Human gut I, Human gut II and Marine. The following methods are included in the figure trimmed mean of M-values (TMM), relative log expression (RLE), cumulative sum scaling (CSS), reversed cumulative sum scaling (RCSS), quantile-quantile (Quant), upper quartile (UQ), median (Med), total count (TC) and rarefying (Rare). (PDF 40 kb

Additional file 1 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Figure S1. Histograms of Spearman correlations between normalization factors and raw counts of non-differentially abundant genes (non-DAGs). Spearman correlations were compute per gene in the Human gut I, for group size 10+10, with 10% of effects divided equally between the two group, and fold-change 3. Affected genes were randomly selected in 100 iterations. The following methods are included in the figure trimmed mean of M-values (TMM), relative log expression (RLE), cumulative sum scaling (CSS), reversed cumulative sum scaling (RCSS), upper quartile (UQ), median (Med) and total count (TC). (PDF 76 kb

Additional file 5 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Table S2. False positive rate at a fix true positive rate of 0.50 for a group size of 10+10. (PDF 16 kb

Additional file 4 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Table S1. True positive rate at a fixed false positive rate of 0.01 for a group size of 10+10. (PDF 16 kb

Additional file 2 of Comparison of normalization methods for the analysis of metagenomic gene abundance data

Figure S2. Scatterplot of normalization factors for each pair of scaling methods. Normalization factors estimated per sample in the Human gut I, for group size 10+10, with 10% of effects divided equally between the two group, and fold-change 3. Affected genes were randomly selected in 100 iterations. The number on the top-left of each plot indicates the Spearman correlation for the normalization factors presented in the plot. The following methods are included in the figure trimmed mean of M-values (TMM), relative log expression (RLE), cumulative sum scaling (CSS), reversed cumulative sum scaling (RCSS), upper quartile (UQ), median (Med) and total count (TC). (PDF 316 kb