Results for the first real data example.

<p>Multiplicity-adjusted <i>p</i>-values of the tests for relative effects for the loci selected at the screening stage based on the asymptotic <i>χ</i>² multiple test (<i>χ</i>²) and the multiple permutation test (Perm) in combination with the closure principle. The multiplicity-adjusted <i>p</i>-value for locus ℓ denotes the smallest significance level such that </p><p></p><p></p><p><mi>H</mi><mo>ℓ</mo><mo>′</mo></p><p></p><p></p> is rejected for the actually observed data. The permutation test was carried out as a Monte Carlo permutation test employing 9,999 randomly chosen permutations of {1,…,<i>N</i>}, together with the identity permutation.<p></p><p>Results for the first real data example.</p

Type I error for the global hypothesis, large sample sizes.

<p>Monte Carlo simulation results, based on <i>K</i> = 10,000 repetitions, regarding the type I error rate for testing the global hypothesis in the large sample size regime (<i>n</i>_<i>A</i> = 100,<i>n</i>_<i>B</i> = 150) for the asymptotic <i>χ</i>²-based test (<i>χ</i>²) and the permutation test (Perm). The data have been generated according to Model 1 with correlation parameter <i>ρ</i>. The nominal significance level was set to <i>α</i> = 5% in all simulations. The permutation test was carried out as a Monte Carlo permutation test employing 9,999 randomly chosen permutations of {1,…,<i>N</i>}, together with the identity permutation.</p><p>Type I error for the global hypothesis, large sample sizes.</p

Results for the second real data example.

<p>Multiplicity-adjusted <i>p</i>-values of the tests for relative effects with respect to disease groups for three different immune-relevant parameters based on the asymptotic <i>χ</i>² multiple test (<i>χ</i>²) and the multiple permutation test (Perm) in combination with the closure principle. The multiplicity-adjusted <i>p</i>-value for parameter ℓ denotes the smallest significance level such that </p><p></p><p></p><p><mi>H</mi><mo>ℓ</mo><mo>′</mo></p><p></p><p></p> is rejected for the actually observed data. The permutation test was carried out as a Monte Carlo permutation test employing 9,999 randomly chosen permutations of {1,…,<i>N</i>}, together with the identity permutation. Treg: number of regulatory T-cells, tTL: total number of T-cells, immunoCRIT: cellular ratio of immune tolerance<p></p><p>Results for the second real data example.</p

Simultaneous Statistical Inference for Epigenetic Data

<div><p>Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.</p></div

Empirical family-wise error rates.

<p>Monte Carlo simulation results, based on <i>K</i> = 5,000 repetitions, regarding the FWER for the asymptotic <i>χ</i>²-based multiple test (<i>χ</i>²) and the multiple permutation test (Perm). The data have been generated according to Model 1 with correlation parameter <i>ρ</i> and <i>d</i> = 5. The nominal FWER level was set to <i>α</i> = 5% in all simulations. The permutation test was carried out as a Monte Carlo permutation test employing 9,999 randomly chosen permutations of {1,…,<i>N</i>}, together with the identity permutation.</p><p>Empirical family-wise error rates.</p

Correlation between FOXP3 demethylation (%) and cytokine production in CBMC.

<p>n = 60. Data were analyzed with Spearman Rank Order Correlation. r = correlation coefficient.</p

Percentage of FOXP3⁺ T-cells in unstimulated CD4⁺CD25^hi T-cells.

<p>Flow cytometry of unstimulated CD4⁺CD25⁺ cells as described in methods. <b>A</b>. R2 = CD4⁺CD25^hi T-cell gate. <b>B</b>. Out of CD4⁺CD25^hi T-cells, 98.1% were FOXP3⁺ T-cells.</p

Correlation between demethylation of FOXP3 and unstimulated and LpA-activated FOXP3 mRNA expression.

<p>Demethylation (%) of FOXP3 in whole cord blood was measured with real-time PCR. FOXP3 mRNA expression was measured with real-time RT-PCR in unstimulated (A) and LpA-stimulated CBMCs (B), which were shown as delta ct to 18S. Lower delta ct represents higher mRNA expression. N = 48; correlation was analyzed with Pearsons correlation coefficient.</p

FOXP3 demethylation in isolated CD4CD25⁻ and CD4CD25^hi cells.

<p>A/B. CD4CD25⁻ and CD4CD25^hi cells were isolated with Dako MoFlow. DNA was extracted and demethylation (%) of FOXP3 was measured with real-time PCR. C. Intracellular FOXP3 protein was measured in CD4CD25^hi cells as described in methods. N = 5, mean ± SEM were shown in B, one representative sample for A/C.</p

Human Regulatory T Cells of G-CSF Mobilized Allogeneic Stem Cell Donors Qualify for Clinical Application

<div><p>Recent clinical studies demonstrate the high potency of regulatory T cells (Tregs) to control graft-versus-host disease in hematopoietic stem cell transplantation (SCT). However, the adoptive transfer of Tregs is limited by their low frequency in unstimulated donors and considerable concerns that G-CSF induced SC mobilization might have negative effects on the stability and function of Tregs. The isolation of Tregs from the G-CSF mobilized SC grafts would extend this novel strategy for tolerance induction to the unrelated setting and simplify global clinical application. We characterized CD4⁺CD25^highCD127⁻ Tregs from SC donors before and after G-CSF mobilization for their phenotype, function, and stability. After G-CSF application the Treg cell yield increased significantly. Donor Tregs retained their cytokine profile, phenotypic characteristics and <em>in vitro</em> expansion capacity after SC mobilization. Most importantly, <em>in vivo</em> G-CSF stimulated Tregs remained highly suppressive on the proliferation of effector T cells, also after <em>in vitro</em> expansion, and displayed a stable phenotype in epigenetic studies. The surface expression of CXCR3 is transiently reduced. However, donor-derived Tregs maintain their migratory properties after G-CSF stimulation. Therefore, the adoptive transfer of Tregs from G-CSF mobilized SC donors seems to be a feasible and safe strategy for clinical application in allogeneic SCT.</p> </div