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"The limits of my language are the limits of my mind. All I know is what I have words for" (Wittgenstein). When learning something completely new, we connect the unknown term to an already existing part of our knowledge. We can only build new ideas and insights upon an existing conceptual foundation. In the field of statistics, we educators frequently find ourselves met with great confusion when teaching novices. These students, entirely unfamiliar with even basic statistics, must connect the introduced statistical terms within their personal existing networks of largely non-statistical knowledge. Lecturers, on the other hand, who are well versed in statistics, have deeply internalized the content to be taught and its relevant context. The juxtaposition of the two roles may produce amusement in a lecturer upon gaining insight into the word associations made by the statistical novices. For example, a ‘logistic regression’ does not involve the ‘shipping of goods in economically difficult times,’ though this might seem entirely reasonable and intuitive to the statistics learner. Other times, these different perspectives can lead to headaches and frustration for both learners and their lecturers. In this article, we illustrate how simple statistical terms are initially connected to a student’s pre-exiting knowledge and how these associations change after completing an introductory course in applied statistics. Furthermore, we emphasize the important difference between “term”, “approach”, and “context”. Understanding this fundamental distinction may help improve the communication between the lecturer and the learner. We offer a collection of practical tools for instructors to help promote students’ conceptual understanding in a supportive, mutually-beneficial learning environment.
Background
Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are frequent and serious complications after surgery. We aim to investigate the association between genetic variants in cholinergic candidate genes according to the Kyoto encyclopedia of genes and genomes - pathway: cholinergic neurotransmission with the development of POD or POCD in elderly patients.
Methods
This analysis is part of the European BioCog project (www.biocog.eu), a prospective multicenter observational study with elderly surgical patients. Patients with a Mini-Mental-State-Examination score ≤ 23 points were excluded. POD was assessed up to seven days after surgery using the Nursing Delirium Screening Scale, Confusion Assessment Method and a patient chart review. POCD was assessed three months after surgery with a neuropsychological test battery. Genotyping was performed on the Illumina Infinium Global Screening Array. Associations with POD and POCD were analyzed using logistic regression analysis, adjusted for age, comorbidities and duration of anesthesia (for POCD analysis additionally for education). Odds ratios (OR) refer to minor allele counts (0, 1, 2).
Results
745 patients could be included in the POD analysis, and 452 in the POCD analysis. The rate of POD within this group was 20.8% (155 patients), and the rate of POCD was 10.2% (46 patients). In a candidate gene approach three genetic variants of the cholinergic genes CHRM2 and CHRM4 were associated with POD (OR [95% confidence interval], rs8191992: 0.61[0.46; 0.80]; rs8191992: 1.60[1.22; 2.09]; rs2067482: 1.64[1.10; 2.44]). No associations were found for POCD.
Conclusions
We found an association between genetic variants of CHRM2 and CHRM4 and POD. Further studies are needed to investigate whether disturbances in acetylcholine release and synaptic plasticity are involved in the development of POD.
Background
In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.
Results
We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.
Conclusions
The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.
Background
To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.
Results
In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study.
Conclusion
Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.
During gestation, the most drastic change in oxygen supply occurs with the onset of ventilation after birth. As the too early exposure of premature infants to high arterial oxygen pressure leads to characteristic diseases, we studied the adaptation of the oxygen sensing system and its targets, the hypoxia-inducible factor- (HIF-) regulated genes (HRGs) in the developing lung. We draw a detailed picture of the oxygen sensing system by integrating information from qPCR, immunoblotting, in situ hybridization, and single-cell RNA sequencing data in ex vivo and in vivo models. HIF1α protein was completely destabilized with the onset of pulmonary ventilation, but did not coincide with expression changes in bona fide HRGs. We observed a modified composition of the HIF-PHD system from intrauterine to neonatal phases: Phd3 was significantly decreased, while Hif2a showed a strong increase and the Hif3a isoform Ipas exclusively peaked at P0. Colocalization studies point to the Hif1a-Phd1 axis as the main regulator of the HIF-PHD system in mouse lung development, complemented by the Hif3a-Phd3 axis during gestation. Hif3a isoform expression showed a stepwise adaptation during the periods of saccular and alveolar differentiation. With a strong hypoxic stimulus, lung ex vivo organ cultures displayed a functioning HIF system at every developmental stage. Approaches with systemic hypoxia or roxadustat treatment revealed only a limited in vivo response of HRGs. Understanding the interplay of the oxygen sensing system components during the transition from saccular to alveolar phases of lung development might help to counteract prematurity-associated diseases like bronchopulmonary dysplasia.