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Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold 𝜀
) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p < 𝜀
). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.
Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans)
(2022)
Background: Data transformations are commonly used in bioinformatics data processing in the context of data projection and clustering. The most used Euclidean metric is not scale invariant and therefore occasionally inappropriate for complex, e.g., multimodal distributed variables and may negatively affect the results of cluster analysis. Specifically, the squaring function in the definition of the Euclidean distance as the square root of the sum of squared differences between data points has the consequence that the value 1 implicitly defines a limit for distances within clusters versus distances between (inter-) clusters.
Methods: The Euclidean distances within a standard normal distribution (N(0,1)) follow a N(0,2–√) distribution. The EDO-transformation of a variable X is proposed as EDO=X/(2–√⋅s) following modeling of the standard deviation s by a mixture of Gaussians and selecting the dominant modes via item categorization. The method was compared in artificial and biomedical datasets with clustering of untransformed data, z-transformed data, and the recently proposed pooled variable scaling.
Results: A simulation study and applications to known real data examples showed that the proposed EDO scaling method is generally useful. The clustering results in terms of cluster accuracy, adjusted Rand index and Dunn’s index outperformed the classical alternatives. Finally, the EDO transformation was applied to cluster a high-dimensional genomic dataset consisting of gene expression data for multiple samples of breast cancer tissues, and the proposed approach gave better results than classical methods and was compared with pooled variable scaling.
Conclusions: For multivariate procedures of data analysis, it is proposed to use the EDO transformation as a better alternative to the established z-standardization, especially for nontrivially distributed data. The “EDOtrans” R package is available at https://cran.r-project.org/package=EDOtrans.
Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments.
Background and Aims: Chronic infection with the hepatitis B virus (HBV) is a major health issue worldwide. Recently, single nucleotide polymorphisms (SNPs) within the human leukocyte antigen (HLA)-DP locus were identified to be associated with HBV infection in Asian populations. Most significant associations were observed for the A alleles of HLA-DPA1 rs3077 and HLA-DPB1 rs9277535, which conferred a decreased risk for HBV infection. We assessed the implications of these variants for HBV infection in Caucasians.
Methods: Two HLA-DP gene variants (rs3077 and rs9277535) were analyzed for associations with persistent HBV infection and with different clinical outcomes, i.e., inactive HBsAg carrier status versus progressive chronic HBV (CHB) infection in Caucasian patients (n = 201) and HBsAg negative controls (n = 235).
Results: The HLA-DPA1 rs3077 C allele was significantly associated with HBV infection (odds ratio, OR = 5.1, 95% confidence interval, CI: 1.9–13.7; p = 0.00093). However, no significant association was seen for rs3077 with progressive CHB infection versus inactive HBsAg carrier status (OR = 2.7, 95% CI: 0.6–11.1; p = 0.31). In contrast, HLA-DPB1 rs9277535 was not associated with HBV infection in Caucasians (OR = 0.8, 95% CI: 0.4–1.9; p = 1).
Conclusions: A highly significant association of HLA-DPA1 rs3077 with HBV infection was observed in Caucasians. However, as a differentiation between different clinical courses of HBV infection was not possible, knowledge of the HLA-DPA1 genotype cannot be translated into personalized anti-HBV therapy approaches.
Inverted perceptual judgment of nociceptive stimuli at threshold level following inconsistent cues
(2015)
Objective: The perception of pain is susceptible to modulation by psychological and contextual factors. It has been shown that subjects judge noxious stimuli as more painful in a respective suggestive context, which disappears when the modifying context is resolved. However, a context in which subjects judge the painfulness of a nociceptive stimulus in exactly the opposite direction to that of the cues has never been shown so far.
Methods: Nociceptive stimuli (300 ms intranasal gaseous CO2) at the individual pain threshold level were applied after a visual cue announcing the stimulus as either "no pain", merely a "stimulus", or "pain". Among the stimuli at threshold level, other CO2 stimuli that were clearly below or above pain threshold were randomly interspersed. These were announced beforehand in 12 subjects randomly with correct or incorrect cues, i.e., clearly painful or clearly non-painful stimuli were announced equally often as not painful or painful. By contrast, in a subsequent group of another 12 subjects, the stimuli were always announced correctly with respect to the evoked pain.
Results: The random and often incorrect announcement of stimuli clearly below or above pain threshold caused the subjects to rate the stimuli at pain-threshold level in the opposite direction of the cue, i.e., when the stimuli were announced as "pain" significantly more often than as non-painful and vice versa (p < 10-4). By contrast, in the absence of incongruence between announcement and perception of the far-from-threshold stimuli, stimuli at pain threshold were rated in the cued direction.
Conclusions: The present study revealed the induction of associations incongruent with a given message in the perception of pain. We created a context of unreliable cues whereby subjects perceived the stimulus opposite to that suggested by a prior cue, i.e., potentially nociceptive stimuli at pain threshold level that were announced as painful were judged as non-painful and vice versa. These findings are consistent with reported data on the effects of distrust on non-painful cognitive responses.
Background: Cannabis proofed to be effective in pain relief, but one major side effect is its influence on memory in humans. Therefore, the role of memory on central processing of nociceptive information was investigated in healthy volunteers.
Methods: In a placebo-controlled cross-over study including 22 healthy subjects, the effect of 20 mg oral Δ9-tetrahydrocannabinol (THC) on memory involving nociceptive sensations was studied, using a delayed stimulus discrimination task (DSDT). To control for nociceptive specificity, a similar DSDT-based study was performed in a subgroup of thirteen subjects, using visual stimuli.
Results: For each nociceptive stimulus pair, the second stimulus was associated with stronger and more extended brain activations than the first stimulus. These differences disappeared after THC administration. The THC effects were mainly located in two clusters comprising the insula and inferior frontal cortex in the right hemisphere, and the caudate nucleus and putamen bilaterally. These cerebral effects were accompanied in the DSDT by a significant reduction of correct ratings from 41.61% to 37.05% after THC administration (rm-ANOVA interaction "drug" by "measurement": F (1,21) = 4.685, p = 0.042). Rating performance was also reduced for the visual DSDT (69.87% to 54.35%; rm-ANOVA interaction of "drug" by "measurement": F (1,12) = 13.478, p = 0.003) and reflected in a reduction of stimulus-related brain deactivations in the bilateral angular gyrus.
Conclusions: Results suggest that part of the effect of THC on pain may be related to memory effects. THC reduced the performance in DSDT of nociceptive and visual stimuli, which was accompanied by significant effects on brain activations. However, a pain specificity of these effects cannot be deduced from the data presented.