Generalizing Boltzmann Configurational Entropy to Surfaces, Point Patterns and Landscape Mosaics

Several methods have been recently proposed to calculate configurational entropy, based on Boltzmann entropy. Some of these methods appear to be fully thermodynamically consistent in their application to landscape patch mosaics, but none have been shown to be fully generalizable to all kinds of landscape patterns, such as point patterns, surfaces, and patch mosaics. The goal of this paper is to evaluate if the direct application of the Boltzmann relation is fully generalizable to surfaces, point patterns, and landscape mosaics. I simulated surfaces and point patterns with a fractal neutral model to control their degree of aggregation. I used spatial permutation analysis to produce distributions of microstates and fit functions to predict the distributions of microstates and the shape of the entropy function. The results confirmed that the direct application of the Boltzmann relation is generalizable across surfaces, point patterns, and landscape mosaics, providing a useful general approach to calculating landscape entropy.

A large-sample crisis? Exaggerated false positives by popular differential expression methods

We report a surprising phenomenon about identifying differentially expressed genes (DEGs) from population-level RNA-seq data: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates (FDRs). Via permutation analysis on an immunotherapy RNA-seq dataset, we observed that DESeq2 and edgeR identified even more DEGs after samples’ condition labels were randomly permuted. Motivated by this, we evaluated six DEG identification methods (DESeq2, edgeR, limma-voom, NOISeq, dearseq, and the Wilcoxon rank-sum test) on population-level RNA-seq datasets. We found that the FDR control was often failed by the three popular parametric methods—DESeq2, edgeR, and limma-voom— and the new non-parametric method dearseq. In particular, the actual FDRs of DESeq2 and edgeR sometimes exceeded 20% when the target FDR threshold was only 5%. Although NOISeq, a non-parametric method used by GTEx, controlled the FDR better than the other four methods did, its power was much lower than that of the Wilcoxon rank-sum test, a classic nonparametric test that consistently controlled the FDR and achieved good power in our evaluation. Based on these results, for population-level RNA-seq studies, we recommend the Wilcoxon rank-sum test.

Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy

Cross-frequency phase–amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.

Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the “target function” for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences.Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the “function” that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using “f-SPoC” to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex “target functions” like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

Self-reported taste and smell alterations and the liking of oral nutritional supplements with sensory-adapted flavors in cancer patients receiving systemic antitumor treatment

Purpose Taste and smell alterations (TAs and SAs) are often reported by patients with cancer receiving systemic antitumor therapy and can negatively impact food intake and quality of life. This study aimed to examine the occurrence of TAs and SAs and investigate the impact of TAs on overall liking of oral nutritional supplements (ONS) with warming and cooling sensations. Methods Patients receiving systemic antitumor therapy completed a questionnaire on sensory alterations and evaluated overall liking of 5 prototype flavors of Nutridrink® Compact Protein (hot tropical ginger (HTG), hot mango (HM), cool red fruits (CRF), cool lemon (CL), and neutral (N)) on a 10-point scale via a sip test. Differences between patients with and without TAs were investigated using permutation analysis. Results Fifty patients with various cancer types and treatments were included. Thirty patients (60%) reported TAs and 13 (26%) experienced SAs. Three flavors were rated highly with a liking score > 6 (CRF 6.8 ± 1.7; N 6.5 ± 1.9; HTG 6.0 ± 2.0). Larger variation in ONS liking scores was observed in patients with TAs with or without SAs (4.5–6.9 and 4.6–7.2, respectively) vs. patients without TAs (5.9–6.5). TAs were associated with increased liking of CRF (Δ = + 0.9) and N (Δ = + 1.0) flavors. Conclusions TAs and SAs are common in patients with cancer undergoing systemic antitumor therapy. Patients with TAs were more discriminant in liking of ONS flavors compared to patients without TAs, and sensory-adapted flavors appeared to be appreciated. The presence of TAs should be considered when developing or selecting ONS for patients with cancer. Trial registration Registration at ClinicalTrials.gov (NCT03525236) on 26 April 2018.

The effect of statistical normalisation on network propagation scores

Motivation Network diffusion and label propagation are fundamental tools in computational biology, with applications like gene-disease association, protein function prediction and module discovery. More recently, several publications have introduced a permutation analysis after the propagation process, due to concerns that network topology can bias diffusion scores. This opens the question of the statistical properties and the presence of bias of such diffusion processes in each of its applications. In this work, we characterised some common null models behind the permutation analysis and the statistical properties of the diffusion scores. We benchmarked seven diffusion scores on three case studies: synthetic signals on a yeast interactome, simulated differential gene expression on a protein-protein interaction network and prospective gene set prediction on another interaction network. For clarity, all the datasets were based on binary labels, but we also present theoretical results for quantitative labels. Results Diffusion scores starting from binary labels were affected by the label codification, and exhibited a problem-dependent topological bias that could be removed by the statistical normalisation. Parametric and non-parametric normalisation addressed both points by being codification-independent and by equalising the bias. We identified and quantified two sources of bias -mean value and variance- that yielded performance differences when normalising the scores. We provided closed formulae for both and showed how the null covariance is related to the spectral properties of the graph. Despite none of the proposed scores systematically outperformed the others, normalisation was preferred when the sought positive labels were not aligned with the bias. We conclude that the decision on bias removal should be problem and data-driven, i.e. based on a quantitative analysis of the bias and its relation to the positive entities. Availability The code is publicly available at https://github.com/b2slab/diffuBench Supplementary information Supplementary data are available at Bioinformatics online.

798. Metronidazole Exposure Prior to Clostridiodes difficile Infection (CDI) is a Risk Factor for Severe C. difficile Disease in Cancer Patients

Background Antibiotic use is a risk factor for CDI. Few studies have correlated use of prior antibiotics with CDI severity in cancer patients. This study identified clinical and microbiology risk factors associated with severe CDI in patients with cancer. We hypothesized that previous antibiotic exposure and microbiome composition at time of CDI presentation, are risk factors for severe disease in cancer patients. Methods This non-interventional, prospective, single-center cohort study examined patients with cancer who had their first episode or first recurrence of CDI between Oct 27, 2016 and Jul 1, 2019. C. difficile was identified using nucleic acid amplification testing. Multivariate analysis was used to determine significant clinical risk factors for severe CDI as defined in the 2018 IDSA/SHEA guidelines. Alpha, and beta diversities were calculated to measure the average species diversity and the overall microbial composition. Differential abundance analysis and progressive permutation analysis were used to single out the significant microbial features that differed across CDI severity levels. Results Patient (n=200) demographics show mean age of 60 yrs., 53% female, majority White (76%) and non-Hispanic (85%). Prior 90 day metronidazole use (Odds Ratio OR 4.68 [1.47-14.91] p0.009) was a significant risk factor for severe CDI. Other factors included Horn’s Index > 2 (OR 7.75 [1.05-57.35] p0.045), Leukocytosis (OR 1.29 [1.16-1.43] p< 0.001), Neutropenia (OR 6.01 [1.34-26.89] p0.019) and Serum Creatinine >0.95 mg/dL (OR 25.30 [8.08-79.17] p< 0.001). Overall, there were no significant differences in alpha and beta diversity between severity levels. However, when identifying individual microbial features, the high presence of Bacteroides uniformis, Ruminococceae, Citrobacter koseri and Salmonella were associated with protection from severe CDI (p< 0.05). Table 1 - Results of multivariate logistic regression analysis of factors associated with severe CDI Figure 1. Microbiome features identified by progressive permutation analysis as seen in a volcano plot. Conclusion A number of risk factors for severe CDI were identified among this population, including prior 90 day metronidazole use. Also, increased relative abundance of Bacteroides uniformis, Ruminococceae, Citrobacter koseri and Salmonella were linked to protection from severe CDI. Reducing metronidazole use in patients with cancer may help prevent subsequent severe CDI. Disclosures Adilene Olvera, MPH MLS (ASCP), MERK (Grant/Research Support, Scientific Research Study Investigator) Kevin W. Garey, PharmD, MS, FASHP, Merck & Co. (Grant/Research Support, Scientific Research Study Investigator) Ryan J. Dillon, MSc, Merck & Co., Inc., (Employee) Engels N. Obi, PhD, Merck & Co. (Employee)

The Heritability of Cortical Folding: Evidence from the Human Connectome Project

The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as cell signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms and do not account for the function of a protein in a given phosphorylation state. Thus, analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. Here, we propose a method that combines shotgun phosphoproteomics data, protein-protein interactions and functional annotations from ontologies or pathway databases into a heterogeneous multilayer network. Phosphorylation sites are then associated to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach using a dataset modelling the MAPK/ERK pathway and were able to associate differentially regulated sites on the same protein to their previously described functions. Random permutation analysis proved that these associations were not random and were determined by the network topology. We then applied the RWHN algorithm to two previously published datasets; the algorithm was able to reproduce the experimentally validated conclusions from the publications, and associate phosphorylation sites with both new and known functions based on their regulatory patterns. The approach described here provides a robust, phosphorylation site-centric method to analyzing phosphoproteomics data and identifying potential context-specific functions for sites with similar phosphorylation profiles.