Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY : This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/ .

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease.

BACKGROUND: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. METHODS: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. RESULTS: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. CONCLUSIONS: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

NetSHy: network summarization via a hybrid approach leveraging topological properties.

MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

Sexually dimorphic eye size in dragonfishes, a response to a bioluminescent signalling gap.

Deep-sea fishes must overcome extremely large nearest-neighbour distances and darkness to find mates. Sexual dimorphism in the size of luminescent structures in many deep-sea taxa, including dragonfishes (family Stomiidae), indicates reproductive behaviours may be mediated by visual signalling. This presents a paradox: if male photophores are larger, females may find males at shorter distances than males find females. Solutions to this gap may include females closing this gap or by males gathering more photons with a larger eye. We examine the eye size of two species of dragonfishes (Malacosteus niger and Phostomias guernei) for sexual dimorphism and employ a model of detection distance to evaluate the potential for such dimorphism to bridge the detection gap. This model incorporates the flux of sexually dimorphic postorbital photophores and eye lens size to predict detection distances. In both species, we found a significant visual detection gap in which females find males before males find females and that male lens size is larger, marking the second known case of size dimorphism in the actinopterygian visual system. Our results indicate the larger eye affords males a significant improvement in detection distance. We conclude that this dimorphic phenotype may have evolved to close the detection gap.

Impact of surgical lung biopsy on lung function and survival in patients with idiopathic pulmonary fibrosis in a multi-centre registry cohort.

BACKGROUND AND OBJECTIVE: Establishing an accurate and timely diagnosis of idiopathic pulmonary fibrosis (IPF) is essential for appropriate management and prognostication. In some cases, surgical lung biopsy (SLB) is performed but carries non-negligible risk. The objective of this retrospective study was to determine if SLB is associated with accelerated lung function decline in patients with IPF using the Canadian Registry for Pulmonary Fibrosis. METHODS: Linear mixed models and Cox proportional hazards regression models were used to compare decline in forced vital capacity (FVC)%, diffusion capacity of the lung (DLCO%) and risk of death or lung transplantation between SLB and non-SLB patients. Adjustments were made for baseline age, sex, smoking history, antifibrotic use, and lung function. A similar analysis compared lung function changes 12 months pre- and post-SLB. RESULTS: A total of 81 SLB patients and 468 non-SLB patients were included. In the SLB group, the post-biopsy annual FVC% decline was 2.0% (±0.8) in unadjusted, and 2.1% (±0.8) in adjusted models. There was no difference in FVC% decline, DLCO% decline, or time to death or lung transplantation between the two groups, in adjusted or unadjusted models (all p-values >0.07). In the pre-post SLB group, no differences were identified in FVC% decline in unadjusted or adjusted models (p = 0.07 for both). CONCLUSION: No association between SLB and lung function decline or risk of death or lung transplantation was identified in this multi-centre study of patients with IPF.

Expanding Kane's argument-based validity framework: What can validation practices in language assessment offer health professions education?

CONTEXT: One central consideration in health professions education (HPE) is to ensure we are making sound and justifiable decisions based on the assessment instruments we use on health professionals. To achieve this goal, HPE assessment researchers have drawn on Kane's argument-based framework to ascertain the validity of their assessment tools. However, the original four-inference model proposed by Kane - frequently used in HPE validation research - has its limitations in terms of what each inference entails and what claims and sources of backing are housed in each inference. The under-specification in the four-inference model has led to inconsistent practices in HPE validation research, posing challenges for (i) researchers who want to evaluate the validity of different HPE assessment tools and/or (ii) researchers who are new to test validation and need to establish a coherent understanding of argument-based validation. METHODS: To address these identified concerns, this article introduces the expanded seven-inference argument-based validation framework that is established practice in the field of language testing and assessment (LTA). We explicate (i) why LTA researchers experienced the need to further specify the original four Kanean inferences; (ii) how LTA validation research defines each of their seven inferences and (iii) what claims, assumptions and sources of backing are associated with each inference. Sampling six representative validation studies in HPE, we demonstrate why an expanded model and a shared disciplinary validation framework can facilitate the examination of the validity evidence in diverse HPE validation contexts. CONCLUSIONS: We invite HPE validation researchers to experiment with the seven-inference argument-based framework from LTA to evaluate its usefulness to HPE. We also call for greater interdisciplinary dialogue between HPE and LTA since both disciplines share many fundamental concerns about language use, communication skills, assessment practices and validity in assessment instruments.

Pachyonychia congenita: A father and son with a novel variant in the KRT16 gene.

This study underscores the significance of identifying the clinical manifestations of pachyonychia congenita (PC) and emphasizes the patterns of genetic inheritance. A 12-month-old boy presented with a "white hairy tongue" and, following a comprehensive evaluation, was diagnosed with PC. His father exhibited similar symptoms. Genetic testing revealed a KRT16 pathogenic variant (c.616 T > G) in both the patient and his father, marking it as a novel variant in the PC literature. This case contributes to a broader understanding of PC's genetic diversity and its clinical presentations.

Outbreak of Sexually Transmitted Nongroupable Neisseria meningitidis-Associated Urethritis, Vietnam.

We report on an outbreak of nongroupable Neisseria meningitidis-associated urethritis, primarily among men who have sex with men in southern Vietnam. Nearly 50% of N. meningitidis isolates were resistant to ciprofloxacin. This emerging pathogen should be considered in the differential diagnosis and management of urethritis.

Shifting-corrected regularized regression for 1H NMR metabolomics identification and quantification.

The process of identifying and quantifying metabolites in complex mixtures plays a critical role in metabolomics studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies and quantifies metabolites in a mixture automatically. A detailed algorithm is also proposed to implement the proposed method. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification and quantification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.

SPF: A spatial and functional data analytic approach to cell imaging data.

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

Leveraging the HMBC to Facilitate Metabolite Identification.

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

New onset of psoriasis following COVID-19 vaccination.

The cutaneous side effects of COVID-19 vaccines are being studied and their immunogenicity is most likely linked to the pathophysiology of psoriasis. Although uncommon, several cases of exacerbation and new onset of psoriasis have been reported globally after vaccination. To contribute to the literature on this intriguing topic, we present three cases of de novo psoriasis in adult patients following COVID-19 vaccination. Our observations and a literature review show that this occurrence is independent of the type and brand of vaccines.

Prevalence and burden of dengue infection in Europe: A systematic review and meta-analysis.

Imported dengue cases are thought to be important source for transmission of autochthonous dengue in Europe. We aimed to investigate the prevalence of dengue in Europe, its severity, and factors associated with it. Out of 5287 reports resulting from the search of nine electronic search engines, we included 174 reports. Meta-analysis was performed by pooling the event rate and 95% confidence interval (CI). Subgroup meta-analyses were performed to test the effect of the covariates. Among 20 284 reported cases, 130 autochthonous dengue cases were reported in eight countries with the highest number of cases reported in Israel (n = 41). The highest number of imported dengue cases was in Germany (n = 6638) then France (n = 6610). Most cases were imported from Southeast Asia (n = 2533) especially Thailand. Dengue infection cases increased with time, with 4157 cases reported in 2010. Second dengue infection and dengue serotype 2 were positively associated with dengue severity. The proportion of autochthonous dengue infection increased with time to reach 14.8% (95% CI, 7.6-26.9) in 2015. The pooled proportion of severe dengue was 6.18% (95% CI, 2.7-13.3). The United Kingdom and France had the highest rate of severe dengue (25%; 95% CI, 6.3-62.3, and 21.4%; 95% CI, 24.5-18.7, respectively). This change may be due to the surveillance efforts instead of true biological phenomenon; thus, the lack of surveillance is an obvious limitation. In conclusion, imported and autochthonous dengue has been increasing in Europe. Severe dengue began to increase recently in Europe. European health authorities should pay more attention for the diagnosis and control of dengue infection among returning travelers, especially the travelers with fever of unknown origin.

Role of AmpG in the resistance to ß-lactam agents, including cephalosporins and carbapenems: candidate for a novel antimicrobial target.

BACKGROUND: A complex cascade of genes, enzymes, and transcription factors regulates AmpC ß-lactamase overexpression. We investigated the network of AmpC ß-lactamase overexpression in Klebsiella aerogenes and identified the role of AmpG in resistance to ß-lactam agents, including cephalosporins and carbapenems. METHODS: A transposon mutant library was created for carbapenem-resistant K. aerogenes YMC2008-M09-943034 (KE-Y1) to screen for candidates with increased susceptibility to carbapenems, which identified the susceptible mutant derivatives KE-Y3 and KE-Y6. All the strains were subjected to highly contiguous de novo assemblies using PacBio sequencing to investigate the loss of resistance due to transposon insertion. Complementation and knock-out experiments using lambda Red-mediated homologous recombinase and CRISPR-Cas9 were performed to confirm the role of gene of interest. RESULTS: In-depth analysis of KE-Y3 and KE-Y6 revealed the insertion of a transposon at six positions in each strain, at which truncation of the AmpG permease gene was common in both. The disruption of the AmpG permease leads to carbapenem susceptibility, which was further confirmed by complementation. We generated an AmpG permease gene knockout using lambda Red-mediated recombineering in K. aerogenes KE-Y1 and a CRISPR-Cas9-mediated gene knockout in multidrug-resistant Klebsiella pneumoniae-YMC/2013/D to confer carbapenem susceptibility. CONCLUSIONS: These findings suggest that inhibition of the AmpG is a potential strategy to increase the efficacy of ß-lactam agents against Klebsiella aerogenes.

Safety and Efficacy of Tattoo Removal Using a Dual-Wavelength 1064/532-nm Picosecond Laser in Patients With Fitzpatrick Skin Type III and IV.

BACKGROUND AND OBJECTIVES: To investigate the safety and efficacy of a dual-wavelength 1064/532-nm picosecond-domain laser for tattoo removal in Vietnamese patients. STUDY DESIGN/MATERIALS AND METHODS: This prospective clinical study enrolled 30 subjects with 52 decorative tattoos treated with up to six treatments of laser removal at intervals of 6-8 weeks. Safety and efficacy were assessed at each treatment session and at 4 weeks after the final session. A "good" response was defined as at least 75% clearance of tattoo pigments. RESULTS: A significant reduction of tattoo appearance was achieved in all subjects. 88.5% of tattoos exhibited a "good" response to treatment by the end of the six sessions and more than 36% of tattoos exhibited better than "good" responses. Adverse events were common in the early period after treatment but did not persist in most patients. Only one case of prolonged hypopigmentation was reported. CONCLUSIONS: Treatment using a 1064/532-nm picosecond laser is an effective approach for removal of decorative tattoos, which poses a minimal risk of long-term adverse events in patients with Fitzpatrick skin type III or IV. Lasers Surg. Med. © 2020 Wiley Periodicals LLC.

Nitric Oxide Synthase Inhibitors into the Clinic at Last.

The 1998 Nobel Prize in Medicine and Physiology for the discovery of nitric oxide, a nitrogen containing reactive oxygen species (also termed reactive nitrogen or reactive nitrogen/oxygen species) stirred great hopes. Clinical applications, however, have so far pertained exclusively to the downstream signaling of cGMP enhancing drugs such as phosphodiesterase inhibitors and soluble guanylate cyclase stimulators. All clinical attempts, so far, to inhibit NOS have failed even though preclinical models were strikingly positive and clinical biomarkers correlated perfectly. This rather casts doubt on our current way of target identification in drug discovery in general and our way of patient stratification based on correlating but not causal biomarkers or symptoms. The opposite, NO donors, nitrite and enhancing NO synthesis by eNOS/NOS3 recoupling in situations of NO deficiency, are rapidly declining in clinical relevance or hold promise but need yet to enter formal therapeutic guidelines, respectively. Nevertheless, NOS inhibition in situations of NO overproduction often jointly with enhanced superoxide (or hydrogen peroxide production) still holds promise, but most likely only in acute conditions such as neurotrauma (Stover et al., J Neurotrauma 31(19):1599-1606, 2014) and stroke (Kleinschnitz et al., J Cereb Blood Flow Metab 1508-1512, 2016; Casas et al., Proc Natl Acad Sci U S A 116(14):7129-7136, 2019). Conversely, in chronic conditions, long-term inhibition of NOS might be too risky because of off-target effects on eNOS/NOS3 in particular for patients with cardiovascular risks or metabolic and renal diseases. Nitric oxide synthases (NOS) and their role in health (green) and disease (red). Only neuronal/type 1 NOS (NOS1) has a high degree of clinical validation and is in late stage development for traumatic brain injury, followed by a phase II safety/efficacy trial in ischemic stroke. The pathophysiology of NOS1 (Kleinschnitz et al., J Cereb Blood Flow Metab 1508-1512, 2016) is likely to be related to parallel superoxide or hydrogen peroxide formation (Kleinschnitz et al., J Cereb Blood Flow Metab 1508-1512, 2016; Casas et al., Proc Natl Acad Sci U S A 114(46):12315-12320, 2017; Casas et al., Proc Natl Acad Sci U S A 116(14):7129-7136, 2019) leading to peroxynitrite and protein nitration, etc. Endothelial/type 3 NOS (NOS3) is considered protective only and its inhibition should be avoided. The preclinical evidence for a role of high-output inducible/type 2 NOS (NOS2) isoform in sepsis, asthma, rheumatic arthritis, etc. was high, but all clinical development trials in these indications were neutral despite target engagement being validated. This casts doubt on the role of NOS2 in humans in health and disease (hence the neutral, black coloring).

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data.

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Comparing normalization methods and the impact of noise.

INTRODUCTION: Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways. OBJECTIVE: Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets. METHODS: Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and random dilution factors. Methods were evaluated based on an ability to recover the intensities of the true spectral peaks and the reproducibility of true classifying features from orthogonal projections to latent structures-discriminant analysis model (OPLS-DA). RESULTS: Most normalization methods (except histogram matching) performed equally well at modest levels of signal variance. Only probabilistic quotient (PQ) and constant sum (CS) maintained the highest level of peak recovery (> 67%) and correlation with true loadings (> 0.6) at maximal noise. CONCLUSION: PQ and CS performed the best at recovering peak intensities and reproducing the true classifying features for an OPLS-DA model regardless of spectral noise level. Our findings suggest that performance is largely determined by the level of noise in the dataset, while the effect of dilution factors was negligible. A minimal allowable noise level of 20% was also identified for a valid NMR metabolomics dataset.