Phototherapy Alters the Plasma Metabolite Profile in Infants Born Preterm with Hyperbilirubinemia.

OBJECTIVE: To investigate the effects of gestational age (GA) and phototherapy on the plasma metabolite profile of preterm infants with neonatal hyperbilirubinemia (NHB). STUDY DESIGN: From a cohort of prospectively enrolled infants born preterm (N=92), plasma samples of very preterm (VPT; GA, 28+0 to 31+6 weeks, N =27) and moderate/late preterm (M/LPT; GA, 32+0 to 35+6 weeks, N =33) infants requiring phototherapy for NHB were collected prior to the initiation of phototherapy and 24 hours after starting phototherapy. An additional sample was collected 48 hours after starting phototherapy in a randomly selected subset (N=30; VPT N=15; M/LPT N=15). Metabolite profiles were determined using ultraperformance liquid chromatography tandem mass spectroscopy. Two-way ANCOVA was used to identify metabolites that differed between GA groups and timepoints after adjusting for total serum bilirubin (TSB) levels (FDR q-value<0.05). Top impacted pathways were identified using pathway over-representation analysis. RESULTS: Phototherapy was initiated at lower TSB (mean ± SD mg/dL) levels in VPT compared with M/LPT infants (7.3 ± 1.4 vs. 9.9 ± 1.9, p<0.01). We identified 664 metabolites that were significant for a phototherapy effect, 191 metabolites significant for GA, and 46 metabolites significant for GA x phototherapy interaction (FDR q-value<0.05). Longer duration phototherapy had a larger mean effect size (24 hours post-phototherapy: d=0.36; 48 hours post-phototherapy: d=0.43). Top pathways affected by phototherapy included membrane lipid metabolism, one-carbon metabolism, creatine biosynthesis, and oligodendrocyte differentiation. CONCLUSION: Phototherapy alters the plasma metabolite profile more than GA in preterm infants with NHB, affecting pathways related to lipid and one-carbon metabolism, energy biosynthesis, and oligodendrocyte differentiation.

Prognostic Performance of Hematological and Serum Iron and Metabolite Indices for Detection of Early Iron Deficiency Induced Metabolic Brain Dysfunction in Infant Rhesus Monkeys.

BACKGROUND: The current pediatric practice of monitoring for infantile iron deficiency (ID) via hemoglobin (Hgb) screening at one y of age does not identify preanemic ID nor protect against later neurocognitive deficits. OBJECTIVES: To identify biomarkers of iron-related metabolic alterations in the serum and brain and determine the sensitivity of conventional iron and heme indices for predicting risk of brain metabolic dysfunction using a nonhuman primate model of infantile ID. METHODS: Simultaneous serum iron and RBC indices, and serum and cerebrospinal fluid (CSF) metabolomic profiles were determined in 20 rhesus infants, comparing iron sufficient (IS; N = 10) and ID (N = 10) infants at 2 and 4 mo of age. RESULTS: Reticulocyte hemoglobin (RET-He) was lower at 2 wk in the ID group. Significant IS compared with ID differences in serum iron indices were present at 2 mo, but Hgb and RBC indices differed only at 4 mo (P < 0.05). Serum and CSF metabolomic profiles of the ID and IS groups differed at 2 and 4 mo (P < 0.05). Key metabolites, including homostachydrine and stachydrine (4-5-fold lower at 4 mo in ID group, P < 0.05), were altered in both serum and CSF. Iron indices and RET-He at 2 mo, but not Hgb or other RBC indices, were correlated with altered CSF metabolic profile at 4 mo and had comparable predictive accuracy (area under the receiver operating characteristic curve scores, 0.75-0.80). CONCLUSIONS: Preanemic ID at 2 mo was associated with metabolic alterations in serum and CSF in infant monkeys. Among the RBC indices, only RET-He predicted the future risk of abnormal CSF metabolic profile with a predictive accuracy comparable to serum iron indices. The concordance of homostachydrine and stachydrine changes in serum and CSF indicates their potential use as early biomarkers of brain metabolic dysfunction in infantile ID.

Multiple augmented reduced rank regression for pan-cancer analysis.

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types.

Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.

Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.

Intranasal insulin treatment partially corrects the altered gene expression profile in the hippocampus of developing rats with perinatal iron deficiency.

Perinatal iron deficiency (FeD) targets the hippocampus and leads to long-term cognitive deficits. Intranasal insulin administration improves cognitive deficits in adult humans with Alzheimer's disease and type 2 diabetes and could provide benefits in FeD-induced hippocampal dysfunction. To objective was to assess the effects of intranasal insulin administration intranasal insulin administration on the hippocampal transcriptome in a developing rat model of perinatal FeD. Perinatal FeD was induced using low-iron diet from gestational day 3 until postnatal day (P) 7, followed by an iron sufficient (FeS) diet through P21. Intranasal insulin was administered at a dose of 0.3 IU twice daily from P8 to P21. Hippocampi were removed on P21 from FeS control, FeD control, FeS insulin, and FeD insulin groups. Total RNA was isolated and profiled using next-generation sequencing. Gene expression profiles were characterized using custom workflows and expression patterns examined using ingenuity pathways analysis (n = 7-9 per group). Select RNAseq results were confirmed via qPCR. Transcriptomic profiling revealed that mitochondrial biogenesis and flux, oxidative phosphorylation, quantity of neurons, CREB signaling in neurons, and RICTOR-based mTOR signaling were disrupted with FeD and positively affected by intranasal insulin treatment with the most benefit observed in the FeD insulin group. Both perinatal FeD and intranasal insulin administration altered gene expression profile in the developing hippocampus. Intranasal insulin treatment reversed the adverse effects of FeD on many molecular pathways and could be explored as an adjunct therapy in perinatal FeD.

Reticulocyte Hemoglobin Equivalent has Comparable Predictive Accuracy as Conventional Serum Iron Indices for Predicting Iron Deficiency and Anemia in a Nonhuman Primate model of Infantile Iron Deficiency.

BACKGROUND: Infantile iron deficiency (ID) causes anemia and compromises neurodevelopment. Current screening relies on hemoglobin (Hgb) determination at 1 year of age, which lacks sensitivity and specificity for timely detection of infantile ID. Low reticulocyte Hgb equivalent (RET-He) indicates ID, but its predictive accuracy relative to conventional serum iron indices is unknown. OBJECTIVES: The objective was to compare diagnostic accuracies of iron indices, red blood cell (RBC) indices, and RET-He for predicting the risk of ID and IDA in a nonhuman primate model of infantile ID. METHODS: Serum iron, total iron binding capacity, unsaturated iron binding capacity, transferrin saturation (TSAT), Hgb, RET-He, and other RBC indices were determined at 2 wk and 2, 4, and 6 mo in breastfed male and female rhesus infants (N = 54). The diagnostic accuracies of RET-He, iron, and RBC indices for predicting the development of ID (TSAT < 20%) and IDA (Hgb < 10 g/dL + TSAT < 20%) were determined using t tests, area under the receiver operating characteristic curve (AUC) analysis, and multiple regression models. RESULTS: Twenty-three (42.6%) infants developed ID and 16 (29.6%) progressed to IDA. All 4 iron indices and RET-He, but not Hgb or RBC indices, predicted future risk of ID and IDA (P < 0.001). The predictive accuracy of RET-He (AUC = 0.78, SE = 0.07; P = 0.003) for IDA was comparable to that of the iron indices (AUC = 0.77-0.83, SE = 0.07; P ≤ 0.002). A RET-He threshold of 25.5 pg strongly correlated with TSAT < 20% and correctly predicted IDA in 10 of 16 infants (sensitivity: 62.5%) and falsely predicted possibility of IDA in only 4 of 38 unaffected infants (specificity: 89.5%). CONCLUSIONS: RET-He is a biomarker of impending ID/IDA in rhesus infants and can be used as a hematological parameter to screen for infantile ID.

Bayesian predictive modeling of multi-source multi-way data.

A Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e., multidimensional tensor) structure is described. As a motivating example, molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model are considered. The method uses a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that the model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for the motivating application.

A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.

BACKGROUND: Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. RESULTS: We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. CONCLUSIONS: We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

Multiomic profiling of iron-deficient infant monkeys reveals alterations in neurologically important biochemicals in serum and cerebrospinal fluid before the onset of anemia.

The effects of iron deficiency (ID) during infancy extend beyond the hematologic compartment and include short- and long-term adverse effects on many tissues including the brain. However, sensitive biomarkers of iron-dependent brain health are lacking in humans. To determine whether serum and cerebrospinal fluid (CSF) biomarkers of ID-induced metabolic dysfunction are concordant in the pre/early anemic stage of ID before anemia in a nonhuman primate model of infantile iron deficiency anemia (IDA). ID (n = 7), rhesus infants at 4 mo (pre-anemic period) and 6 mo of age (anemic) were examined. Hematological, metabolomic, and proteomic profiles were generated via HPLC/MS at both time points to discriminate serum biomarkers of ID-induced brain metabolic dysfunction. We identified 227 metabolites and 205 proteins in serum. Abnormalities indicating altered liver function, lipid dysregulation, and increased acute phase reactants were present in ID. In CSF, we measured 210 metabolites and 1,560 proteins with changes in ID infants indicative of metabolomic and proteomic differences indexing disrupted synaptogenesis. Systemic and CSF proteomic and metabolomic changes were present and concurrent in the pre-anemic and anemic periods. Multiomic serum and CSF profiling uncovered pathways disrupted by ID in both the pre-anemic and anemic stages of infantile IDA, including evidence for hepatic dysfunction and activation of acute phase response. Parallel changes observed in serum and CSF potentially provide measurable serum biomarkers of ID that reflect at-risk brain processes prior to progression to clinical anemia.

sJIVE: Supervised Joint and Individual Variation Explained.

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. The proposed method, supervised joint and individual variation explained (sJIVE), can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study explores gene expression and proteomic patterns associated with lung function.

Generalized integrative principal component analysis for multi-type data with block-wise missing structure.

High-dimensional multi-source data are encountered in many fields. Despite recent developments on the integrative dimension reduction of such data, most existing methods cannot easily accommodate data of multiple types (e.g. binary or count-valued). Moreover, multi-source data often have block-wise missing structure, i.e. data in one or more sources may be completely unobserved for a sample. The heterogeneous data types and presence of block-wise missing data pose significant challenges to the integration of multi-source data and further statistical analyses. In this article, we develop a low-rank method, called generalized integrative principal component analysis (GIPCA), for the simultaneous dimension reduction and imputation of multi-source block-wise missing data, where different sources may have different data types. We also devise an adapted Bayesian information criterion (BIC) criterion for rank estimation. Comprehensive simulation studies demonstrate the efficacy of the proposed method in terms of rank estimation, signal recovery, and missing data imputation. We apply GIPCA to a mortality study. We achieve accurate block-wise missing data imputation and identify intriguing latent mortality rate patterns with sociological relevance.

Bayesian GWAS with Structured and Non-Local Priors.

MOTIVATION: The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for a marker's gene-parent membership and other characteristics to influence its probability of association with an outcome. Second, we describe a non-local alternative model for differential minor allele rates at each marker, in which the null and alternative hypotheses have no common support. RESULTS: We employ a non-parametric model that allows for clustering of the genes in tandem with a regression model for marker-level covariates, and demonstrate how incorporating these additional characteristics can improve power. We further demonstrate that our non-local alternative model gives symmetric rates of convergence for the null and alternative hypotheses, whereas commonly used local alternative models have asymptotic rates that favor the alternative hypothesis over the null. We demonstrate the robustness and flexibility of our structured and non-local model for different data generating scenarios and signal-to-noise ratios. We apply our Bayesian GWAS method to single nucleotide polymorphisms data collected from a pool of Alzheimer's disease and cognitively normal patients from the Alzheimer's Database Neuroimaging Initiative. AVAILABILITY AND IMPLEMENTATION: R code to perform the SNLPs method is available at https://github.com/lockEF/BayesianScreening.

Early-Life Iron Deficiency and Its Natural Resolution Are Associated with Altered Serum Metabolomic Profiles in Infant Rhesus Monkeys.

BACKGROUND: Iron deficiency is the most common nutrient deficiency in human infants aged 6 to 24 mo, and negatively affects many cellular metabolic processes, including energy production, electron transport, and oxidative degradation of toxins. There can be persistent influences on long-term metabolic health beyond its acute effects. OBJECTIVES: The objective was to determine how iron deficiency in infancy alters the serum metabolomic profile and to test whether these effects persist after the resolution of iron deficiency in a nonhuman primate model of spontaneous iron deficiency. METHODS: Blood was collected from naturally iron-sufficient (IS; n = 10) and iron-deficient (ID; n = 10) male and female infant rhesus monkeys (Macaca mulatta) at 6 mo of age. Iron deficiency resolved without intervention upon feeding of solid foods, and iron status was re-evaluated at 12 mo of age from the IS and formerly ID monkeys using hematological and other indices; sera were metabolically profiled using HPLC/MS and GC/MS with isobaric standards for identification and quantification at both time points. RESULTS: A total of 413 metabolites were measured, with differences in 40 metabolites identified between IS and ID monkeys at 6 mo (P$\le $ 0.05). At 12 mo, iron-related hematological parameters had returned to normal, but the formerly ID infants remained metabolically distinct from the age-matched IS infants, with 48 metabolites differentially expressed between the groups. Metabolomic profiling indicated altered liver metabolites, differential fatty acid production, increased serum uridine release, and atypical bile acid production in the ID monkeys. CONCLUSIONS: Pathway analyses of serum metabolites provided evidence of a hypometabolic state, altered liver function, differential essential fatty acid production, irregular uracil metabolism, and atypical bile acid production in ID infants. Many metabolites remained altered after the resolution of ID, suggesting long-term effects on metabolic health.

Integrative factorization of bidimensionally linked matrices.

Advances in molecular "omics" technologies have motivated new methodologies for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different cohorts on a single platform). This is limiting for data that take the form of bidimensionally linked matrices (eg, multiple cohorts measured on multiple platforms), which are increasingly common in large-scale biomedical studies. In this paper, we propose bidimensional integrative factorization (BIDIFAC) for integrative dimension reduction and signal approximation of bidimensionally linked data matrices. Our method factorizes data into (a) globally shared, (b) row-shared, (c) column-shared, and (d) single-matrix structural components, facilitating the investigation of shared and unique patterns of variability. For estimation, we use a penalized objective function that extends the nuclear norm penalization for a single matrix. As an alternative to the complicated rank selection problem, we use results from the random matrix theory to choose tuning parameters. We apply our method to integrate two genomics platforms (messenger RNA and microRNA expression) across two sample cohorts (tumor samples and normal tissue samples) using the breast cancer data from the Cancer Genome Atlas. We provide R code for fitting BIDIFAC, imputing missing values, and generating simulated data.

Linked matrix factorization.

Several recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). We introduce an approach for simultaneous horizontal and vertical integration, Linked Matrix Factorization (LMF), for the general case where some matrices share rows (e.g., features) and some share columns (e.g., samples). Our motivating application is a cytotoxicity study with accompanying genomic and molecular chemical attribute data. The toxicity matrix (cell lines × chemicals) shares samples with a genotype matrix (cell lines × SNPs) and shares features with a molecular attribute matrix (chemicals × attributes). LMF gives a unified low-rank factorization of these three matrices, which allows for the decomposition of systematic variation that is shared and systematic variation that is specific to each matrix. This allows for efficient dimension reduction, exploratory visualization, and the imputation of missing data even when entire rows or columns are missing. We present theoretical results concerning the uniqueness, identifiability, and minimal parametrization of LMF, and evaluate it with extensive simulation studies.

Discriminating sample groups with multi-way data.

High-dimensional linear classifiers, such as distance weighted discrimination (DWD) and versions of the support vector machine (SVM), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In practice, data are often multi-way, or measured over multiple dimensions. For example, metabolite abundance may be measured over multiple regions or tissues, or gene expression may be measured over multiple time points, for the same subjects. We propose a framework for linear classification of high-dimensional multi-way data, in which coefficients can be factorized into weights that are specific to each dimension. More generally, the coefficients for each measurement in a multi-way dataset are assumed to have low-rank structure. This framework extends existing classification techniques from single vector to multi-way features, and we have implemented multi-way versions of SVM and DWD. We describe informative simulation results, and apply multi-way DWD to data for two very different clinical research studies. The first study uses magnetic resonance spectroscopy metabolite data over multiple brain regions to compare participants with and without spinocerebellar ataxia; the second uses publicly available gene expression time-course data to compare degrees of treatment response among patients with multiple sclerosis. Our multi-way method can improve performance and simplify interpretation over naive applications of full rank linear and non-linear classification to multi-way data. The R package is available at https://github.com/lockEF/MultiwayClassification.

Multi-omic molecular profiling of lung cancer in COPD.

Chronic obstructive pulmonary disease (COPD) is a known risk factor for developing lung cancer but the underlying mechanisms remain unknown. We hypothesise that the COPD stroma contains molecular mechanisms supporting tumourigenesis.We conducted an unbiased multi-omic analysis to identify gene expression patterns that distinguish COPD stroma in patients with or without lung cancer. We obtained lung tissue from patients with COPD and lung cancer (tumour and adjacent non-malignant tissue) and those with COPD without lung cancer for profiling of proteomic and mRNA (both cytoplasmic and polyribosomal). We used the Joint and Individual Variation Explained (JIVE) method to integrate and analyse across the three datasets.JIVE identified eight latent patterns that robustly distinguished and separated the three groups of tissue samples (tumour, adjacent and control). Predictive variables that associated with the tumour, compared to adjacent stroma, were mainly represented in the transcriptomic data, whereas predictive variables associated with adjacent tissue, compared to controls, were represented at the translatomic level. Pathway analysis revealed extracellular matrix and phosphatidylinositol-4,5-bisphosphate 3-kinase-protein kinase B signalling pathways as important signals in the tumour adjacent stroma.The multi-omic approach distinguishes tumour adjacent stroma in lung cancer and reveals two stromal expression patterns associated with cancer.

Metabolomic analysis of CSF indicates brain metabolic impairment precedes hematological indices of anemia in the iron-deficient infant monkey.

OBJECTIVES: Iron deficiency (ID) anemia leads to long-term neurodevelopmental deficits by altering iron-dependent brain metabolism. The objective of the study was to determine if ID induces metabolomic abnormalities in the cerebrospinal fluid (CSF) in the pre-anemic stage and to ascertain the aspects of abnormal brain metabolism affected. METHODS: Standard hematological parameters [hemoglobin (Hgb), mean corpuscular volume (MCV), transferrin (Tf) saturation, and zinc protoporphyrin/heme (ZnPP/H)] were compared at 2, 4, 6, 8, and 12 months in iron-sufficient (IS; n = 7) and iron-deficient (ID; n = 7) infant rhesus monkeys. Five CSF metabolite ratios were determined at 4, 8, and 12 months using 1H NMR spectroscopy at 16.4 T and compared between groups and in relation to hematologic parameters. RESULTS: ID infants developed ID (Tf saturation < 25%) by 4 months of age and all became anemic (Hgb < 110 g/L and MCV < 60 fL) at 6 months. Their heme indices normalized by 12 months. Pyruvate/glutamine and phosphocreatine/creatine (PCr/Cr) ratios in CSF were lower in the ID infants by 4 months (P < 0.05). The PCr/Cr ratio remained lower at 8 months (P = 0.02). ZnPP/H, an established blood marker of pre-anemic ID, was positively correlated with the CSF citrate/glutamine ratio (marginal correlation, 0.34; P < 0.001; family wise error rate = 0.001). DISCUSSION: Metabolomic analysis of the CSF is sensitive for detecting the effects of pre-anemic ID on brain energy metabolism. Persistence of a lower PCr/Cr ratio at 8 months, even as hematological measures demonstrated recovery from anemia, indicate that the restoration of brain energy metabolism is delayed. Metabolomic platforms offer a useful tool for early detection of the impact of ID on brain metabolism in infants.

R.JIVE for exploration of multi-source molecular data.

UNLABELLED: : The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION: R.JIVE is available via the Comprehensive R Archive Network (CRAN) under the GPLv3 license: https://cran.r-project.org/web/packages/r.jive/ CONTACT: elock@umn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian genome- and epigenome-wide association studies with gene level dependence.

High-throughput genetic and epigenetic data are often screened for associations with an observed phenotype. For example, one may wish to test hundreds of thousands of genetic variants, or DNA methylation sites, for an association with disease status. These genomic variables can naturally be grouped by the gene they encode, among other criteria. However, standard practice in such applications is independent screening with a universal correction for multiplicity. We propose a Bayesian approach in which the prior probability of an association for a given genomic variable depends on its gene, and the gene-specific probabilities are modeled nonparametrically. This hierarchical model allows for appropriate gene and genome-wide multiplicity adjustments, and can be incorporated into a variety of Bayesian association screening methodologies with negligible increase in computational complexity. We describe an application to screening for differences in DNA methylation between lower grade glioma and glioblastoma multiforme tumor samples from The Cancer Genome Atlas. Software is available via the package BayesianScreening for R: github.com/lockEF/BayesianScreening.