Statistical modelling of an outcome variable with integrated multi-omics.

The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.

Objective: The current study aimed to evaluate the relation between leptin and serum lipids, and to explore whether alcohol consumption modifies the effect of leptin on lipids. Method: We conducted a Mendelian randomization analysis to the baseline data for 3,780 participants of the Framingham Heart Study Generation III cohort. A genetic risk score (GRS) for leptin was generated by summing leptin increasing alleles of 3 single-nucleotide polymorphisms, weighted by their corresponding effect sizes on leptin reported by Kilpelainen et al. For those taking lipid lowering medications, total cholesterol (TC) was adjusted as TC/0.8, and low density lipoprotein (LDL) was imputed by replacing the adjusted TC into the Friedewald equation. Leptin and triglycerides (TG) were log transformed. Associations between the GRS and leptin, leptin and imputed lipids, and GRS and imputed lipids were assessed, respectively, by multivariate linear regression models, controlling for age and sex in basic model and additionally controlling for education, smoking, drinking, and physical activity in the fully adjusted model. Interactions between the GRS and alcohol drinking was evaluated by adding an interaction term, GRS*drinking, in the fully adjusted model. Sensitivity analyses were performed among those not taking lipid or glucose lowering medications. Result: In univariate analyses, the GRS was significantly associated with leptin (Beta=1.44, P =4.55E-4), but not with age ( P =0.33), sex ( P =0.62), education ( P =0.37), smoking ( P =0.59), drinking ( P =0.42), or physical activity ( P =0.80). Leptin was significantly associated with age and sex adjusted LDL (Beta=7.29, P =5.95E-38), high density lipoprotein (HDL) (Beta=-4.30, P =4.35E-62), TG (Beta=0.22, P =1.05E-119), and TC (Beta=8.06, P =1.72E-36). The GRS for leptin was associated with HDL (Beta=-16.72, P =0.01), but not LDL (Beta=2.64, P =0.85), TG (Beta=-0.35, P =0.16), or TC (Beta=-23.34, P =0.15), in partially adjusted model. In the fully adjusted model, association between the GRS and HDL was still significant (Beta=-16.70, P =0.01). When stratified by drinking status, the GRS for leptin was significantly associated with reduced LDL (Beta=-99.26, P =0.02), TG (Beta=-2.4, P =0.002), and TC (Beta=-156.10, P =0.001) among non-current drinkers, and reduced HDL (Beta=-19.98, P =0.005) among current drinkers, adjusting for age and sex. After further adjustment, these associations were still significant. In the fully adjusted model, significant interactions between the GRS and alcohol drinking were identified for LDL ( P =0.02), TG ( P =0.005), and TC ( P =0.008). Sensitivity analyses among those not taking lipid or glucose lowering medications revealed similar associations. Conclusion: Out study provided evidence for a causal relationship between leptin and lipids, and an interaction effect of alcohol drinking on leptin and lipids associations.

To investigate whether polygenic risk scores obtained using data from a genome-wide association study (GWAS) of rheumatoid arthritis (RA) susceptibility can be predictors of radiographic progression. We constructed polygenic risk scores using GWAS summary data on associations of single-nucleotide polymorphisms with RA susceptibility. The polygenic risk scores were stratified into quintiles based on levels of significance (ranging from top quintile of polygenic risk scores to bottom quintile). In addition, change in the Sharp/van der Heijde score (SHS) of radiographic progression over the first 5 years after onset of RA was assessed. The change in SHS over 5 years was stratified according to quartiles, with the top quartile of change in SHS defined as severe radiographic progression (score change of >35 points) and the remaining 3 quartiles defined as nonsevere radiographic progression. Polygenic risk scores were assessed for their ability to predict the SHS status over 5 years in a training set (n = 500 RA patients) for selection of the best model, and in a testing set (n = 740 RA patients) for validation of the data. We evaluated the performance of the polygenic risk score as a predictor of severe radiographic progression in univariable and multivariable analyses with inclusion of other factors. Polygenic risk scores constructed from 43,784 single-nucleotide polymorphisms significantly differed between patients who experienced severe radiographic progression and those with nonsevere radiographic progression in both the training set (P = 0.0064) and the testing set (P = 0.017). Patients with polygenic risk scores in the top quintile had a higher risk of severe progression compared to those with polygenic risk scores in the bottom quintile (odds ratio [OR] 1.90, P = 0.0022), and the risk of severe radiographic progression was even higher when restricted to patients who were younger at disease onset (OR 5.06, P = 0.00038). The group with polygenic risk scores in the top quintile and the anti-citrullinated protein antibody (ACPA)-positive group had significantly higher proportions of patients with severe radiographic progression (P = 0.00052 and P = 0.0022, respectively) compared to the remaining groups. Multivariable analysis showed that polygenic risk score (P = 0.00019) as well as female sex (P = 0.0033), ACPA positivity (P = 0.0023), and body mass index (P = 0.024) were independent risk factors for severe radiographic progression. A polygenic risk score that is derived from GWAS data on RA susceptibility is associated with the level of severity of radiographic progression in patients with RA.

OPINION article Front. Physiol., 22 January 2014Sec. Integrative Physiology Volume 4 - 2013 | https://doi.org/10.3389/fphys.2013.00403

Objective To explore the construction of self-efficacy model of hepatocellular carcinoma patients. Methods A total of 290 patients with hepatocellular carcinoma from Wuhan Union Hospital were selected by convenient sampling method. The patients were investigated with general information questionnaire, self-management efficacy scale, Self-rating Depression Scale and Social Support Scale. χ2 test was applied to high level self-management efficacy group and not high level self-management efficacy group. The significant factor through univariate analysis (P < 0.05) was used to investigate the influencing factors of self-efficacy in patients with hepatocellular carcinoma by Logistic regression analysis. The structural equation model of self-efficacy model of hepatocellular carcinoma patients was obtained by inputting the status survey data into Amos 23.0 software, and the correlation between the variables of the model was analyzed. Results Logistic regression analysis showed that age (P= 0.018), marital status (P= 0.030) and income (P= 0.034) were the influencing factors of self-efficacy in patients with hepatocellular carcinoma. The model of fitness model was established by χ2/df= 2.381, root mean square residual= 0.008, goodness-of-fit index= 0.945, adjusted goodness-of-fit index= 0.906, normed fit index= 0.951, incremental fit index = 0.971, comparative fit index = 0.971, root mean square error of approximation = 0.071, the model had a good fit, could reflect the relationship between the variables, social support for patients with hepatocellular carcinoma depression direct effect of -0.209, social support for patients with hepatocellular carcinoma self-efficacy direct effect of 0.241, depression on the effect of self-efficacy on patients with hepatocellular carcinoma was -0.171. The model recognizes that depression was a risk factor for self-efficacy in patients with hepatocellular carcinoma and that social support was a protective factor. Conclusions In clinical work, health care workers should pay attention to the influencing factors and protective factors of self-efficacy of patients with hepatocellular carcinoma. The self-efficacy model of hepatocellular carcinoma patients can be used to improve the quality of life of patients with hepatocellular carcinoma. Key words: Liver neoplasms; Structural equation; Self-efficacy model

Nebivolol and valsartan (VAL) in combination with each other successfully control blood pressure and improve hypertension patient outcomes. To develop and validate innovative, simple, and sustainable spectrophotometric methods for the simultaneous analysis of nebivolol and valsartan. The new modified difference amplitude modulation (MD-AM) method uses only unified regression equation and does not require any resolution techniques. Other different approaches were also applied for the determination of the same mixture including univariate and multivariate spectrophotometric methods. The multivariate methods were PLS and PCR, whereas the univariate methods were derivative ratio (DD1), ratio difference (RD), constant center (CC), constant center spectrum subtraction (CC-SS), constant value coupled with amplitude difference (CV-AD), advanced concentration value (ACV), and amplitude difference (AD). The proposed methods use a green solvent; thus, the environmental impact of the presented procedures was evaluated qualitatively and quantitatively using six well-known evaluation tools. All methods were applied successfully for the analysis of the studied drugs in their bulk powder, pharmaceutical dosage form Byvalson®, and in vitro release at intestinal pH (7.4) using a USP dissolution tester. Results obtained were compared statistically with the reported method and with each other using a one-way ANOVA statistical test, and no significant differences were found. All green and white analytical chemistry evaluation tools results confirm the safety, sustainability, and cost-effectiveness of the approaches, indicating that the methods are regarded green and sustainable. Results were agreeable, encouraging their applicability in quality control laboratories for dosage form and making these methods an eco-friendly substitute for the analysis of this combined dosage form and for evaluating the dissolution profile. For the first time, a severely overlapped spectrum was determined using a unified regression equation without the need of extended part or zero contribution regions by the novel method MD-AM. The proposed methods are the first study of in vitro dissolution profiling of nebivolol hydrochloride (NEB) and VAL and the first sustainable and green methods applied without compromising the analytical criteria.

Methods for imputation of missing values in air quality data sets

Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P &amp;lt; 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P &amp;lt; 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P &amp;lt; 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-10.

BackgroundInclusion Body Myositis is an acquired muscle disease. Its pathogenesis is unclear due to the co-existence of inflammation, muscle degeneration and mitochondrial dysfunction. We aimed to provide a more advanced understanding of the disease by combining multi-omics analysis with prior knowledge. We applied molecular subnetwork identification to find highly interconnected subnetworks with a high degree of change in Inclusion Body Myositis. These could be used as hypotheses for potential pathomechanisms and biomarkers that are implicated in this disease.ResultsOur multi-omics analysis resulted in five subnetworks that exhibit changes in multiple omics layers. These subnetworks are related to antigen processing and presentation, chemokine-mediated signaling, immune response-signal transduction, rRNA processing, and mRNA splicing. An interesting finding is that the antigen processing and presentation subnetwork links the underexpressed miR-16-5p to overexpressed HLA genes by negative expression correlation. In addition, the rRNA processing subnetwork contains the RPS18 gene, which is not differentially expressed, but has significant variant association. The RPS18 gene could potentially play a role in the underexpression of the genes involved in 18 S ribosomal RNA processing, which it is highly connected to.ConclusionsOur analysis highlights the importance of interrogating multiple omics to enhance knowledge discovery in rare diseases. We report five subnetworks that can provide additional insights into the molecular pathogenesis of Inclusion Body Myositis. Our analytical workflow can be reused as a method to study disease mechanisms involved in other diseases when multiple omics datasets are available.

Evaluation of Equations for Unbound Serum Concentration Prediction of Carbamazepine and Carbamazepine-10,11-epoxide in Polytherapy Pediatric Patients with Epilepsy

Phenolic compounds are toxic chemical pollutants present in water. Three-dimensional fluorescence spectroscopy analysis is an effective and rapid method for real-time phenol monitoring in aquatic environments. However, similar chemical structures of phenols result in highly overlapping three-dimensional fluorescence spectra. Therefore, it is extremely difficult to analyze and quantify the concentration of components in a mixture system that includes two or more phenolic compounds. In this article, we study the mixed phenol system containing phenol, o-cresol, p-cresol, m-cresol, catechol, and resorcinol combined with excitation-emission matrix (EEM) fluorescence data. A multivariate statistical method called best linear unbiased prediction (BLUP) is proposed to analyze the spectra with the aim to achieve quantitative results and a trilinear decomposition algorithm called parallel factor analysis (PARAFAC) was used for comparison. Two experiments with different calibration samples were set to validate the effectiveness of BLUP through recovery, ARecovery (Average Recovery), AREP (Average Relative Error of Prediction), and RMSE (Root Mean Square Error). Overall, the average recovery of each component in experiment 1 and experiment 2 ranged from 95.91% to 111.62% and 82.91% to 129.02%, respectively. Based on the results of the experiments, the concentration of phenolic compounds in water can be quantitatively determined by combining three-dimensional fluorescence spectroscopy with the BLUP method.

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

Among the Earth Observation (EO) instruments used in environmental and agricultural applications, the SPOT4-VEGETATION (SPOT4-VGT) instrument allows daily monitoring of terrestrial vegetation cover through remote sensing, at regional and global levels. The characterization of the geometric and radiometric performances of remote sensing data and their temporal stability play an important role in the analysis of EO images and are essential in environmental and agricultural monitoring applications. The validation and characterization of the SPOT4-VGT image geometric performances and temporal stability are investigated in this letter. Three time series of data acquired during 1999, 2000 and 2001 were used for this purpose. These images were downlinked by several VGT L-band receiving stations and processed by the SPACE-VGT software. From analyses made, it can be concluded that the geometric characteristics of the VGT L-band images agree with the VGT International User Committee requirements: the multispectral registration has a root mean square (RMS) error in the range of 0.25±0.17 pixels for B2, B3 and MIR bands, and 0.48±0.21 pixels for B0 band; the multi-temporal registration has a RMS error in the range of 0.38±0.23 pixels; and the absolute geo-location has a RMS error in the range of ±660 m.