Differential Analysis Methods Research Articles

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Repercussion of Fiber Content on the Mechanical, Thermal, and Thermomechanical Properties of Natural Fibers Reinforced Thermoplastic Composites for Automotive Application

Abstract Natural fibers are becoming very popular as a reinforcement in composite materials owing to their benefits, such as low-price, lightweight, availability, and environmental friendliness. In this study, abaca fiber-reinforced polypropylene (PP) and high-density polyethylene (HDPE) composites were created with the help of the injection molding method. Prior to composite fabrication, abaca fibers were chemically treated with a 5 wt.% caustic soda (NaOH) solution to improve the bonding between the abaca fibers and the matrix and to enhance their properties. Scanning electron microscopy (SEM) was utilized to assess the fiber surface microstructures before as well as after the chemical treatment, along with the fractured surfaces of tensile specimens. The mechanical properties, such as tensile, bending, and impact strength, of abaca/PP and abaca/HDPE composites were evaluated and compared. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) methods were utilized to investigate the thermal behaviour of composites. Also, the dynamic mechanical analysis (DMA) method was utilized to explore the thermomechanical properties of the fabricated composites. The outcomes of the experimental findings showed that abaca/PP composite with 10 and 20 wt.% fibers is the best choice of material to be used in the automobile industry.

Node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking.

Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks can be built to represent distinct conditions and compared to uncover graph-level differences-such as when comparing patterns of gene-gene interactions that change between biological states. Given the importance of the graph comparison problem, there is a clear and growing need for robust and scalable methods that can identify meaningful differences. We introduce node2vec2rank (n2v2r), a method for graph differential analysis that ranks nodes according to the disparities of their representations in joint latent embedding spaces. Improving upon previous bag-of-features approaches, we take advantage of recent advances in machine learning and statistics to compare graphs in higher-order structures and in a data-driven manner. Formulated as a multi-layer spectral embedding algorithm, n2v2r is computationally efficient, incorporates stability as a key feature, and can provably identify the correct ranking of differences between graphs in an overall procedure that adheres to veridical data science principles. By better adapting to the data, node2vec2rank clearly outperformed the commonly used node degree in finding complex differences in simulated data. In the real-world applications of breast cancer subtype characterization, analysis of cell cycle in single-cell data, and searching for sex differences in lung adenocarcinoma, node2vec2rank found meaningful biological differences enabling the hypothesis generation for therapeutic candidates. Software and analysis pipelines implementing n2v2r and used for the analyses presented here are publicly available.

Interpretable machine learning decodes soil microbiome’s response to drought stress

BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.ResultsThis study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.ConclusionsIn the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Qualitative analysis of licorice and strychnine decoction before and after combination using UPLC-QE-Orbitrap-MS.

Glycyrrhiza glabra L. (GG) and Strychnos nux-vomica L. (NV) are traditional Chinese medicines (TCMs). Changes in the chemical composition may occur before and after the GG-NV compatibility. Ultra-performance liquid chromatography Q-exactive Orbitrap mass spectrometry (UPLC-QE-Orbitrap-MS) was applied here to study the difference in the components of the GG and NV decoctions before and after they were combined. The changes in the chemical composition of GG and NV before and after the combination were determined. The precise molecular weight, retention time, and fragment ion peak of the different components of the decoctions before and after compatibility were obtained through UPLC-QE-Orbitrap-MS. Differential analysis methods, such as principal component analysis, were used for comparison. In the positive ion mode, 200 new components were added, whereas six components were lost. In the negative ion mode, 144 new compounds were identified, whereas three components were missing. The compatibility difference between GG and NV was studied through UPLC-QE-Orbitrap-MS. The chemical composition of GG and NV changed before and after compatibility, and a class of compounds different from GG and NV was identified in the co-decoction. This study provides an experimental basis for subsequent research into detoxification mechanisms of the GG-NV combination and offers a new analytical method for investigating the compatibility of various other TCM pairs.

The impact of Sangju Qingjie Decoction on the pulmonary microbiota in the prevention and treatment of chronic obstructive pulmonary disease.

Exploring the effect of SJQJD on the pulmonary microbiota of chronic obstructive pulmonary disease (COPD) rats through 16S ribosomal RNA (rRNA) sequencing. A COPD rat model was constructed through smoking and lipopolysaccharide (LPS) stimulation, and the efficacy of SJQJD was evaluated by hematoxylin and eosin (H&E) staining and Enzyme-Linked Immunosorbnent Assay (ELISA). The alveolar lavage fluid of rats was subjected to 16S rRNA sequencing. The diversity of lung microbiota composition and community structure was analyzed and differential microbiota were screened. Additionally, machine learning algorithms were used for screening biomarkers of each group of the microbiota. SJQJD could improve lung structure and inflammatory response in COPD rats. 16s rRNA sequencing analysis showed that SJQJD could significantly improve the abundance and diversity of bacterial communities in COPD rats. Through differential analysis and machine learning methods, potential microbial biomarkers were identified as Mycoplasmataceae, Bacillaceae, and Lachnospiraceae. SJQJD could improve tissue morphology and local inflammatory response in COPD rats, and its effect may be related to improve pulmonary microbiota.

Magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing.

Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this work, we propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics.

The quality of selected raw and pasteurized honeys based on their sensory profiles and consumer preferences.

The purpose of this study was to determine the sensory profile of honeys based on the method of quantitative descriptive analysis and principal component analysis and assess consumer preferences of raw and pasteurized honeys. Samples of multi-floral honeys (from the store and apiary) were subjected to sensory analysis based on the method of ranking for taste preference, the method of scaling based on color, aroma, taste, and texture, and the method of differential descriptive analysis using 11 quality descriptors. The results were subjected to statistical analysis using the Principal Component Analysis method. The taste was found to be a descriptor that differentiates honey by origin. Consumers prefer the taste of pasteurized honeys. As a result of assessing the quality of honeys using the scaling method, it was found that: raw honeys are characterized by a lighter color than pasteurized honeys, store-bought honeys have a less noticeable aroma than honeys obtained from beekeepers, while samples of pasteurized honeys were judged to have a consistency more like that of typical honey. The sensory profiles obtained highlight the differences between pasteurized honeys and raw honeys.

Communicative accentuation in Buryat emotive constructions

The article examines Buryat emotive constructions from the point of view of the communicative identification of participants in an emotive situation. The relevance of the study lies in the current general interest of linguists currently in the problem of the verbalisation of emotions. The novelty of the work lies in the application of an additional, communicative aspect to the study of the means of verbalising emotiveness in the Buryat language. The aim of the study is to identify the connection between the syntactic and communicative structure of a sentence, or more precisely, between the syntactic position and the communicative emphasis. Methods of linguistic observation, introspection, description, methods of differential and component analysis have been used. It has been shown that a shift in the speaker's focus from one participant to another, or to the emotion itself, is reflected in the syntactic structure of the sentence. The position of the subject is communicatively highlighted and significant in the structure of the sentence. The communicative emphasis of the subject in the Buryat language is also determined by the strict order of words in the sentence (the subject is always the topic). In the course of studying Buryat emotive constructions as speech acts, the author identified three types of constructions with communicative emphasis of the participants in an emotive situation in the subject position. These are constructions with a communicative emphasis: 1) on the Experiencer, 2) on the Stimulus, 3) on the Emotion itself. It is noted that the Stimulus can be presented both in expanded form (subordinate clauses) and in collapsed form (nominal groups). The research material was modern texts of literary works by Buryat authors, published in the electronic Buryat corpus. A survey of native speakers was also conducted in order to confirm or refute doubtful cases of communicative emphasis.

Влияние структурных характеристик полиэтилентерефталата и вторичных продуктов его производства на технологические параметры получения филамента

The temperatures of phase transitions were analyzed and the ratio of crystalline and amorphous phases was determinedfor polyethylene terephthalate samples involving secondary PET (Vivilen product) and its wastes by methods of differential scanning calorimetry and X-ray structural analysis. The studied polyethylene terephthalate samples were ranked according to their glass transition and melting temperatures, which made it possible to correlate the samples according to the degree of crystallinity.To obtain filament on the basis of dust-like wastes of the solid-phase polycondensation process, the extrusion temperature conditions were justified and filament samples for 3D printing were obtained.

Mathematical Frontiers : Exploring Advances in Differential Equations, Analysis, and Computational Methods

Titled “Mathematical Frontiers : Exploring Advances in Differential Equations, Analysis, and Computational Methods” this special issue explores the intricate network linking mathematical ideas with their many useful applications. This problem spans several mathematical disciplines, each adding to the great fabric of human knowledge and invention.

Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures

Microbiome differential abundance analysis methods for two groups are well-established in the literature. However, many microbiome studies involve more than two groups, sometimes even ordered groups such as stages of a disease, and require different types of comparison. Standard pairwise comparisons are inefficient in terms of power and false discovery rates. In this Article, we propose a general framework, ANCOM-BC2, for performing a wide range of multigroup analyses with covariate adjustments and repeated measures. We illustrate our methodology through two real datasets. The first example explores the effects of aridity on the soil microbiome, and the second example investigates the effects of surgical interventions on the microbiome of patients with inflammatory bowel disease.

Msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer’s disease dataset

Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.

Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.

Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.

Study on Screening Core Biomarkers of Noise and Drug-Induced Hearing Loss Based on Transcriptomics.

Background Noise and drug-induced hearing loss (HL) is becoming more and more serious, but the integration and analysis based on transcriptomics and proteomics are lacking. On the one hand, this study aims to integrate existing public transcriptomic data on noise and gentamicin-induced HL. On the other hand, the study aims to establish the gentamicin and noise-induced HL model of guinea pigs, then to perform the transcriptomic and proteomic analyses. Through comprehensive analysis of the above data, we aim to screen, predict, and preliminarily verify biomarkers closely related to HL. Material and Methods We screened the Gene Expression Omnibus database to obtain transcriptome data expression profiles of HL caused by noise and gentamicin, then constructed the guinea pig HL model and perform the transcriptomic and proteomic analyses. Differential expression and enrichment analysis were performed on public and self-sequenced data, and common differentially expressed genes (DEGs) and signaling pathways were obtained. Finally, we used proteomic data to screen for common differential proteins and validate common differential expression genes for HL. Results By integrating the public data set with self-constructed model data set, we eventually obtained two core biomarkers of HL, which were RSAD2 and matrix metalloproteinase-3 (MMP3). Their main function is to regulate the development of sense organ in the inner ear and they are mainly involved in mitogen-activated protein kinase and phosphoinositol-3 kinase/protein kinase B signaling pathways. Finally, by integrating the proteomic data of the self-constructed model, we also found differential expression of MMP3 protein. This also preliminarily and partially verified the above-mentioned core biomarkers. Conclusion and Significance In this study, public database and transcriptomic data of self-constructed model were integrated, and we screened out two core genes and various signal pathways of HL through differential analysis, enrichment analysis, and other analysis methods. Then, we preliminarily validated the MMP3 by proteomic analysis of self-constructed model. This study pointed out the direction for further laboratory verification of key biomarkers of HL, which is of great significance for revealing the core pathogenic mechanism of HL.

A cell abundance analysis based on efficient PAM clustering for a better understanding of the dynamics of endometrial remodelling

BackgroundSingle-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating cell abundance changes during tissue regeneration and remodeling processes. Differential cell abundance supports the initial clustering of all cells; then, the number of cells per cluster and sample are evaluated, and the dependence of these counts concerning the phenotypic covariates of the samples is studied. Analysis heavily depends on the clustering method. Partitioning Around Medoids (PAM or k-medoids) represents a well-established clustering procedure that leverages the downstream interpretation of clusters by pinpointing real individuals in the dataset as cluster centers (medoids) without reducing dimensions. Of note, PAM suffers from high computational costs and memory requirements.ResultsThis paper proposes a method for differential abundance analysis using PAM as a clustering method and negative binomial regression as a statistical model to relate covariates to cluster/cell counts. We used this approach to study the differential cell abundance of human endometrial cell types throughout the natural secretory phase of the menstrual cycle. We developed a new R package -scellpam-, that incorporates an efficient parallel C++ implementation of PAM, and applied this package in this study. We compared the PAM-BS clustering method with other methods and evaluated both the computational aspects of its implementation and the quality of the classifications obtained using distinct published datasets with known subpopulations that demonstrate promising results.ConclusionsThe implementation of PAM-BS, included in the scellpam package, exhibits robust performance in terms of speed and memory usage compared to other related methods. PAM allowed quick and robust clustering of sets of cells with a size ranging from 70,000 to 300,000 cells. https://cran.r-project.org/web/packages/scellpam/index.html. Finally, our approach provides important new insights into the transient subpopulations associated with the fertile time frame when applied to the study of changes in the human endometrium during the secretory phase of the menstrual cycle.

Микропузырьковая безреагентная гидрофобизация поверхности угля

The paper describes methodological features of coal surface hydrophobization the using a microbubble gas-liquid medium to intensify the process of flotation concentration of coal. Air microbubbles were generated using decompression of liquid saturated under air pressure. The influence of water saturation pressure with gas on the size of the bubbles and their stability over time has been established. It is shown that addition of microbubbles leads to formation of an intermediate hydrophobic air layer on the surface of coal, which is the basis for catching bubbles of the flotation size. The results of coking coals flotation using a microbubble gas-liquid mixture are presented. By means of the method of differential particle size analysis, the preferred flotation of fine coal particles < 50 microns in size by micro-bubbles of air was established. It was found that for low-ash coals, the efficiency of reagentless microbubble flotation can be comparable with the results of concentration using traditional petrochemical reagents. In the reagent mode, the micro-bubbles are attached to the oil-hydrophobized surface of small particles with the formation of carbon-air complexes.

Robust Differential Abundance Analysis of Microbiome Sequencing Data.

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.

A continuous and long-term in-situ stress measuring method based on fiber optic. Part I: Theory of inverse differential strain analysis

A method for in-situ stress measurement via fiber optics was proposed. The method utilizes the relationship between rock mass elastic parameters and in-situ stress. The approach offers the advantage of long-term stress measurements with high spatial resolution and frequency, significantly enhancing the ability to measure in-situ stress. The sensing casing, spirally wrapped with fiber optic, is cemented into the formation to establish a formation sensing nerve. Injecting fluid into the casing generates strain disturbance, establishing the relationship between rock mass properties and treatment pressure. Moreover, an optimization algorithm is established to invert the elastic parameters of formation via fiber optic strains. In the first part of this paper series, we established the theoretical basis for the inverse differential strain analysis method for in-situ stress measurement, which was subsequently verified using an analytical model. This paper is the fundamental basis for the inverse differential strain analysis method.