Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Oct. 17, 2022

Abstract Background Cell-cell interactions are important for information exchange between different cells, which the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization cell-cell using computational methods. However, it is hard to evaluate these methods since no ground truth provided. Spatial transcriptomics (ST) data profiles relative position cells. We propose that spatial distance suggests interaction tendency cell types, thus could be used evaluating tools. Results benchmark 16 by integrating scRNA-seq with ST data. characterize into short-range and long-range distributions ligands receptors. Based on this classification, we define enrichment score apply an evaluation workflow tools 15 simulated 5 real datasets. also compare consistency results from single commonly identified interactions. Our suggest predicted highly dynamic, statistical-based show overall better performance than network-based ST-based Conclusions study presents a comprehensive scRNA-seq. CellChat, CellPhoneDB, NicheNet, ICELLNET other terms software scalability. recommend at least two ensure accuracy have packaged detailed documentation GitHub ( https://github.com/wanglabtongji/CCI ).

Language: Английский

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Synthetic data generation methods in healthcare: A review on open-source tools and methods

Computational and Structural Biotechnology Journal, Journal Year: 2024, Volume and Issue: 23, P. 2892 - 2910

Published: July 9, 2024

Synthetic data generation has emerged as a promising solution to overcome the challenges which are posed by scarcity and privacy concerns, well as, address need for training artificial intelligence (AI) algorithms on unbiased with sufficient sample size statistical power. Our review explores application efficacy of synthetic methods in healthcare considering diversity medical data. To this end, we systematically searched PubMed Scopus databases great focus tabular, imaging, radiomics, time-series, omics Studies involving multi-modal were also explored. The type method used process was identified each study categorized into statistical, probabilistic, machine learning, deep learning. Emphasis given programming languages implementation method. evaluation revealed that majority studies utilize generators to: (i) reduce cost time required clinical trials rare diseases conditions, (ii) enhance predictive power AI models personalized medicine, (iii) ensure delivery fair treatment recommendations across diverse patient populations, (iv) enable researchers access high-quality, representative multimodal datasets without exposing sensitive information, among others. We underline wide use learning based 72.6 % included studies, 75.3 being implemented Python. A thorough documentation open-source repositories is finally provided accelerate research field.Graphical abstract

Language: Английский

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Recent advances in mass spectrometry-based computational metabolomics

Current Opinion in Chemical Biology, Journal Year: 2023, Volume and Issue: 74, P. 102288 - 102288

Published: March 24, 2023

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of across a wide array scientific medical disciplines. continues expand as modern instrumentation produces datasets with increasing complexity, resolution, sensitivity. These must be processed, annotated, modeled, interpreted enable biological insight. Techniques for visualization, integration (within or between omics), interpretation data have evolved along innovation in databases knowledge resources required aid understanding. In this review, we highlight recent advances reflect on opportunities innovations response most pressing challenges. This review was compiled from discussions 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra Knowledge".

Language: Английский

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PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(3), P. e1011814 - e1011814

Published: March 25, 2024

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation such data. Current typically output lists, clusters, or subnetworks molecules related to outcome. Even with expert domain knowledge, discerning biological processes involved a time-consuming activity. Here we propose PathIntegrate, method integrating datasets based on pathways, designed exploit knowledge systems thus provide interpretable models studies. PathIntegrate employs single-sample pathway analysis transform from molecular pathway-level, applies predictive single-view multi-view model integrate Model outputs include pathways ranked by their contribution outcome prediction, each omics layer, importance molecule in pathway. Using semi-synthetic demonstrate benefit grouping into detect signals low signal-to-noise scenarios, as well ability precisely identify important at effect sizes. Finally, using COPD COVID-19 showcase how enables convenient complex high-dimensional datasets. available open-source Python package.

Language: Английский

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Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(6)

Published: Sept. 23, 2024

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic cancer, and cognitive disorders to respiratory pathologies conditions that involve dysregulated metabolism. Because importance systems-level interpretation, many methods have been developed identify biologically significant pathways using metabolomics data. In this review, we first describe complete workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable performing functional including those combine with other types investigate disease-relevant changes at multiple omics layers. discuss their availability, implementation, capability pre-processing quality control, supported types, embedded databases, pathway analysis methodologies, integration techniques. also provide rating evaluation each software, focusing on key technique, software accessibility, documentation, user-friendliness. Following our guideline, life scientists can easily choose suitable method depending rating, available data, input format, category. More importantly, highlight outstanding challenges potential solutions need be addressed by future research. To further assist users in executing reviewed methods, wrappers packages https://github.com/tinnlab/metabolite-pathway-review-docker.

Language: Английский

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Recent advances in high-throughput biofluid metabotyping by direct infusion and ambient ionization mass spectrometry

TrAC Trends in Analytical Chemistry, Journal Year: 2023, Volume and Issue: 168, P. 117287 - 117287

Published: Sept. 17, 2023

Biofluid metabolomics is a popular tool for biomarker discovery to decipher disease-, genetics-, and exposure-related metabolic alterations an essential component understanding integrated metabolite-level responses. The conventional workflow in mass spectrometry (MS) involves hyphenation with chromatographic separation represents valuable analytical both research clinical settings. However, complexity, relatively low throughput, high costs often hinder implementation when routine, large-scale analysis sample turnover desired, such as point-of-care applications. In this context, direct infusion (DI) ambient ionization (AI) MS, where samples can be analysed directly, rapidly, minimal handling, offer attractive alternatives hyphenated methods. Recent technological advances have addressed the typical issues of AIMS DIMS methods regarding metabolome coverage, reproducibility, repeatability encountered during their early development. systematic review, we discussed recent (2017–2023) original publications on DIMS- AIMS-based biofluid considering reported biomedical implementations, assets workflow, data handling coherence platforms.

Language: Английский

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A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 31, 2025

Aging is a complex and systematic biological process that involves multiple genes pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular during remains insufficiently explored. To bridge this gap, we propose novel computational framework to identify effect of coordinated patterns gene-expression Our includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential connectivity analysis age groups (3) machine learning models, XGBoost Random Forest (RF) fed by expression levels lower-dimensional pathway score space, unique key for classifying aging. We applied our approach three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal Brain-Cortex. The RF model demonstrated best performance in predicting group (AUC < 88%) highlighting involved coordination processes also identified involvement lipid metabolism, immune system, cell communication detected distinct manifested proposed highlights importance underlying provides valuable insights into mechanisms which can further assist development therapeutic strategies promoting healthy

Language: Английский

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Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis

BMC Bioinformatics, Journal Year: 2025, Volume and Issue: 26(1)

Published: April 16, 2025

Language: Английский

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The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

Computational and Structural Biotechnology Journal, Journal Year: 2023, Volume and Issue: 21, P. 4933 - 4943

Published: Jan. 1, 2023

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from specimens, holistic insights can be gained on lung its interaction with host immunity inflammation in diseases. power multi-omics have moved field forward associative assessment alterations to causative understanding pathogenesis chronic, acute other However, application remains unique challenges sample processing, integration, downstream validation. In this review, we first introduce applicable studying microbiome. We next describe approaches for focusing dimensionality reduction, association prediction. then summarize progresses finally discuss current share our thoughts future promises field.

Language: Английский

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The metabolic role of vitamin D in children’s neurodevelopment: a network study

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 23, 2024

Neurodevelopmental disorders are rapidly increasing in prevalence and have been linked to various environmental risk factors. Mounting evidence suggests a potential role of vitamin D child neurodevelopment, though the causal mechanisms remain largely unknown. Here, we investigate how deficiency affects children's communication development, particularly relation Autism Spectrum Disorder (ASD). We do so by developing an integrative network approach that combines metabolomic profiles, clinical traits, neurodevelopmental data from pediatric cohort. Our results show low levels associated with changes metabolic networks tryptophan, linoleic, fatty acid metabolism. These correlate distinct ASD-related phenotypes, including delayed skills respiratory dysfunctions. Additionally, our analysis kynurenine serotonin sub-pathways may mediate effect on early life development. Altogether, findings provide metabolome-wide insights into as therapeutic option for ASD other disorders.

Language: Английский

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