11
views
0
recommends
+1 Recommend
1 collections
    0
    shares

      To submit to the journal, please click here

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE)

      research-article
      1 , 1 , 2 , 1 , 2 , * , , 3 , * , , 3 , * ,
      Research
      AAAS

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

          Related collections

          Most cited references42

          • Record: found
          • Abstract: found
          • Article: not found

          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Integrated analysis of multimodal single-cell data

              Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
                Bookmark

                Author and article information

                Journal
                Research (Wash D C)
                Research (Wash D C)
                RESEARCH
                Research
                AAAS
                2639-5274
                29 April 2024
                2024
                : 7
                : 0368
                Affiliations
                [ 1 ]School of Mathematics and Big Data, Foshan University , Foshan 528000, China.
                [ 2 ]School of Biology and Biological Engineering, South China University of Technology , Guangzhou 510640, China.
                [ 3 ]School of Mathematics, South China University of Technology , Guangzhou 510640, China.
                Author notes
                [*] [* ]Address correspondence to: fling@ 123456scut.edu.cn (F.L.); chenpei@ 123456scut.edu.cn (P.C.); scliurui@ 123456scut.edu.cn (R.L.)
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-0508-1383
                Article
                0368
                10.34133/research.0368
                11075703
                38716473
                d3878087-c276-4392-91b9-f41034999b77
                Copyright © 2024 Jiayuan Zhong et al.

                Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

                History
                : 10 December 2023
                : 06 April 2024
                : 29 April 2024
                Page count
                Figures: 5, Tables: 1, References: 44, Pages: 0
                Funding
                Funded by: National Natural Science Foundation of China, FundRef http://dx.doi.org/10.13039/501100001809;
                Award ID: 12322119
                Award Recipient : Rui Liu
                Funded by: National Natural Science Foundation of China, FundRef http://dx.doi.org/10.13039/501100001809;
                Award ID: 62172164
                Award Recipient : Rui Liu
                Funded by: National Natural Science Foundation of China, FundRef http://dx.doi.org/10.13039/501100001809;
                Award ID: 12271180
                Award Recipient : Pei Chen
                Categories
                Research Article

                Comments

                Comment on this article