Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data
Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell–cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quan...
Saved in:
| Published in | NAR genomics and bioinformatics Vol. 7; no. 2; p. lqaf084 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
01.06.2025
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell–cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell–cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell–cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects. |
|---|---|
| AbstractList | Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell-cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell-cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects. Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell-cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell-cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects.Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell-cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell-cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects. |
| Author | Di Camillo, Barbara Tussardi, Gaia Baruzzo, Giacomo Cesaro, Giulia |
| Author_xml | – sequence: 1 givenname: Giulia orcidid: 0000-0001-7971-963X surname: Cesaro fullname: Cesaro, Giulia – sequence: 2 givenname: Giacomo orcidid: 0000-0001-6129-5007 surname: Baruzzo fullname: Baruzzo, Giacomo – sequence: 3 givenname: Gaia orcidid: 0009-0009-7173-2683 surname: Tussardi fullname: Tussardi, Gaia – sequence: 4 givenname: Barbara orcidid: 0000-0001-8415-4688 surname: Di Camillo fullname: Di Camillo, Barbara |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40585304$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkctLw0AQxhep2Fp79Sh79JJ2X3kdS31CURA9h8l2NkQ3m3Y3QfzvTW0VmcMMH78ZPuY7JyPXOiTkkrM5Z7lcOPAVlAu7A8MydUImIpE8ykWSjf7NYzIL4Z0xJmIVK8bPyFixOIslUxNS3dTGoEfX1WCpRmt7C57qtml6V2vo6tbR2v0gGqnx0OBn6z-oaT0dyAqjoMEiDbWrLEb7C_TlaRkF3PXDyqDSDXRwQU4N2ICzY5-St7vb19VDtH6-f1wt15EWueiikiMKabgeSokceMx5yhQwzhRPUw6GQ4IIkOTZxoBOSp4kg2IkF6YELafk-nB369vBQOiKpg57U-Cw7UMhhYizVCgZD-jVEe3LBjfF1tcN-K_i9zkDMD8A2rcheDR_CGfFPoDiEEBxDEB-A3PLfAs |
| Cites_doi | 10.1126/sciadv.abf1356 10.1016/j.coisb.2017.07.004 10.3390/biom11030437 10.1038/s41551-021-00770-5 10.1038/s41598-022-07959-x 10.1042/BST20210863 10.4324/9780203771587 10.1016/j.cell.2021.06.018 10.1038/s41576-023-00685-8 10.1016/j.immuni.2019.11.014 10.1016/j.ccell.2022.10.008 10.1038/s41598-021-98241-z 10.1101/507871 10.1038/s42003-021-02986-2 10.1016/j.neuron.2012.02.017 10.1038/s41596-024-01045-4 10.1016/j.cell.2024.02.031 10.7554/eLife.27041 10.1186/s13059-015-0844-5 10.1038/s41467-022-30755-0 10.1101/gr.278001.123 10.3390/cancers14194957 10.1093/bioinformatics/btaa482 10.1001/jamaneurol.2013.234 10.21037/atm.2017.03.62 10.1128/EC.00041-08 10.3390/biology12101307 10.1093/bioinformatics/btac447 10.1093/nar/gkaa183 10.1038/s41587-023-01782-z 10.1109/PDP66500.2025.00045 10.1016/j.coisb.2021.03.007 10.1093/bioadv/vbac092 10.1038/s41467-023-36800-w 10.18632/aging.101195 10.1038/s41477-023-01544-4 10.1038/s41467-021-21246-9 10.1080/00031305.2017.1375990 10.1073/pnas.94.20.10925 10.2174/1381612826666200115095937 10.1038/s41467-021-25960-2 10.1038/s41576-020-00292-x 10.1093/bioinformatics/btac036 10.1093/bioinformatics/btab370 10.1038/s41556-024-01469-w 10.1093/cercor/bhu317 10.1093/nar/28.1.27 10.1007/s00702-009-0271-4 10.1016/j.bbi.2020.11.038 10.1038/s41467-021-21244-x 10.4103/1673-5374.324840 10.1101/2023.06.13.544751 10.18637/jss.v055.i14 10.1038/s43587-023-00514-x 10.1186/s13059-022-02783-y 10.1016/j.bbadis.2006.05.002 10.1016/j.csbj.2023.01.008 10.1038/s41586-022-05060-x 10.1038/s41593-024-01796-z 10.1126/science.abl4896 10.1093/bib/bbac234 10.1038/s41467-020-18873-z |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. |
| Copyright_xml | – notice: The Author(s) 2025. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/nargab/lqaf084 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2631-9268 |
| ExternalDocumentID | 40585304 10_1093_nargab_lqaf084 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 53G AAFWJ AAPXW AAVAP AAYXX ABEJV ABGNP ABPTD ABXVV AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AMNDL BBNVY BENPR BHPHI CCPQU CITATION EBS EMOBN GROUPED_DOAJ HCIFZ IAO KSI M7P M~E PHGZM PHGZT PIMPY PQGLB RPM TOX CGR CUY CVF ECM EIF IGS IHR INH ITC NPM 7X8 |
| ID | FETCH-LOGICAL-c292t-b1ee23f1c1c1429a1511704a01041771af1a6eeaa698dfac6b166a6ef312fbac3 |
| ISSN | 2631-9268 |
| IngestDate | Fri Jul 11 16:56:44 EDT 2025 Mon Jul 21 05:59:02 EDT 2025 Tue Jul 15 05:56:35 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc/4.0 The Author(s) 2025. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c292t-b1ee23f1c1c1429a1511704a01041771af1a6eeaa698dfac6b166a6ef312fbac3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-6129-5007 0000-0001-8415-4688 0000-0001-7971-963X 0009-0009-7173-2683 |
| OpenAccessLink | https://academic.oup.com/nargab/article-pdf/7/2/lqaf084/63526388/lqaf084.pdf |
| PMID | 40585304 |
| PQID | 3225872435 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3225872435 pubmed_primary_40585304 crossref_primary_10_1093_nargab_lqaf084 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-01 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | NAR genomics and bioinformatics |
| PublicationTitleAlternate | NAR Genom Bioinform |
| PublicationYear | 2025 |
| References | Almet (2025062715125311100_B11) 2021; 26 Jin (2025062715125311100_B18) 2024; 20 Ding (2025062715125311100_B61) 2021; 92 Xin (2025062715125311100_B44) 2022; 38 Baruzzo (2025062715125311100_B30) 2025 Solovey (2025062715125311100_B21) 2020; 36 Kane (2025062715125311100_B35) 2013; 55 Kanehisa (2025062715125311100_B49) 2000; 28 Raredon (2025062715125311100_B16) 2022; 12 Squair (2025062715125311100_B40) 2021; 12 Page (2025062715125311100_B38) 1999 Hu (2025062715125311100_B24) 2021; 7 You (2025062715125311100_B51) 2023; 12 Jin (2025062715125311100_B23) 2021; 12 Cowen (2025062715125311100_B6) 2008; 7 Lagger (2025062715125311100_B19) 2023; 3 Cherry (2025062715125311100_B15) 2021; 5 Wang (2025062715125311100_B17) Cihankaya (2025062715125311100_B1) 2022; 17 Wilk (2025062715125311100_B42) 2023; 42 Angerer (2025062715125311100_B29) 2017; 4 Foerster (2025062715125311100_B55) 2013; 70 Han (2025062715125311100_B9) 2023; 9 Hou (2025062715125311100_B13) 2020; 11 Browaeys (2025062715125311100_B27) Armingol (2025062715125311100_B12) 2024; 25 Kuppe (2025062715125311100_B43) 2022; 608 Regev (2025062715125311100_B8) 2017; 6 Lerma-Martin (2025062715125311100_B45) 2024; 27 Dimitrov (2025062715125311100_B25) 2024; 26 Jassal (2025062715125311100_B50) 2020; 48 Cabello-Aguilar (2025062715125311100_B36) 2020; 48 Zhao (2025062715125311100_B48) 2023; 14 Luo (2025062715125311100_B33) 2023; 33 Pineda (2025062715125311100_B46) 2024; 187 Cillo (2025062715125311100_B14) 2020; 52 Liu (2025062715125311100_B32) 2022; 23 Salcher (2025062715125311100_B47) 2022; 40 Armingol (2025062715125311100_B64) 2020; 22 Matrone (2025062715125311100_B60) 2023; 21 Garden (2025062715125311100_B2) 2012; 73 Romano (2025062715125311100_B53) 2021; 11 Cesaro (2025062715125311100_B4) 2022; 2 Nisar (2025062715125311100_B3) 2020; 26 Interlandi (2025062715125311100_B28) 2022; 5 Baruzzo (2025062715125311100_B34) 2022; 38 Andrés-Benito (2025062715125311100_B54) 2017; 9 Dimitrov (2025062715125311100_B31) 2022; 13 Favuzzi (2025062715125311100_B58) 2021; 184 Peng (2025062715125311100_B63) 2022; 23 Nagai (2025062715125311100_B22) 2021; 37 Gao (2025062715125311100_B26) 2022; 14 Jin (2025062715125311100_B10) 2022; 50 Steinacker (2025062715125311100_B59) 2009; 116 Schiavone (2025062715125311100_B5) 2017; 5 Cohen (2025062715125311100_B39) 1998 Saba (2025062715125311100_B62) 2016; 26 Finak (2025062715125311100_B37) 2015; 16 Van (2025062715125311100_B52) 2006; 1762 Noël (2025062715125311100_B20) 2021; 12 Hemerková (2025062715125311100_B57) 2021; 11 Poss (2025062715125311100_B56) 1997; 94 Eddelbuettel (2025062715125311100_B41) 2018; 72 Jones (2025062715125311100_B7) 2022; 376 |
| References_xml | – volume: 7 start-page: eabf1356 year: 2021 ident: 2025062715125311100_B24 article-title: CytoTalk: de novo construction of signal transduction networks using single-cell transcriptomic data publication-title: Sci Adv doi: 10.1126/sciadv.abf1356 – volume: 4 start-page: 85 year: 2017 ident: 2025062715125311100_B29 article-title: Single cells make big data: new challenges and opportunities in transcriptomics publication-title: Curr OpinSyst Biol doi: 10.1016/j.coisb.2017.07.004 – year: 1999 ident: 2025062715125311100_B38 publication-title: The PageRank Citation Ranking: Bringing Order to the Web – volume: 11 start-page: 437 year: 2021 ident: 2025062715125311100_B57 article-title: Role of oxidative stress in the pathogenesis of amyotrophic lateral sclerosis: antioxidant metalloenzymes and therapeutic strategies publication-title: Biomolecules doi: 10.3390/biom11030437 – volume: 5 start-page: 1228 year: 2021 ident: 2025062715125311100_B15 article-title: Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics publication-title: Nat Biomed Eng doi: 10.1038/s41551-021-00770-5 – volume: 12 start-page: 1 year: 2022 ident: 2025062715125311100_B16 article-title: Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome publication-title: Scientific Reports doi: 10.1038/s41598-022-07959-x – volume: 50 start-page: 297 year: 2022 ident: 2025062715125311100_B10 article-title: Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data publication-title: Biochem Soc Trans doi: 10.1042/BST20210863 – volume-title: Statistical Power Analysis for the Behavioral Sciences year: 1998 ident: 2025062715125311100_B39 doi: 10.4324/9780203771587 – volume: 184 start-page: 4048 year: 2021 ident: 2025062715125311100_B58 article-title: GABA-receptive microglia selectively sculpt developing inhibitory circuits publication-title: Cell doi: 10.1016/j.cell.2021.06.018 – volume: 25 start-page: 381 year: 2024 ident: 2025062715125311100_B12 article-title: The diversification of methods for studying cell–cell interactions and communication publication-title: Nat Rev Genet doi: 10.1038/s41576-023-00685-8 – volume: 52 start-page: 183 year: 2020 ident: 2025062715125311100_B14 article-title: Immune landscape of viral- and carcinogen-driven head and neck cancer publication-title: Immunity doi: 10.1016/j.immuni.2019.11.014 – volume: 40 start-page: 1503 year: 2022 ident: 2025062715125311100_B47 article-title: High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer publication-title: Cancer Cell doi: 10.1016/j.ccell.2022.10.008 – volume: 11 start-page: 18761 year: 2021 ident: 2025062715125311100_B53 article-title: TDP-43 regulates GAD1 mRNA splicing and GABA signaling in drosophila CNS publication-title: SciRep doi: 10.1038/s41598-021-98241-z – ident: 2025062715125311100_B17 article-title: iTALK: an R package to characterize and illustrate intercellular communication doi: 10.1101/507871 – volume: 5 start-page: 21 year: 2022 ident: 2025062715125311100_B28 article-title: InterCellar enables interactive analysis and exploration of cell−cell communication in single-cell transcriptomic data publication-title: Commun Biol doi: 10.1038/s42003-021-02986-2 – volume: 73 start-page: 886 year: 2012 ident: 2025062715125311100_B2 article-title: Intercellular (Mis)communication in neurodegenerative disease publication-title: Neuron doi: 10.1016/j.neuron.2012.02.017 – volume: 20 start-page: 180 year: 2024 ident: 2025062715125311100_B18 article-title: CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics publication-title: Nat Protoc doi: 10.1038/s41596-024-01045-4 – volume: 187 start-page: 1971 year: 2024 ident: 2025062715125311100_B46 article-title: Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD publication-title: Cell doi: 10.1016/j.cell.2024.02.031 – volume: 6 start-page: e27041 year: 2017 ident: 2025062715125311100_B8 article-title: The human cell atlas publication-title: eLife doi: 10.7554/eLife.27041 – volume: 16 start-page: 278 year: 2015 ident: 2025062715125311100_B37 article-title: MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data publication-title: Genome Biol doi: 10.1186/s13059-015-0844-5 – volume: 13 start-page: 3224 year: 2022 ident: 2025062715125311100_B31 article-title: Comparison of methods and resources for cell–cell communication inference from single-cell RNA-seq data publication-title: NatCommun doi: 10.1038/s41467-022-30755-0 – volume: 33 start-page: 1788 year: 2023 ident: 2025062715125311100_B33 article-title: ESICCC as a systematic computational framework for evaluation, selection, and integration of cell–cell communication inference methods publication-title: Genome Res doi: 10.1101/gr.278001.123 – volume: 14 start-page: 4957 year: 2022 ident: 2025062715125311100_B26 article-title: CellCallEXT: analysis of ligand–receptor and transcription factor activities in cell–cell communication of tumor immune microenvironment publication-title: Cancers doi: 10.3390/cancers14194957 – volume: 36 start-page: 4296 year: 2020 ident: 2025062715125311100_B21 article-title: COMUNET: a tool to explore and visualize intercellular communication publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa482 – volume: 70 start-page: 1009 year: 2013 ident: 2025062715125311100_B55 article-title: An imbalance between excitatory and inhibitory neurotransmitters in amyotrophic lateral sclerosis revealed by use of 3-T proton magnetic resonance spectroscopy publication-title: JAMA Neurol doi: 10.1001/jamaneurol.2013.234 – volume: 5 start-page: 222 year: 2017 ident: 2025062715125311100_B5 article-title: Therapeutic targeting of dysregulated cellular communication publication-title: Ann Transl Med doi: 10.21037/atm.2017.03.62 – volume: 7 start-page: 747 year: 2008 ident: 2025062715125311100_B6 article-title: Stress, drugs, and evolution: the role of cellular signaling in fungal drug resistance publication-title: Eukaryot Cell doi: 10.1128/EC.00041-08 – volume: 12 start-page: 1307 year: 2023 ident: 2025062715125311100_B51 article-title: Microglia and astrocytes in amyotrophic lateral sclerosis: disease-associated states, pathological roles, and therapeutic potential publication-title: Biology doi: 10.3390/biology12101307 – volume: 38 start-page: 4117 year: 2022 ident: 2025062715125311100_B44 article-title: LRLoop: a method to predict feedback loops in cell–cell communication publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac447 – volume: 48 start-page: e55 year: 2020 ident: 2025062715125311100_B36 article-title: SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics publication-title: Nucleic Acids Res doi: 10.1093/nar/gkaa183 – volume: 42 start-page: 470 year: 2023 ident: 2025062715125311100_B42 article-title: Comparative analysis of cell–cell communication at single-cell resolution publication-title: Nat Biotechnol doi: 10.1038/s41587-023-01782-z – volume-title: 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), Turin year: 2025 ident: 2025062715125311100_B30 article-title: quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data doi: 10.1109/PDP66500.2025.00045 – volume: 26 start-page: 12 year: 2021 ident: 2025062715125311100_B11 article-title: The landscape of cell–cell communication through single-cell transcriptomics publication-title: Curr Opin Syst Biol doi: 10.1016/j.coisb.2021.03.007 – volume: 2 start-page: vbac092 year: 2022 ident: 2025062715125311100_B4 article-title: MAST: a hybrid multi-agent spatio-temporal model of tumor microenvironment informed using a data-driven approach publication-title: Bioinform Adv doi: 10.1093/bioadv/vbac092 – volume: 14 start-page: 1128 year: 2023 ident: 2025062715125311100_B48 article-title: Inferring neuron–neuron communications from single-cell transcriptomics through NeuronChat publication-title: NatCommun doi: 10.1038/s41467-023-36800-w – volume: 9 start-page: 823 year: 2017 ident: 2025062715125311100_B54 article-title: Amyotrophic lateral sclerosis, gene deregulation in the anterior horn of the spinal cord and frontal cortex area 8: implications in frontotemporal lobar degeneration publication-title: Aging doi: 10.18632/aging.101195 – volume: 9 start-page: 2095 year: 2023 ident: 2025062715125311100_B9 article-title: Time series single-cell transcriptional atlases reveal cell fate differentiation driven by light in Arabidopsis seedlings publication-title: Nat Plants doi: 10.1038/s41477-023-01544-4 – volume: 12 start-page: 1088 year: 2021 ident: 2025062715125311100_B23 article-title: Inference and analysis of cell–cell communication using CellChat publication-title: NatCommun doi: 10.1038/s41467-021-21246-9 – volume: 72 start-page: 28 year: 2018 ident: 2025062715125311100_B41 article-title: Extending R with C++: a brief introduction to Rcpp publication-title: Am Stat doi: 10.1080/00031305.2017.1375990 – volume: 94 start-page: 10925 year: 1997 ident: 2025062715125311100_B56 article-title: Reduced stress defense in heme oxygenase 1-deficient cells publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.94.20.10925 – volume: 26 start-page: 429 year: 2020 ident: 2025062715125311100_B3 article-title: Exploring dysregulated signaling pathways in cancer publication-title: Curr Pharm Des doi: 10.2174/1381612826666200115095937 – volume: 12 start-page: 5692 year: 2021 ident: 2025062715125311100_B40 article-title: Confronting false discoveries in single-cell differential expression publication-title: NatCommun doi: 10.1038/s41467-021-25960-2 – volume: 22 start-page: 71 year: 2020 ident: 2025062715125311100_B64 article-title: Deciphering cell–cell interactions and communication from gene expression publication-title: Nat Rev Genet doi: 10.1038/s41576-020-00292-x – volume: 38 start-page: 1920 year: 2022 ident: 2025062715125311100_B34 article-title: Identify, quantify and characterize cellular communication from single cell RNA sequencing data with scSeqComm publication-title: Bioinformatics doi: 10.1093/bioinformatics/btac036 – volume: 37 start-page: 4263 year: 2021 ident: 2025062715125311100_B22 article-title: CrossTalkeR: analysis and visualization of ligand–receptor networks publication-title: Bioinformatics doi: 10.1093/bioinformatics/btab370 – volume: 26 start-page: 1613 year: 2024 ident: 2025062715125311100_B25 article-title: LIANA+ provides an all-in-one framework for cell–cell communication inference publication-title: Nat Cell Biol doi: 10.1038/s41556-024-01469-w – volume: 48 start-page: D498 year: 2020 ident: 2025062715125311100_B50 article-title: The reactome pathway knowledgebase publication-title: Nucleic Acids Res – volume: 26 start-page: 1512 year: 2016 ident: 2025062715125311100_B62 article-title: Altered functionality, morphology, and vesicular glutamate transporter expression of cortical motor neurons from a presymptomatic mouse model of amyotrophic lateral sclerosis publication-title: Cereb Cortex doi: 10.1093/cercor/bhu317 – volume: 28 start-page: 27 year: 2000 ident: 2025062715125311100_B49 article-title: KEGG: Kyoto Encyclopedia of Genes and Genomes publication-title: Nucleic Acids Res doi: 10.1093/nar/28.1.27 – volume: 116 start-page: 1169 year: 2009 ident: 2025062715125311100_B59 article-title: Concentrations of beta-amyloid precursor protein processing products in cerebrospinal fluid of patients with amyotrophic lateral sclerosis and frontotemporal lobar degeneration publication-title: J Neural Transm doi: 10.1007/s00702-009-0271-4 – volume: 92 start-page: 139 year: 2021 ident: 2025062715125311100_B61 article-title: Glutaminase in microglia: a novel regulator of neuroinflammation publication-title: Brain Behav Immun doi: 10.1016/j.bbi.2020.11.038 – volume: 12 start-page: 1089 year: 2021 ident: 2025062715125311100_B20 article-title: Dissection of intercellular communication using the transcriptome-based framework ICELLNET publication-title: NatCommun doi: 10.1038/s41467-021-21244-x – volume: 17 start-page: 1015 year: 2022 ident: 2025062715125311100_B1 article-title: Significance of intercellular communication for neurodegenerative diseases publication-title: Neural Regen Res doi: 10.4103/1673-5374.324840 – ident: 2025062715125311100_B27 article-title: MultiNicheNet: a flexible framework for differential cell–cell communication analysis from multi-sample multi-condition single-cell transcriptomics data doi: 10.1101/2023.06.13.544751 – volume: 55 start-page: 1 year: 2013 ident: 2025062715125311100_B35 article-title: Scalable Strategies for Computing with Massive Data publication-title: J Stat Softw doi: 10.18637/jss.v055.i14 – volume: 3 start-page: 1446 year: 2023 ident: 2025062715125311100_B19 article-title: scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication publication-title: Nat Aging doi: 10.1038/s43587-023-00514-x – volume: 23 start-page: 218 year: 2022 ident: 2025062715125311100_B32 article-title: Evaluation of cell–cell interaction methods by integrating single-cell RNA sequencing data with spatial information publication-title: Genome Biol doi: 10.1186/s13059-022-02783-y – volume: 1762 start-page: 1068 year: 2006 ident: 2025062715125311100_B52 article-title: The role of excitotoxicity in the pathogenesis of amyotrophic lateral sclerosis publication-title: Biochim Biophys Acta doi: 10.1016/j.bbadis.2006.05.002 – volume: 21 start-page: 923 year: 2023 ident: 2025062715125311100_B60 article-title: The paradigm of amyloid precursor protein in amyotrophic lateral sclerosis: the potential role of the 682YENPTY687 motif publication-title: Comput Struct Biotechnol J doi: 10.1016/j.csbj.2023.01.008 – volume: 608 start-page: 766 year: 2022 ident: 2025062715125311100_B43 article-title: Spatial multi-omic map of human myocardial infarction publication-title: Nature doi: 10.1038/s41586-022-05060-x – volume: 27 start-page: 2354 year: 2024 ident: 2025062715125311100_B45 article-title: Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions publication-title: Nat Neurosci doi: 10.1038/s41593-024-01796-z – volume: 376 start-page: eabl4896 year: 2022 ident: 2025062715125311100_B7 article-title: The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans publication-title: Science doi: 10.1126/science.abl4896 – volume: 23 start-page: bbac234 year: 2022 ident: 2025062715125311100_B63 article-title: Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies publication-title: Brief Bioinform doi: 10.1093/bib/bbac234 – volume: 11 start-page: 381 year: 2020 ident: 2025062715125311100_B13 article-title: Predicting cell-to-cell communication networks using NATMI publication-title: Nat Commun doi: 10.1038/s41467-020-18873-z |
| SSID | ssj0002545401 |
| Score | 2.293035 |
| Snippet | Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell–cell communication, which plays a... Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a... Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a... |
| SourceID | proquest pubmed crossref |
| SourceType | Aggregation Database Index Database |
| StartPage | lqaf084 |
| SubjectTerms | Cell Communication - genetics Computational Biology - methods Gene Expression Profiling - methods Humans Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software Transcriptome |
| Title | Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40585304 https://www.proquest.com/docview/3225872435 |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fa9swEBZZ-7KX0bFf2bqgwcoeitpItmX7MUu7lcHCCC30zUiONAKps9X2S_6G_dG7sxTbGRl0I2CMjC-J7kO6O313R8j7WIkwtknKAmkDFmohWSqEZrkCW0TEymqB-c5fZ_LqJvxyG90OBr96rKW60mf5Zm9eyf9oFcZAr5gl-w-abYXCANyDfuEKGobrg3R84bubVBj2xhB8wynN-zkfDdvKVZK1Wx5WQy1cIQWclaAic4rxgpVhKOF0Ppswz6_GKILPXWsN2Nlkjl2XMZfZVXfWy7Uvvlr1iPNTUyqXQfN5Wa-Wndev7uvNxj-Axfhu3RE8yhLR2jxS3RsXy5OpOJmMpxiJcedE_oykH7AQUUescuuakAEHQLhuOmdmz5hfmOMe_sTe5d6VwiqwJbCGm9VPZceu49xuZe0_dryWh-hO4IPMScj8-4_IoQCnQ_RiP7ivgysN5i168O2vbauABudOxLkXsWvl_MV1aUyY6yPyxPsedOKA9JQMTPGMfO-DiG5BRHdARFsQ0RZEFHROeyCiPRDRXRBRBNFzcvPp8np6xXz3DZaLVFRMc2NEYHkOHzBaFJiGPB6HCh14HsdcWa6kMUrJNFlYlUvNpYQRG3BhtcqDF-SgWBfmFaE2iRT6IVxHeRhKnYZ6LAz2UlgkabTgQ_JhO1vZD1dkJduvmiF5t53MDNZB_EuqMOu6zHBjSmIB1v-QvHSz3MoCpwSs0nH4-sHf84Y87qB7TA6q-9q8Beuz0iNy-PFy9m0-aqI3owYivwHSX4_3 |
| linkProvider | National Library of Medicine |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Differential+cellular+communication+inference+framework+for+large-scale+single-cell+RNA-sequencing+data&rft.jtitle=NAR+genomics+and+bioinformatics&rft.au=Cesaro%2C+Giulia&rft.au=Baruzzo%2C+Giacomo&rft.au=Tussardi%2C+Gaia&rft.au=Di%C2%A0Camillo%2C+Barbara&rft.date=2025-06-01&rft.issn=2631-9268&rft.eissn=2631-9268&rft.volume=7&rft.issue=2&rft_id=info:doi/10.1093%2Fnargab%2Flqaf084&rft.externalDBID=n%2Fa&rft.externalDocID=10_1093_nargab_lqaf084 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2631-9268&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2631-9268&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2631-9268&client=summon |