CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics
Abstract Motivation Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Ma...
Uloženo v:
| Vydáno v: | Bioinformatics (Oxford, England) Ročník 39; číslo Supplement_1; s. i484 - i493 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Oxford University Press
30.06.2023
|
| Témata: | |
| ISSN: | 1367-4803, 1367-4811, 1367-4811 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Abstract
Motivation
Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.
Results
We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.
Availability and implementation
The source code and data is available at https://github.com/MihirBafna/CLARIFY. |
|---|---|
| AbstractList | Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.
We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.
The source code and data is available at https://github.com/MihirBafna/CLARIFY. Abstract Motivation Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. Results We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. Availability and implementation The source code and data is available at https://github.com/MihirBafna/CLARIFY. Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.MOTIVATIONGene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.RESULTSWe propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.The source code and data is available at https://github.com/MihirBafna/CLARIFY.AVAILABILITY AND IMPLEMENTATIONThe source code and data is available at https://github.com/MihirBafna/CLARIFY. |
| Author | Li, Hechen Zhang, Xiuwei Bafna, Mihir |
| Author_xml | – sequence: 1 givenname: Mihir surname: Bafna fullname: Bafna, Mihir email: mbafna@gatech.edu – sequence: 2 givenname: Hechen surname: Li fullname: Li, Hechen – sequence: 3 givenname: Xiuwei surname: Zhang fullname: Zhang, Xiuwei email: xiuwei.zhang@gatech.edu |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37387180$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUU1v1DAQtVAR_YC_UPnIZakdJ3GMkFC1olBpJSQEB06WY08Wg2MH22m1N_4D_5BfgqPdVpQLnGY08968pzen6MgHDwidU_KCEsEuehusH0IcVbY6XfRZmaoVj9AJZS1f1R2lR_c9YcfoNKWvhJCGNO0TdMw46zjtyAkK683lh-urzy-xBud-_fi5FGx9hqh0tsFj5Q3eggccYTs7lUPcYQ_5NsRvZTRYDyP4jIcYRpymYkc5tyubFNwNGJyj8klHO-UwFqdP0eNBuQTPDvUMfbp683H9brV5__Z6fblZ6brheSUqxQWvjBENUxUhTPd6ED0B0MPQ9XW7dHVnTAV8EEbQWpjOKFBE9ZqzgZ2h1_u709yPYHSxGJWTU7SjijsZlJUPN95-kdtwIylhlDLKyoXnhwsxfJ8hZTnatKSjPIQ5yapjVcNb3tACPf9T7F7lLuYCeLUH6BhSKqlJbbNa4i3a1hVRuXxVPvyqPHy10Nu_6HcK_yTSPTHM0_9yfgNX5cbN |
| CitedBy_id | crossref_primary_10_1186_s13073_024_01282_y crossref_primary_10_1016_j_jmb_2024_168543 crossref_primary_10_1038_s41576_023_00685_8 crossref_primary_10_3390_ijms26093997 crossref_primary_10_1093_bib_bbaf236 crossref_primary_10_1038_s44320_025_00088_3 crossref_primary_10_1002_advs_202403572 crossref_primary_10_1093_bioinformatics_btaf057 crossref_primary_10_1007_s10462_025_11257_z crossref_primary_10_1038_s41592_025_02721_3 crossref_primary_10_3390_cimb46050298 crossref_primary_10_1002_ctm2_70331 crossref_primary_10_52601_bpr_2024_240006 crossref_primary_10_1049_syb2_70000 crossref_primary_10_1016_j_drudis_2024_103889 crossref_primary_10_1093_bioinformatics_btaf254 crossref_primary_10_1093_bib_bbaf109 crossref_primary_10_1016_j_cels_2025_101243 |
| Cites_doi | 10.1038/s41551-022-00951-w 10.1126/science.aat5691 10.1038/s41576-020-00292-x 10.1016/j.bpj.2011.11.4022 10.1186/s13059-020-02214-w 10.1038/s41467-020-15968-5 10.1126/science.aaw1219 10.1371/journal.pone.0012776 10.1186/s13059-022-02692-0 10.1093/bib/bbaa269 10.1016/j.coisb.2021.03.007 10.1038/s41467-022-30755-0 10.1038/s41586-019-1049-y 10.1038/nmeth.2892 10.1038/s41467-022-32111-8 10.1016/j.sbi.2021.09.003 10.1073/pnas.0408031102 10.1038/s41596-020-0292-x 10.1038/s41592-019-0690-6 10.1016/j.celrep.2021.109211 10.1016/j.coisb.2017.04.001 10.1038/s41588-021-00972-2 10.1038/s41551-022-00997-w 10.1016/j.cell.2018.01.015 10.1038/s41551-022-00942-x 10.1126/science.aau5324 10.1126/science.aaf2403 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023. Published by Oxford University Press. 2023 The Author(s) 2023. Published by Oxford University Press. |
| Copyright_xml | – notice: The Author(s) 2023. Published by Oxford University Press. 2023 – notice: The Author(s) 2023. Published by Oxford University Press. |
| DBID | TOX AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| DOI | 10.1093/bioinformatics/btad269 |
| DatabaseName | Oxford Journals Open Access Collection CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| DocumentTitleAlternate | ISMB/ECCB 2023 Proceedings |
| EISSN | 1367-4811 |
| EndPage | i493 |
| ExternalDocumentID | PMC10311313 37387180 10_1093_bioinformatics_btad269 10.1093/bioinformatics/btad269 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NIGMS NIH HHS grantid: R35 GM143070 – fundername: ; grantid: R35GM143070 – fundername: ; grantid: DBI-2019771 |
| GroupedDBID | --- -E4 -~X .-4 .2P .DC .GJ .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AASNB AAUQX AAVAP AAVLN ABEFU ABEUO ABIXL ABNKS ABPTD ABQLI ABQTQ ABWST ABXVV ABZBJ ACGFS ACIWK ACMRT ACPRK ACUFI ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADOCK ADPDF ADRDM ADRIX ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFNX AFFZL AFGWE AFIYH AFOFC AFRAH AFXEN AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AI. AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC APIBT APWMN AQDSO ARIXL ASPBG ATTQO AVWKF AXUDD AYOIW AZFZN AZVOD BAWUL BAYMD BCRHZ BHONS BQDIO BQUQU BSWAC BTQHN C1A C45 CAG CDBKE COF CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD ELUNK EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HVGLF HW0 HZ~ IOX J21 JXSIZ KAQDR KC5 KOP KQ8 KSI KSN M-Z M49 MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NTWIH NU- NVLIB O0~ O9- OAWHX ODMLO OJQWA OK1 OVD OVEED O~Y P2P PAFKI PB- PEELM PQQKQ Q1. Q5Y R44 RD5 RIG RNI RNS ROL ROX RPM RUSNO RW1 RXO RZF RZO SV3 TEORI TJP TLC TOX TR2 VH1 W8F WOQ X7H XJT YAYTL YKOAZ YXANX ZGI ZKX ~91 ~KM AAYXX ABEJV ABGNP ADMLS AMNDL CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM |
| ID | FETCH-LOGICAL-c457t-92a7972dd953a2003cbcf9b0eecff8b460eec48dd2e7f9d9149d8daea0abc73f3 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001027457000057&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1367-4803 1367-4811 |
| IngestDate | Thu Aug 21 18:37:39 EDT 2025 Wed Oct 01 13:51:04 EDT 2025 Thu May 08 07:32:57 EDT 2025 Tue Nov 18 22:34:18 EST 2025 Sat Nov 29 03:49:27 EST 2025 Wed Aug 28 03:15:05 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | Supplement_1 |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 The Author(s) 2023. Published by Oxford University Press. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c457t-92a7972dd953a2003cbcf9b0eecff8b460eec48dd2e7f9d9149d8daea0abc73f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://dx.doi.org/10.1093/bioinformatics/btad269 |
| PMID | 37387180 |
| PQID | 2832576751 |
| PQPubID | 23479 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_10311313 proquest_miscellaneous_2832576751 pubmed_primary_37387180 crossref_citationtrail_10_1093_bioinformatics_btad269 crossref_primary_10_1093_bioinformatics_btad269 oup_primary_10_1093_bioinformatics_btad269 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-06-30 |
| PublicationDateYYYYMMDD | 2023-06-30 |
| PublicationDate_xml | – month: 06 year: 2023 text: 2023-06-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Bioinformatics (Oxford, England) |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2023 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Almet (2023063008162088400_btad269-B1) 2021; 26 Rouault (2023063008162088400_btad269-B20) 2012; 102 Levine (2023063008162088400_btad269-B12) 2005; 102 Pratapa (2023063008162088400_btad269-B18) 2020; 17 Yuan (2023063008162088400_btad269-B28) 2020; 21 Kipf (2023063008162088400_btad269-B11) Wang (2023063008162088400_btad269-B25) 2018; 361 Dimitrov (2023063008162088400_btad269-B5) 2022; 13 Efremova (2023063008162088400_btad269-B6) 2020; 15 Moffitt (2023063008162088400_btad269-B17) 2018; 362 Wang (2023063008162088400_btad269-B26) 2021; 35 Grover (2023063008162088400_btad269-B9) 2016 Shao (2023063008162088400_btad269-B21) 2022; 13 Zeng (2023063008162088400_btad269-B29) 2022; 72 Rodriques (2023063008162088400_btad269-B19) 2019; 363 Ståhl (2023063008162088400_btad269-B23) 2016; 353 Armingol (2023063008162088400_btad269-B2) 2021; 22 Eng (2023063008162088400_btad269-B7) 2019; 568 Tie (2023063008162088400_btad269-B24) 2022; 6 Zhang (2023063008162088400_btad269-B31) 2022; 13 Wu (2023063008162088400_btad269-B27) 2022; 6 Li (2023063008162088400_btad269-B14) 2022; 6 Garcia-Alonso (2023063008162088400_btad269-B8) 2021; 53 Huynh-Thu (2023063008162088400_btad269-B10) 2010; 5 Zhang (2023063008162088400_btad269-B32) 2022 Li (2023063008162088400_btad269-B15) 2022; 23 Li (2023063008162088400_btad269-B13) 2022 Shao (2023063008162088400_btad269-B22) 2021; 22 Lubeck (2023063008162088400_btad269-B16) 2014; 11 Chasman (2023063008162088400_btad269-B4) 2017; 2 Zhang (2023063008162088400_btad269-B30) 2023 Cang (2023063008162088400_btad269-B3) 2020; 11 Zhou (2023063008162088400_btad269-B33) 2018; 172 |
| References_xml | – volume: 6 start-page: 1435 year: 2022 ident: 2023063008162088400_btad269-B27 article-title: Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens publication-title: Nat Biomed Eng doi: 10.1038/s41551-022-00951-w – volume: 361 start-page: eaat5691 year: 2018 ident: 2023063008162088400_btad269-B25 article-title: Three-dimensional intact-tissue sequencing of single-cell transcriptional states publication-title: Science doi: 10.1126/science.aat5691 – volume: 22 start-page: 71 year: 2021 ident: 2023063008162088400_btad269-B2 article-title: Deciphering cell-cell interactions and communication from gene expression publication-title: Nat Rev Genet doi: 10.1038/s41576-020-00292-x – volume: 102 start-page: 417 year: 2012 ident: 2023063008162088400_btad269-B20 article-title: Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks publication-title: Biophys J doi: 10.1016/j.bpj.2011.11.4022 – volume: 21 start-page: 1 year: 2020 ident: 2023063008162088400_btad269-B28 article-title: Gcng: graph convolutional networks for inferring gene interaction from spatial transcriptomics data publication-title: Genome Biol doi: 10.1186/s13059-020-02214-w – volume: 11 start-page: 1 year: 2020 ident: 2023063008162088400_btad269-B3 article-title: Inferring spatial and signaling relationships between cells from single cell transcriptomic data publication-title: Nat Commun doi: 10.1038/s41467-020-15968-5 – volume: 363 start-page: 1463 year: 2019 ident: 2023063008162088400_btad269-B19 article-title: Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution publication-title: Science doi: 10.1126/science.aaw1219 – volume: 5 start-page: e12776 year: 2010 ident: 2023063008162088400_btad269-B10 article-title: Inferring regulatory networks from expression data using tree-based methods publication-title: PLoS One doi: 10.1371/journal.pone.0012776 – volume: 23 start-page: 1 year: 2022 ident: 2023063008162088400_btad269-B15 article-title: De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with deeplinc publication-title: Genome Biol doi: 10.1186/s13059-022-02692-0 – volume: 22 start-page: bbaa269 year: 2021 ident: 2023063008162088400_btad269-B22 article-title: Celltalkdb: a manually curated database of ligand–receptor interactions in humans and mice publication-title: Brief Bioinform doi: 10.1093/bib/bbaa269 – year: 2022 ident: 2023063008162088400_btad269-B13 – volume: 26 start-page: 12 year: 2021 ident: 2023063008162088400_btad269-B1 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: 13 start-page: 1 year: 2022 ident: 2023063008162088400_btad269-B5 article-title: Comparison of methods and resources for cell-cell communication inference from single-cell RNA-seq data publication-title: Nat Commun doi: 10.1038/s41467-022-30755-0 – volume: 568 start-page: 235 year: 2019 ident: 2023063008162088400_btad269-B7 article-title: Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH publication-title: Nature doi: 10.1038/s41586-019-1049-y – volume: 11 start-page: 360 year: 2014 ident: 2023063008162088400_btad269-B16 article-title: Single-cell in situ RNA profiling by sequential hybridization publication-title: Nat Methods doi: 10.1038/nmeth.2892 – volume: 13 start-page: 4429 year: 2022 ident: 2023063008162088400_btad269-B21 article-title: Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk publication-title: Nat Commun doi: 10.1038/s41467-022-32111-8 – volume: 72 start-page: 114 year: 2022 ident: 2023063008162088400_btad269-B29 article-title: Toward better drug discovery with knowledge graph publication-title: Curr Opin Struct Biol doi: 10.1016/j.sbi.2021.09.003 – volume: 102 start-page: 4936 year: 2005 ident: 2023063008162088400_btad269-B12 article-title: Gene regulatory networks for development publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0408031102 – year: 2022 ident: 2023063008162088400_btad269-B32 – volume: 13 start-page: 1 year: 2022 ident: 2023063008162088400_btad269-B31 article-title: Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease publication-title: Nat Commun – volume: 15 start-page: 1484 year: 2020 ident: 2023063008162088400_btad269-B6 article-title: CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes publication-title: Nat Protoc doi: 10.1038/s41596-020-0292-x – volume: 17 start-page: 147 year: 2020 ident: 2023063008162088400_btad269-B18 article-title: Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data publication-title: Nat Methods doi: 10.1038/s41592-019-0690-6 – ident: 2023063008162088400_btad269-B11 – volume: 35 start-page: 109211 year: 2021 ident: 2023063008162088400_btad269-B26 article-title: Cell-type-specific gene regulatory networks underlying murine neonatal heart regeneration at single-cell resolution publication-title: Cell Rep doi: 10.1016/j.celrep.2021.109211 – volume: 2 start-page: 130 year: 2017 ident: 2023063008162088400_btad269-B4 article-title: Inference of cell type specific regulatory networks on mammalian lineages publication-title: Curr Opin Syst Biol doi: 10.1016/j.coisb.2017.04.001 – volume: 53 start-page: 1698 year: 2021 ident: 2023063008162088400_btad269-B8 article-title: Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro publication-title: Nat Genet doi: 10.1038/s41588-021-00972-2 – year: 2023 ident: 2023063008162088400_btad269-B30 – volume: 6 start-page: 1319 year: 2022 ident: 2023063008162088400_btad269-B24 article-title: Contextual learning is nearly all you need publication-title: Nat Biomed Eng doi: 10.1038/s41551-022-00997-w – volume: 172 start-page: 744 year: 2018 ident: 2023063008162088400_btad269-B33 article-title: Circuit design features of a stable two-cell system publication-title: Cell doi: 10.1016/j.cell.2018.01.015 – start-page: 855 year: 2016 ident: 2023063008162088400_btad269-B9 – volume: 6 start-page: 1353 year: 2022 ident: 2023063008162088400_btad269-B14 article-title: Graph representation learning in biomedicine and healthcare publication-title: Nat Biomed Eng doi: 10.1038/s41551-022-00942-x – volume: 362 start-page: eaau5324 year: 2018 ident: 2023063008162088400_btad269-B17 article-title: Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region publication-title: Science doi: 10.1126/science.aau5324 – volume: 353 start-page: 78 year: 2016 ident: 2023063008162088400_btad269-B23 article-title: Visualization and analysis of gene expression in tissue sections by spatial transcriptomics publication-title: Science doi: 10.1126/science.aaf2403 |
| SSID | ssj0005056 |
| Score | 2.5486271 |
| Snippet | Abstract
Motivation
Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take... Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and... |
| SourceID | pubmedcentral proquest pubmed crossref oup |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | i484 |
| SubjectTerms | Benchmarking Cell Communication Gene Expression Profiling Gene Regulatory Networks Systems Biology and Networks Transcriptome |
| Title | CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37387180 https://www.proquest.com/docview/2832576751 https://pubmed.ncbi.nlm.nih.gov/PMC10311313 |
| Volume | 39 |
| WOSCitedRecordID | wos001027457000057&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: DOA dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4803 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9wwEB7S0EIvfTfdtFlU6Klg1rYsS-othC4plLSUFLYnoyddWOzidQJ763_oP-wvicbybuNA6eNkYz0Q0sgzGs18H8Aryxh1hbaJL9B1UyqXKB8MOW-t90yynKlINsHPzsRiIT_uQbbNhbl5hS_pTC-bAUQUgYtnulM2LzFlL2MCJfv8w-JXUEfa87UiDllSiJRuc4J_281IHY1S3K5ZmjcDJq9poPn9_xj7A7g3mJvkOMrHQ9hz9SO4EwkoN4-hOXl__Ond_Msbgg78n99_4IMghEQbEx6Iqi0JMuZIG0nrm3ZD6hg6Hj75YKOie5FglgpZY3S2Wq02oSSI9KWzpENV2P-YMPt5_QQ-z9-en5wmAwVDYgrGu0TmikueWysZVRjHZrTxUqfOGe-FLkp8K4S1ueNeWhnOW1ZY5VSqtOHU06ewXze1ewYkc2WpvTDG8KJQ0mrmdcp1mWvuvWRuAmy7EpUZ8MmRJmNVxXtyWo0nsxomcwKzXbtvEaHjjy1eh4X-68ovt_JQhZ2Hy6Bq11ysKyR5YoiFk03gIMrHrk_EiwpaP52AGEnOrgKieo9L6uXXHt0beTcymtHDfxnlc7ibB-srBjK-gP2uvXBHcNtcdst1O4VbfCGmvbth2u-XKzpII1g |
| linkProvider | Oxford University Press |
| 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=CLARIFY%3A+cell%E2%80%93cell+interaction+and+gene+regulatory+network+refinement+from+spatially+resolved+transcriptomics&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Bafna%2C+Mihir&rft.au=Li%2C+Hechen&rft.au=Zhang%2C+Xiuwei&rft.date=2023-06-30&rft.pub=Oxford+University+Press&rft.issn=1367-4803&rft.eissn=1367-4811&rft.volume=39&rft.issue=Suppl+1&rft.spage=i484&rft.epage=i493&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtad269&rft_id=info%3Apmid%2F37387180&rft.externalDocID=PMC10311313 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |