SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks
Abstract Summary Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural...
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| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2024-02, Vol.40 (2) |
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| creator | Mañanes, Diego Rivero-García, Inés Relaño, Carlos Torres, Miguel Sancho, David Jimenez-Carretero, Daniel Torroja, Carlos Sánchez-Cabo, Fátima |
| description | Abstract
Summary
Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools.
Availability and implementation
The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS. |
| doi_str_mv | 10.1093/bioinformatics/btae072 |
| format | Article |
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Summary
Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools.
Availability and implementation
The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btae072</identifier><identifier>PMID: 38366652</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Applications Note ; Availability ; Cellular structure ; Deconvolution ; Gene Expression Profiling ; Gene sequencing ; Neural networks ; Neural Networks, Computer ; Software ; Spatial data ; Transcriptomics</subject><ispartof>Bioinformatics (Oxford, England), 2024-02, Vol.40 (2)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-edd26b28e8454037deb5c7f6abe5781175ec9187fb9a1bfbc6553178afe78b163</citedby><cites>FETCH-LOGICAL-c485t-edd26b28e8454037deb5c7f6abe5781175ec9187fb9a1bfbc6553178afe78b163</cites><orcidid>0000-0003-1881-1664</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10881086/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10881086/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38366652$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Birol, Inanc</contributor><creatorcontrib>Mañanes, Diego</creatorcontrib><creatorcontrib>Rivero-García, Inés</creatorcontrib><creatorcontrib>Relaño, Carlos</creatorcontrib><creatorcontrib>Torres, Miguel</creatorcontrib><creatorcontrib>Sancho, David</creatorcontrib><creatorcontrib>Jimenez-Carretero, Daniel</creatorcontrib><creatorcontrib>Torroja, Carlos</creatorcontrib><creatorcontrib>Sánchez-Cabo, Fátima</creatorcontrib><title>SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks</title><title>Bioinformatics (Oxford, England)</title><addtitle>Bioinformatics</addtitle><description>Abstract
Summary
Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools.
Availability and implementation
The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.</description><subject>Algorithms</subject><subject>Applications Note</subject><subject>Availability</subject><subject>Cellular structure</subject><subject>Deconvolution</subject><subject>Gene Expression Profiling</subject><subject>Gene sequencing</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Software</subject><subject>Spatial data</subject><subject>Transcriptomics</subject><issn>1367-4803</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNqNkU1v1DAQhi1ERUvhL1SWuHBZasfxR7gg1PIlrVSJwomDZTuTxW1iB9sp4t_Xq11WLScOI4_kZ96ZVy9CZ5S8oaRj59ZHH4aYJlO8y-e2GCCyeYJOKBNy1SpKnx56wo7R85xvCCGccPEMHTPFhBC8OUE_rueqYMbLy_X1W2wC_opn427NBnCJuAcXw10clwI470BckgnZJT-XONXVuDfF4CX7sMEBllSJAOV3TLf5BToazJjh5f49Rd8_fvh28Xm1vvr05eL9euVaxcsK-r4RtlGgWt4SJnuw3MlBGAtcViOSg-uokoPtDLWDdYJzRqUyA0hlqWCn6N1Od17sBL2DUG8c9Zz8ZNIfHY3Xj3-C_6k38U5TolStrcLrvUKKvxbIRU8-OxhHEyAuWTddo5qWEbZFX_2D3sQlhepPM9q0LetERysldpRLMecEw-EaSvQ2QP04QL0PsA6ePfRyGPubWAXoDojL_L-i99y7sIY</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Mañanes, Diego</creator><creator>Rivero-García, Inés</creator><creator>Relaño, Carlos</creator><creator>Torres, Miguel</creator><creator>Sancho, David</creator><creator>Jimenez-Carretero, Daniel</creator><creator>Torroja, Carlos</creator><creator>Sánchez-Cabo, Fátima</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1881-1664</orcidid></search><sort><creationdate>20240201</creationdate><title>SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks</title><author>Mañanes, Diego ; Rivero-García, Inés ; Relaño, Carlos ; Torres, Miguel ; Sancho, David ; Jimenez-Carretero, Daniel ; Torroja, Carlos ; Sánchez-Cabo, Fátima</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c485t-edd26b28e8454037deb5c7f6abe5781175ec9187fb9a1bfbc6553178afe78b163</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Applications Note</topic><topic>Availability</topic><topic>Cellular structure</topic><topic>Deconvolution</topic><topic>Gene Expression Profiling</topic><topic>Gene sequencing</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Software</topic><topic>Spatial data</topic><topic>Transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mañanes, Diego</creatorcontrib><creatorcontrib>Rivero-García, Inés</creatorcontrib><creatorcontrib>Relaño, Carlos</creatorcontrib><creatorcontrib>Torres, Miguel</creatorcontrib><creatorcontrib>Sancho, David</creatorcontrib><creatorcontrib>Jimenez-Carretero, Daniel</creatorcontrib><creatorcontrib>Torroja, Carlos</creatorcontrib><creatorcontrib>Sánchez-Cabo, Fátima</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mañanes, Diego</au><au>Rivero-García, Inés</au><au>Relaño, Carlos</au><au>Torres, Miguel</au><au>Sancho, David</au><au>Jimenez-Carretero, Daniel</au><au>Torroja, Carlos</au><au>Sánchez-Cabo, Fátima</au><au>Birol, Inanc</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2024-02-01</date><risdate>2024</risdate><volume>40</volume><issue>2</issue><issn>1367-4803</issn><eissn>1367-4811</eissn><abstract>Abstract
Summary
Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools.
Availability and implementation
The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>38366652</pmid><doi>10.1093/bioinformatics/btae072</doi><orcidid>https://orcid.org/0000-0003-1881-1664</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics (Oxford, England), 2024-02, Vol.40 (2) |
| issn | 1367-4803 1367-4811 |
| language | eng |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms Applications Note Availability Cellular structure Deconvolution Gene Expression Profiling Gene sequencing Neural networks Neural Networks, Computer Software Spatial data Transcriptomics |
| title | SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks |
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