Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model
Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing techno...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/14/7/1368 |
| _version_ | 1797589151478972416 |
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| author | Dongyuan Wu Jeremy T. Gaskins Michael Sekula Susmita Datta |
| author_facet | Dongyuan Wu Jeremy T. Gaskins Michael Sekula Susmita Datta |
| author_sort | Dongyuan Wu |
| collection | DOAJ |
| description | Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell–cell communication and provide a robust and straightforward result. |
| first_indexed | 2024-03-11T01:02:19Z |
| format | Article |
| id | doaj.art-147c5483d6be441e8e536feb067fbeb5 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-11T01:02:19Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-147c5483d6be441e8e536feb067fbeb52023-11-18T19:29:23ZengMDPI AGGenes2073-44252023-06-01147136810.3390/genes14071368Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie ModelDongyuan Wu0Jeremy T. Gaskins1Michael Sekula2Susmita Datta3Department of Biostatistics, University of Florida, Gainesville, FL 32603, USADepartment of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USADepartment of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USADepartment of Biostatistics, University of Florida, Gainesville, FL 32603, USACellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell–cell communication and provide a robust and straightforward result.https://www.mdpi.com/2073-4425/14/7/1368cellular communicationspatial transcriptomicsgeneralized linear regression modelBayesian modelingTweedie distribution |
| spellingShingle | Dongyuan Wu Jeremy T. Gaskins Michael Sekula Susmita Datta Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model Genes cellular communication spatial transcriptomics generalized linear regression model Bayesian modeling Tweedie distribution |
| title | Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model |
| title_full | Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model |
| title_fullStr | Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model |
| title_full_unstemmed | Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model |
| title_short | Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model |
| title_sort | inferring cell cell communications from spatially resolved transcriptomics data using a bayesian tweedie model |
| topic | cellular communication spatial transcriptomics generalized linear regression model Bayesian modeling Tweedie distribution |
| url | https://www.mdpi.com/2073-4425/14/7/1368 |
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