Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916810343841792 |
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| author | Emons, Martin Gunz, Samuel Crowell, Helena L. Mallona, Izaskun Furrer, Reinhard Robinson, Mark D. |
| author_facet | Emons, Martin Gunz, Samuel Crowell, Helena L. Mallona, Izaskun Furrer, Reinhard Robinson, Mark D. |
| contents | Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patterns and lattice data, and there is a wide range of tools available. This paper discusses the application of spatial statistics to spatially-resolved omics data and in particular, discusses various advantages, challenges, and nuances. This work is accompanied by a vignette, pasta, that showcases the usefulness of spatial statistics in biology using several R packages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01561 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta Emons, Martin Gunz, Samuel Crowell, Helena L. Mallona, Izaskun Furrer, Reinhard Robinson, Mark D. Quantitative Methods Genomics Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patterns and lattice data, and there is a wide range of tools available. This paper discusses the application of spatial statistics to spatially-resolved omics data and in particular, discusses various advantages, challenges, and nuances. This work is accompanied by a vignette, pasta, that showcases the usefulness of spatial statistics in biology using several R packages. |
| title | Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta |
| topic | Quantitative Methods Genomics |
| url | https://arxiv.org/abs/2412.01561 |