Sökning: onr:"swepub:oai:DiVA.org:uu-523994" >
Points2Regions :
Points2Regions : Fast, interactive clustering of imaging-based spatial transcriptomics data
-
- Andersson, Axel (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Behanova, Andrea (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Avenel, Christophe (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Science for Life Laboratory, SciLifeLab
-
visa fler...
-
- Windhager, Jonas (författare)
- Uppsala universitet,Science for Life Laboratory, SciLifeLab,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Malmberg, Filip, 1980- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Science for Life Laboratory, SciLifeLab
-
- Wählby, Carolina, professor, 1974- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab,Avdelningen Vi3
-
visa färre...
-
(creator_code:org_t)
- Engelska.
- Relaterad länk:
-
https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces 'Points2Regions', a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Bioinformatics
- Bioinformatik
- Immunologi
- Immunology
- Computerized Image Processing
- Datoriserad bildbehandling
Publikations- och innehållstyp
- vet (ämneskategori)
- ovr (ämneskategori)