Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data.
Agnieszka GerasShadi Darvish ShafighiKacper DomżałIgor FilipiukŁukasz RączkowskiPaulina SzymczakHosein ToosiLeszek KaczmarekŁukasz KoperskiJens LagergrenDominika NowisEwa SzczurekPublished in: Genome biology (2023)
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
Keyphrases
- single cell
- gene expression
- induced apoptosis
- electronic health record
- prostate cancer
- cell cycle arrest
- big data
- healthcare
- oxidative stress
- endoplasmic reticulum stress
- mesenchymal stem cells
- ionic liquid
- resting state
- cell proliferation
- cell therapy
- cell death
- bone marrow
- multiple sclerosis
- mass spectrometry
- functional connectivity
- artificial intelligence
- bioinformatics analysis
- cerebral ischemia