![]() cell-cell interactions exported as graphīy combining these with availabel channel information, the data can be read into a SpatialExperiment or SingleCellExperiment object (see below).per cell: channel intensity, morphology and location.While the output of both approaches is structured differently, the exported features are comparable: steinbock further supports segmentation via the use of Mesmer from the DeepCell library (Greenwald et al. Based on these classification probabilities a CellProfiler pipeline performs cell segmentation and feature extraction.Ī containerized version of this pipeline is implemented in steinbock. The underlying principle is to train a pixel classifier (using ilastik) on a number of selected channels to perform probabilistic classification of each pixel into: background, cytoplasm and nucleus. The ImcSegmentationPipeline has been developed to give the user flexibility in how to perform channel selection and image segmentation. However, imcRtools only supports direct reader functions for two segmentation strategies developed for highly multiplexed imaging technologies: There are multiple different image segmentation approaches available. In this vignette, objects are defined as cells however, also larger scale structures could be segmented. Therefore, a common processing step using multi-channel images is object segmentation. The pixel resolution of most highly multiplexed imaging technologies including IMC support the detection of cellular structures. Nevertheless, analysis performed on the single-cell level is equally applicable. However, data produced by these techniques required different pre-processing steps. Increased multiplexity compared to epitope-based techniques is achieved using single-cell resolved spatial transcriptomics techniques including MERFISH (Chen et al. More info on the data types and further pre-processing can be found below. After technology-dependent data pre-processing, the raw data files are comparable: multi-channel images for the dimensions x, y, and c, where x and y define the number of pixels ( x * y) per image and c the number of proteins (also refered to as “markers”) measured per image. 2018, acquire read-outs of the expression of tens of protein in a spatially resolved manner. 2014) and cyclic immunofluorescence techniques (Lin et al. 2014), multiplexed ion beam imaging (MIBI) (Angelo et al. Highly multiplexed imaging techniques (Bodenmiller 2016) such as imaging mass cytometry (IMC) (Giesen et al.
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