Spatial reconstruction of single-cell gene expression data
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...Background Simple integrated analysis work flows for single-cell transcriptomic data [1] have been enabled by frameworks such as SEURAT [2], MONOCLE [3], SCDE/PAGODA [4], MAST [5], CELL RANGER [6], SCATER [7], and SCRAN [8]....
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...We thank the authors of SEURAT, CELL RANGER, and SPRING for sharing their great tutorials....
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...• Clustering and identifying cell types, adapted from and benchmarked with http://satijalab.org/seurat/ pbmc3k_tutorial.html and one of SEURAT’s tutorials [2]: https://github.com/theislab/scanpy_usage/tree/ master/170505_seurat....
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...html) [2], all steps starting from raw count data to the identification of cell types are carried out, providing speedups between 5 and 90 times in each step (https:// github....
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...SCANPY is benchmarked in comparisons with established packages In a detailed clustering tutorial of 2700 peripheral blood mononuclear cells (PBMCs), adapted from one of SEURAT’s tutorials (http://satijalab.org/seurat/pbmc3k_ tutorial.html) [2], all steps starting from raw count data to the identification of cell types are carried out, providing speedups between 5 and 90 times in each step (https:// github.com/theislab/scanpy_usage/tree/master/170505_ seurat)....
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