Serun singlecell data analysis notebook
  • Seurat - Guided Clustering Tutorial of 2,700 PBMCs
    • Downloading data from 10X Genomics
    • Setup the Seurat Object
    • QC and selecting cells for further analysis
    • Normalizing the data
    • Identification of highly variable features (feature selection)
    • Scaling the data
    • Perform linear dimensional reduction
    • Determine the ‘dimensionality’ of the dataset
    • Cluster the cells
    • Run non-linear dimensional reduction (UMAP/tSNE)
    • Finding differentially expressed features (cluster biomarkers)
    • Assigning cell type identity to clusters
Serun singlecell data analysis notebook
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