Aim:

• test for significant changes in grouped cell abundance across conditions

Example:

• which cell types are depleted or enriched upon treatment?

• Most methods require defined clusters as input. Assigning cells to discrete clusters in context of continuous differentiation, developmental or stimulation trajectories.

• Methods that don’t require clusters also don’t model variability in cell numbers among replicates or can only carry out pairwise comparisons.

• Can be used for complex experimental designs

Steps:

• Construct KNN graph

  • rescales UMI count by per-cell sequencing depth
  • log transforms
  • uses PCA
  • calculates Euclidean distance between cells and its k nearest neighbour in PC space

• Defines Cell Neighbourhoods by sub-sampling the graph to identify useful “index cells” (for computational efficiency)

• Counts cells in Neighbourhoods

• Tests for DA in Neighbourhoods

• Does a multiple testing correction (Spacial FDR)

• Visualises the outputs with our UMAP embeddings

• Milo is implemented in R and uses the SingleCellExperiment class as input

• We will have to convert our Seurat object to a SingleCellExperiment object

• Seurat has a function for this called as.SingleCellExperiment()

• The SCE object also has slots for the differnt elements of our data

  • colData for the metadata
  • assays for the count data
  • reducedDims for the dimensionality reduction embeddings