Cluster Representation By Regression Per Sample — bb_cluster

Use this function to determine the differential representation of cells in clusters. It uses a regression method to determine fold change between groups of biological samples. It can only compare two sample groups, e.g. control vs experimental at this point. See parameter descriptions for how to identify these properly.

Usage

bb_cluster_representation2(
  obj,
  sample_var,
  cluster_var,
  comparison_var,
  comparison_levels = NULL,
  color_pal = c("red3", "blue4"),
  sig_val = c("FDR", "PValue"),
  return_val = c("plot", "data")
)

Source

http://bioconductor.org/books/3.13/OSCA.multisample/differential-abundance.html

Arguments

obj: The (possibly filtered) single cell object to operate on. Can be either Seurat or monocle/CDS object.
sample_var: The metadata column holding the biological sample information.
cluster_var: The metadata column holding the clustering or other cell classification information.
comparison_var: The metadata column holding the comparison group information. There can be only two levels in this column. Character data will be converted to factors.
comparison_levels: A character vector identifying the order of the levels to compare. The first value will be shown with negative log2Fold Change and the second will be positive. If NULL (default), R will pick for you.
color_pal: Color palette for the comparison levels, Default: c("red3", "blue4")
sig_val: Report PValue or FDR, Default: "FDR"
return_val: Value to return, Default: c("plot", "table)

Value

OUTPUT_DESCRIPTION

Details

DETAILS