Cluster Representation By Fisher Exact Test Per Cell
Source:R/bb_cluster_representation.R
bb_cluster_representation.RdUse this function to determine the differential representation of cells in clusters. It will determine fold change in a single experimental class over a single control or reference class. This value is normalized to the number of cells captured in all clusters from the class. Significance is determined using Fisher's exact test. This test may overestimate significance in large data sets. In this case, bb_cluster_representation2 may be more robust.
Usage
bb_cluster_representation(
cds,
cluster_var,
class_var,
experimental_class,
control_class,
pseudocount = 1,
return_value = c("table", "plot")
)Arguments
- cds
A cell data set object
- cluster_var
The CDS cell metadata column holding cluster data. There can be any number of clusters in this column.
- class_var
The CDS cell metadata column holding sample class data. There can be only 2 classes in this column. You may need to subset or reclass the samples to achieve this.
- experimental_class
The value from the class column indicating the experimental group.
- control_class
The value from the class column indicating the control or reference class.
- return_value
Option to return either a plot or a data table for plotting in a separate step. Must be either "plot" or "table".