Use this function to perform Pseudbulk DGE analysis.
Usage
bb_pseudobulk_mf(
cds,
pseudosample_table,
design_formula,
count_filter = 10,
result_recipe = "default",
test = "Wald",
reduced = NULL
)Arguments
- cds
The cell data set object subset to analyze
- pseudosample_table
A tibble indicating the sample groupings for analysis. This should include 1.) Unique sample identifiers 2.) Any sample-level cell metadata you wish to include in the regression model and 3.) Any Cell-level metadata you may wish to include such as clusters or partitions. Values will be coerced to factors.
- design_formula
The regression-style formula for the analysis. In the form of "~ variable1 + variable2 + ... final_variable". The default behavior is to calculate results according to the final_variable in the design_formula with preceding variables as co-variates. The reference class is chosen according to alphabetical order. This behavior can be modified by specifying the result_recipe argument.
- count_filter
The minimum number of counts required across all pseudosamples in order to keep a gene in the analysis.
- result_recipe
See above for the default recipe. Alternatively, supply a 3-element vector in the form of c("variable", "experimental_level","reference_or_control_level")