bb_pseudobulk_mf: Run Multifactor Pseudobulk Analysis using Deseq2
In blaserlab/blaseRtools: R Tools for Blaser Lab Data Analysis

bb_pseudobulk_mf

R Documentation

Run Multifactor Pseudobulk Analysis using Deseq2

Description

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")