Predictional functional patwhay differential abundance (DA)
Usage
pathway_daa(
abundance,
metadata,
group,
daa_method = "ALDEx2",
select = NULL,
p.adjust = "BH",
reference = NULL
)Arguments
- abundance
a data frame containing predicted functional pathway abundance
- metadata
a tibble containing samples information
- group
a character specifying the group name for differential abundance analysis
- daa_method
a character specifying the method for differential abundance analysis, default is "ALDEx2"
- select
a vector containing sample names for analysis, if NULL all samples are included, default is NULL
- p.adjust
a character specifying the method for p-value adjustment, default is "BH"
- reference
a character specifying the reference group level, required for several differential abundance analysis methods such as LinDA, limme voom and Maaslin2, default is NULL
Examples
# \donttest{
library(ggpicrust2)
library(MicrobiomeStat)
#> Registered S3 method overwritten by 'rmutil':
#> method from
#> print.response httr
abundance <- data.frame(sample1 = c(10, 20, 30),
sample2 = c(20, 30, 40),
sample3 = c(30, 40, 50),
row.names = c("pathway1", "pathway2", "pathway3"))
metadata <- tibble::tibble(sample = paste0("sample", 1:3),
group = c("control", "control", "treatment"))
#Run pathway_daa function
result <- pathway_daa(abundance = abundance, metadata = metadata, group = "group",
daa_method = "LinDA")
#> 0 features are filtered!
#> The filtered data has 3 samples and 3 features will be tested!
#> Fit linear models ...
#> Completed.
# }