compare_DA  R Documentation 
Calculating power, false discovery rates, false positive rates and auc ( area under the receiver operating characteristic (ROC) curve) for various DA methods.
compare_DA(
ps,
group,
taxa_rank = "none",
methods,
args = list(),
n_rep = 20,
effect_size = 5,
k = NULL,
relative = TRUE,
BPPARAM = BiocParallel::SnowParam(progressbar = TRUE)
)
ps, group, taxa_rank 
main arguments of all differential analysis
methods. 
methods 
character vector, differential analysis methods to be compared, available methods are "aldex", "ancom", "ancombc", "deseq2", "edger", "lefse", "limma_voom", "metagenomeseq", "simple_stat". 
args 
named list, which used to set the extra arguments of the
differential analysis methods, so the names must be contained in 
n_rep 
integer, number of times to run the differential analyses. 
effect_size 
numeric, the effect size for the spikeins. Default 5. 
k 
numeric vector of length 3, number of features to spike in each
tertile (lower, mid, upper), e.g. 
relative 
logical, whether rescale the total number of individuals
observed for each sample to the original level after spikein. Default

BPPARAM 

To make this function support for different arguments for a certain DA method
args
allows list of list of list e.g. args = list(lefse = list(list(norm = "CPM"), list(norm = "TSS")))
, which specify to compare the different norm
arguments for lefse analysis.
For taxa_rank
, only taxa_rank = "none"
is supported, if this argument is
not "none", it will be forced to "none" internally.
an compareDA
object, which contains a twolength list of:
metrics
: data.frame
, FPR, AUC and spike detection rate for each run.
mm
: differential analysis results.
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