id_non_spcf_markers: id_non_spcf_markers

In shihuang047/crossRanger: Cross-Application of Ranger Models using microbiome data

id_non_spcf_markers

Description

Non-specific features across datasets (at least present in two of datasets): Last update: 20190130

Usage

id_non_spcf_markers(
  feature_res,
  positive_class = "disease",
  other_class = "health",
  p.adj.method = "BH",
  outdir = NULL
)

Arguments

feature_res	the inheritant output from the function of plot.res_list, rf_clf.comps.summ or rf_clf.by_dataset.summ
positive_class	A string indicates one class in the 'c_category' column of metadata.
other_class	A string indicates the other class in the factor, such as 'health'.
p.adj.method	The p-value correction method, default is "bonferroni".
outdir	The outputh directory, default is "./".

Value

...

Author(s)

Shi Huang

See Also

ranger

Examples

df <- data.frame(rbind(t(rmultinom(15, 75, c(.21,.6,.12,.38,.099))),
            t(rmultinom(15, 75, c(.011,.6,.22,.28,.289))),
            t(rmultinom(15, 75, c(.091,.6,.32,.18,.209))),
            t(rmultinom(15, 75, c(.001,.6,.42,.58,.299)))))
# A dataset with no feature significantly changed in all sub-datasets!
df0 <- data.frame(t(rmultinom(60, 300,c(.001,.6,.2,.3,.299))))
f=factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15)))
comp_group="A"
res <-rf_clf.comps.summ(df, f, comp_group, verbose=FALSE, ntree=500,
                        p_cutoff=0.05, p.adj.method = "bonferroni",
                        q_cutoff=0.05, outdir=NULL)
feature_res<-res$feature_res
id_non_spcf_markers(feature_res, positive_class="disease",
                    other_class="health", p.adj.method= "BH", outdir=NULL)

shihuang047/crossRanger documentation built on Feb. 7, 2023, 10:03 p.m.