Nothing
## -----------------------------------------------------------------------------
# install.packages("GUniFrac")
## ---- message=FALSE-----------------------------------------------------------
library(GUniFrac)
## ---- results = FALSE---------------------------------------------------------
data(throat.otu.tab)
data(throat.meta)
comm <- t(throat.otu.tab)
meta.dat <- throat.meta
meta.dat
## ---- results = FALSE---------------------------------------------------------
ZicoSeq.obj <- ZicoSeq(meta.dat = meta.dat, feature.dat = comm,
grp.name = 'SmokingStatus', adj.name = 'Sex', feature.dat.type = "count",
# Filter to remove rare taxa
prev.filter = 0.2, mean.abund.filter = 0,
max.abund.filter = 0.002, min.prop = 0,
# Winsorization to replace outliers
is.winsor = TRUE, outlier.pct = 0.03, winsor.end = 'top',
# Posterior sampling
is.post.sample = TRUE, post.sample.no = 25,
# Use the square-root transformation
link.func = list(function (x) x^0.5), stats.combine.func = max,
# Permutation-based multiple testing correction
perm.no = 99, strata = NULL,
# Reference-based multiple stage normalization
ref.pct = 0.5, stage.no = 6, excl.pct = 0.2,
# Family-wise error rate control
is.fwer = TRUE, verbose = TRUE, return.feature.dat = TRUE)
## ---- fig.retina = 4, fig.width= 8, fig.height=8, results=FALSE, message=FALSE, warning=FALSE----
ZicoSeq.plot(ZicoSeq.obj, pvalue.type = 'p.adj.fdr', cutoff = 0.1, text.size = 10,
out.dir = NULL, width = 10, height = 6)
## ---- results = FALSE---------------------------------------------------------
comm.p <- t(t(comm) / colSums(comm))
ZicoSeq.obj.p <- ZicoSeq(meta.dat = meta.dat, feature.dat = comm.p,
grp.name = 'SmokingStatus', adj.name = 'Sex', feature.dat.type = "proportion",
# Filter to remove rare taxa
prev.filter = 0.2, mean.abund.filter = 0, max.abund.filter = 0.002, min.prop = 0,
# Winsorization to replace outliers
is.winsor = TRUE, outlier.pct = 0.03, winsor.end = 'top',
# Posterior sampling will be automatically disabled
is.post.sample = FALSE, post.sample.no = 25,
# Use the square-root transformation
link.func = list(function (x) x^0.5, function (x) x^0.25), stats.combine.func = max,
# Permutation-based multiple testing correction
perm.no = 99, strata = NULL,
# Reference-based multiple stage normalization
ref.pct = 0.5, stage.no = 6, excl.pct = 0.2,
# Family-wise error rate control
is.fwer = TRUE, verbose = TRUE, return.feature.dat = T)
suppressWarnings(ZicoSeq.plot(ZicoSeq.obj = ZicoSeq.obj.p, pvalue.type = 'p.adj.fdr',
cutoff = 0.1, text.size = 10, out.dir = NULL, width = 10, height = 6))
## ---- fig.retina = 4, fig.width= 14, fig.height=8, results=FALSE, message=FALSE, warning=FALSE----
comm.o <- comm[rowMeans(comm != 0) >= 0.2, ] + 1
comm.o <- log(t(t(comm.o) / colSums(comm.o)))
ZicoSeq.obj.o <- ZicoSeq(meta.dat = meta.dat, feature.dat = comm.o,
grp.name = 'SmokingStatus', adj.name = 'Sex', feature.dat.type = "other",
# Filter will not be applied
prev.filter = 0, mean.abund.filter = 0, max.abund.filter = 0, min.prop = 0,
# Winsorization the top end
is.winsor = TRUE, outlier.pct = 0.03, winsor.end = 'top',
# Posterior sampling will be automatically disabled
is.post.sample = FALSE, post.sample.no = 25,
# Identity function is used
link.func = list(function (x) x), stats.combine.func = max,
# Permutation-based multiple testing correction
perm.no = 99, strata = NULL,
# Reference-based multiple-stage normalization will not be performed
ref.pct = 0.5, stage.no = 6, excl.pct = 0.2,
# Family-wise error rate control
is.fwer = TRUE, verbose = TRUE, return.feature.dat = T)
ZicoSeq.plot(ZicoSeq.obj = ZicoSeq.obj.o, pvalue.type = 'p.adj.fdr',
cutoff = 0.1, text.size = 10, out.dir = NULL, width = 10, height = 6)
## -----------------------------------------------------------------------------
sessionInfo()
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