plot.geimCV: Plot geimCV results

In LBMC/wormAge: Real Age Prediction from Transcriptome staging On Reference

Plot geimCV results

Description

Plot geimCV results

Usage

## S3 method for class 'geimCV'
plot(
  x,
  join.plots = TRUE,
  to_plot = colnames(x$cve[[1]]),
  names = as.character(x$formula_list),
  names.arrange = NULL,
  tcol = 1,
  swarm = T,
  swarmargs = list(pch = 16),
  main = NULL,
  ...
)

Arguments

x	a geimCV object, as returned by ge_imCV
join.plots	boolean ; if TRUE, sets up par(mfrow) appropriately
to_plot	indices to plot, defaults to all computed indices.
names	the labels of the models, defaults to model formulas.
names.arrange	if defined, prints names with shifted height (like 'steps') by given number of labels
tcol	text color for names (only when names.arrange is defined)
swarm	boolean ; if TRUE, displays individual values on top of boxplots as swarms
swarmargs	list of parameters given to beeswarm
main	title of the plots, pasted with indices on each plot.
...	extra arguments passed on to boxplot

Value

invisibly returns a list of boxplot objects that can be re-plotted through bxp.

Examples

requireNamespace('wormRef', quietly = TRUE)
requireNamespace('stats', quietly = TRUE)

# gene expression data
X <- wormRef::Cel_larval$g

# pheno data (e.g time, batch)
p <- wormRef::Cel_larval$p

# do a pca & select nb of components to use for interpol
pca <- stats::prcomp(X, rank = 20)
nc <- sum(summary(pca)$importance[3, ] < .999) + 1


# find optimal spline type
# setup formulas
smooths <- c('bs', 'tp', 'cr', 'ds')
flist <- as.list(paste0('X ~ s(age, bs = \'', smooths, '\') + cov'))
# do CV
cvres <- ge_imCV(X = scale(X), p = p, formula_list = flist,
                 cv.n = 20, nc = nc)
# check results
plot(cvres, names.arrange = 4) # lowest pred error with 'ds' spline

# build model & make reference
m <- ge_im(X = X, p = p, formula = 'X ~ s(age, bs = \'ds\') + cov', nc = nc)

ref <- make_ref(m, cov.levels = list('cov'='O.20'), n.inter = 100, 
                t.unit='h past egg-laying (20C)')

# check model interpolation on pca components
par(mfrow = c(2,2))
plot(m, ref, ncs=1:4) # showing first 4 PCs


# test
ae_X <- ae(X, ref)
par(mfrow = c(1,2))
plot(p$age, ae_X$age.estimates[,1])
plot(ae_X, groups = p$cov)

LBMC/wormAge documentation built on April 6, 2023, 3:52 a.m.