Bar plot of missing values per lines using highcharter.

Source: R/omXplore_pca.R, R/plot_pca.R

This method plots a bar plot which represents the distribution of the number of missing values (NA) per lines (ie proteins).

  • wrapper_pca()

  • plotPCA_Eigen_hc(): plots the eigen values of PCA with the highcharts library

  • plotPCA_Eigen(): plots the eigen values of PCA

  • plotPCA_Var()

  • plotPCA_Ind()

omXplore_pca_ui(id)

omXplore_pca_server(id, obj, i)

omXplore_pca(obj, i)

wrapper_pca(qdata, group, var.scaling = TRUE, ncp = NULL)

plotPCA_Eigen(res.pca)

plotPCA_Var(res.pca, chosen.axes = c(1, 2))

plotPCA_Ind(res.pca, chosen.axes = c(1, 2))

plotPCA_Eigen_hc(res.pca)

Arguments

id

A character(1) which is the id of the shiny module.

obj

An instance of the class MultiAssayExperiment.

i

An integer which is the index of the assay in the param obj

qdata

A data.frame() of quantitative data

group

A vector with the name of samples

var.scaling

A boolean indicating whether to scale the data or not

ncp

See FactoMineR::PCA()

res.pca

The result of the function FactoMineR::PCA()

chosen.axes

See the parameter 'axes' of the function factoextra::fviz_pca_var()

Value

NA

NA

A shiny app

The result of the function FactoMineR::PCA()

A plot

A plot

A plot

A plot

Author

Samuel Wieczorek, Enora Fremy

Examples

if (FALSE) { # \dontrun{
  data(vdata)
  # Replace missing values for the example
  sel <- is.na(SummarizedExperiment::assay(vdata, 1))
  SummarizedExperiment::assay(vdata[[1]])[sel] <- 0
  omXplore_pca(vdata, 1)
} # }


data(vdata)
obj <- vdata[[1]]
res.pca <- wrapper_pca(SummarizedExperiment::assay(obj), get_group(obj))
plotPCA_Eigen(res.pca)


plotPCA_Var(res.pca)

plotPCA_Eigen_hc(res.pca)
plotPCA_Ind(res.pca)