Principal components analysis on the genes

Computes and plots the principal components of the genes, eventually displaying the samples as in a typical biplot visualization.

genespca(
  x,
  ntop,
  choices = c(1, 2),
  arrowColors = "steelblue",
  groupNames = "group",
  biplot = TRUE,
  scale = 1,
  pc.biplot = TRUE,
  obs.scale = 1 - scale,
  var.scale = scale,
  groups = NULL,
  ellipse = FALSE,
  ellipse.prob = 0.68,
  labels = NULL,
  labels.size = 3,
  alpha = 1,
  var.axes = TRUE,
  circle = FALSE,
  circle.prob = 0.69,
  varname.size = 4,
  varname.adjust = 1.5,
  varname.abbrev = FALSE,
  returnData = FALSE,
  coordEqual = FALSE,
  scaleArrow = 1,
  useRownamesAsLabels = TRUE,
  point_size = 2,
  annotation = NULL
)

Arguments

x: A DESeqTransform() object, with data in assay(x), produced for example by either rlog() or varianceStabilizingTransformation()
ntop: Number of top genes to use for principal components, selected by highest row variance
choices: Vector of two numeric values, to select on which principal components to plot
arrowColors: Vector of character, either as long as the number of the samples, or one single value
groupNames: Factor containing the groupings for the input data. Is efficiently chosen as the (interaction of more) factors in the colData for the object provided
biplot: Logical, whether to additionally draw the samples labels as in a biplot representation
scale: Covariance biplot (scale = 1), form biplot (scale = 0). When scale = 1, the inner product between the variables approximates the covariance and the distance between the points approximates the Mahalanobis distance.
pc.biplot: Logical, for compatibility with biplot.princomp()
obs.scale: Scale factor to apply to observations
var.scale: Scale factor to apply to variables
groups: Optional factor variable indicating the groups that the observations belong to. If provided the points will be colored according to groups
ellipse: Logical, draw a normal data ellipse for each group
ellipse.prob: Size of the ellipse in Normal probability
labels: optional Vector of labels for the observations
labels.size: Size of the text used for the labels
alpha: Alpha transparency value for the points (0 = transparent, 1 = opaque)
var.axes: Logical, draw arrows for the variables?
circle: Logical, draw a correlation circle? (only applies when prcomp was called with scale = TRUE and when var.scale = 1)
circle.prob: Size of the correlation circle in Normal probability
varname.size: Size of the text for variable names
varname.adjust: Adjustment factor the placement of the variable names, '>= 1' means farther from the arrow
varname.abbrev: Logical, whether or not to abbreviate the variable names
returnData: Logical, if TRUE returns a data.frame for further use, containing the selected principal components for custom plotting
coordEqual: Logical, default FALSE, for allowing brushing. If TRUE, plot using equal scale cartesian coordinates
scaleArrow: Multiplicative factor, usually >=1, only for visualization purposes, to allow for distinguishing where the variables are plotted
useRownamesAsLabels: Logical, if TRUE uses the row names as labels for plotting
point_size: Size of the points to be plotted for the observations (genes)
annotation: A data.frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, gene_name, containing e.g. HGNC-based gene symbols

Value

An object created by ggplot, which can be assigned and further customized.

Details

The implementation of this function is based on the beautiful ggbiplot package developed by Vince Vu, available at https://github.com/vqv/ggbiplot. The adaptation and additional parameters are tailored to display typical genomics data such as the transformed counts of RNA-seq experiments

Examples


library(DESeq2)
dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3, betaSD_tissue = 1)
rlt <- rlogTransformation(dds)
groups <- colData(dds)$condition
groups <- factor(groups, levels = unique(groups))
cols <- scales::hue_pal()(2)[groups]
genespca(rlt, ntop=100, arrowColors = cols, groupNames = groups)


groups_multi <- interaction(as.data.frame(colData(rlt)[, c("condition", "tissue")]))
groups_multi <- factor(groups_multi, levels = unique(groups_multi))
cols_multi <- scales::hue_pal()(length(levels(groups_multi)))[factor(groups_multi)]
genespca(rlt, ntop = 100, arrowColors = cols_multi, groupNames = groups_multi)