genespca {pcaExplorer} | R Documentation

## Principal components analysis on the genes

### Description

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

### Usage

    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

### 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

### Value

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

### 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)

[Package pcaExplorer version 2.6.0 Index]