Last data update: 2014.03.03

R: Principal components analysis on the genes
genespcaR 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
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")]))
cols_multi <- scales::hue_pal()(length(levels(groups_multi)))[factor(groups_multi)]
genespca(rlt,ntop=100,arrowColors=cols_multi,groupNames=groups_multi)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(pcaExplorer)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/pcaExplorer/genespca.Rd_%03d_medium.png", width=480, height=480)
> ### Name: genespca
> ### Title: Principal components analysis on the genes
> ### Aliases: genespca
> 
> ### ** Examples
> 
> 
> library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
> dds <- makeExampleDESeqDataSet_multifac(betaSD_condition = 3,betaSD_tissue = 1)
> rlt <- rlogTransformation(dds)
> groups <- colData(dds)$condition
> 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")]))
> cols_multi <- scales::hue_pal()(length(levels(groups_multi)))[factor(groups_multi)]
> genespca(rlt,ntop=100,arrowColors=cols_multi,groupNames=groups_multi)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>