plotPCA: Plot PCA for an SCESet object

In dynverse/scaterlegacy: Single-cell analysis toolkit for gene expression data in R

Description

Produce a principal components analysis (PCA) plot of two or more principal components for an SCESet dataset.

Usage

plotPCASCESet(object, ntop = 500, ncomponents = 2, exprs_values = "exprs",
  colour_by = NULL, shape_by = NULL, size_by = NULL, feature_set = NULL,
  return_SCESet = FALSE, scale_features = TRUE, draw_plot = TRUE,
  pca_data_input = "exprs", selected_variables = NULL,
  detect_outliers = FALSE, theme_size = 10, legend = "auto")

## S4 method for signature 'SCESet'
plotPCA(object, ntop = 500, ncomponents = 2,
  exprs_values = "exprs", colour_by = NULL, shape_by = NULL,
  size_by = NULL, feature_set = NULL, return_SCESet = FALSE,
  scale_features = TRUE, draw_plot = TRUE, pca_data_input = "exprs",
  selected_variables = NULL, detect_outliers = FALSE, theme_size = 10,
  legend = "auto")

Arguments

object	an SCESet object
ntop	numeric scalar indicating the number of most variable features to use for the PCA. Default is 500, but any ntop argument is overrided if the feature_set argument is non-NULL.
ncomponents	numeric scalar indicating the number of principal components to plot, starting from the first principal component. Default is 2. If ncomponents is 2, then a scatterplot of PC2 vs PC1 is produced. If ncomponents is greater than 2, a pairs plots for the top components is produced.
exprs_values	character string indicating which values should be used as the expression values for this plot. Valid arguments are "tpm" (transcripts per million), "norm_tpm" (normalised TPM values), "fpkm" (FPKM values), "norm_fpkm" (normalised FPKM values), "counts" (counts for each feature), "norm_counts", "cpm" (counts-per-million), "norm_cpm" (normalised counts-per-million), "exprs" (whatever is in the 'exprs' slot of the SCESet object; default), "norm_exprs" (normalised expression values) or "stand_exprs" (standardised expression values) or any other named element of the assayData slot of the SCESet object that can be accessed with the get_exprs function.
colour_by	character string defining the column of pData(object) to be used as a factor by which to colour the points in the plot. Alternatively, a data frame with one column, containing values to map to colours for all cells.
shape_by	character string defining the column of pData(object) to be used as a factor by which to define the shape of the points in the plot. Alternatively, a data frame with one column containing values to map to shapes.
size_by	character string defining the column of pData(object) to be used as a factor by which to define the size of points in the plot. Alternatively, a data frame with one column containing values to map to sizes.
feature_set	character, numeric or logical vector indicating a set of features to use for the PCA. If character, entries must all be in featureNames(object). If numeric, values are taken to be indices for features. If logical, vector is used to index features and should have length equal to nrow(object).
return_SCESet	logical, should the function return an SCESet object with principal component values for cells in the reducedDimension slot. Default is FALSE, in which case a ggplot object is returned.
scale_features	logical, should the expression values be standardised so that each feature has unit variance? Default is TRUE.
draw_plot	logical, should the plot be drawn on the current graphics device? Only used if return_SCESet is TRUE, otherwise the plot is always produced.
pca_data_input	character argument defining which data should be used as input for the PCA. Possible options are "exprs" (default), which uses expression data to produce a PCA at the cell level; "pdata" which uses numeric variables from pData(object) to do PCA at the cell level; and "fdata" which uses numeric variables from fData(object) to do PCA at the feature level.
selected_variables	character vector indicating which variables in pData(object) to use for the phenotype-data based PCA. Ignored if the argument pca_data_input is anything other than "pdata".
detect_outliers	logical, should outliers be detected in the PC plot? Only an option when pca_data_input argument is "pdata". Default is FALSE.
theme_size	numeric scalar giving default font size for plotting theme (default is 10).
legend	character, specifying how the legend(s) be shown? Default is "auto", which hides legends that have only one level and shows others. Alternatives are "all" (show all legends) or "none" (hide all legends).
...	further arguments passed to plotPCASCESet

Details

The function prcomp is used internally to do the PCA. The function checks whether the object has standardised expression values (by looking at stand_exprs(object)). If yes, the existing standardised expression values are used for the PCA. If not, then standardised expression values are computed using scale (with feature-wise unit variances or not according to the scale_features argument), added to the object and PCA is done using these new standardised expression values.

If the arguments detect_outliers and return_SCESet are both TRUE, then the element $outlier is added to the pData (phenotype data) slot of the SCESet object. This element contains indicator values about whether or not each cell has been designated as an outlier based on the PCA. These values can be accessed for filtering low quality cells with, foe example, example_sceset$outlier.

Value

either a ggplot plot object or an SCESet object

Examples

## Set up an example SCESet
data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data = sc_example_cell_info)
example_sceset <- newSCESet(countData = sc_example_counts, phenoData = pd)
drop_genes <- apply(exprs(example_sceset), 1, function(x) {var(x) == 0})
example_sceset <- example_sceset[!drop_genes, ]

## Examples plotting PC1 and PC2
plotPCA(example_sceset)
plotPCA(example_sceset, colour_by = "Cell_Cycle")
plotPCA(example_sceset, colour_by = "Cell_Cycle", shape_by = "Treatment")
plotPCA(example_sceset, colour_by = "Cell_Cycle", shape_by = "Treatment",
size_by = "Mutation_Status")
plotPCA(example_sceset, shape_by = "Treatment", size_by = "Mutation_Status")
plotPCA(example_sceset, feature_set = 1:100, colour_by = "Treatment",
shape_by = "Mutation_Status")

## experiment with legend
example_subset <- example_sceset[, example_sceset$Treatment == "treat1"]
plotPCA(example_subset, colour_by = "Cell_Cycle", shape_by = "Treatment", legend = "all")

plotPCA(example_sceset, shape_by = "Treatment", return_SCESet = TRUE)

## Examples plotting more than 2 PCs
plotPCA(example_sceset, ncomponents = 8)
plotPCA(example_sceset, ncomponents = 4, colour_by = "Treatment",
shape_by = "Mutation_Status")

dynverse/scaterlegacy documentation built on Feb. 17, 2020, 5:07 a.m.