olink_pca_plot: Function to plot a PCA of the data

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Function to plot a PCA of the data

Description

Generates a PCA projection of all samples from NPX data along two principal components (default PC2 vs. PC1) including the explained variance and dots colored by QC_Warning using stats::prcomp and ggplot2::ggplot.

Usage

olink_pca_plot(
  df,
  color_g = "QC_Warning",
  x_val = 1,
  y_val = 2,
  label_samples = FALSE,
  drop_assays = FALSE,
  drop_samples = FALSE,
  n_loadings = 0,
  loadings_list = NULL,
  byPanel = FALSE,
  outlierDefX = NA,
  outlierDefY = NA,
  outlierLines = FALSE,
  label_outliers = TRUE,
  quiet = FALSE,
  verbose = TRUE,
  ...
)

Arguments

df	data frame in long format with Sample Id, NPX and column of choice for colors
color_g	Character value indicating which column to use for colors (default QC_Warning)
x_val	Integer indicating which principal component to plot along the x-axis (default 1)
y_val	Integer indicating which principal component to plot along the y-axis (default 2)
label_samples	Logical. If TRUE, points are replaced with SampleID (default FALSE)
drop_assays	Logical. All assays with any missing values will be dropped. Takes precedence over sample drop.
drop_samples	Logical. All samples with any missing values will be dropped.
n_loadings	Integer. Will plot the top n_loadings based on size.
loadings_list	Character vector indicating for which OlinkID's to plot as loadings. It is possible to use n_loadings and loadings_list simultaneously.
byPanel	Perform the PCA per panel (default FALSE)
outlierDefX	The number standard deviations along the PC plotted on the x-axis that defines an outlier. See also 'Details"
outlierDefY	The number standard deviations along the PC plotted on the y-axis that defines an outlier. See also 'Details"
outlierLines	Draw dashed lines at +/-outlierDef[X,Y] standard deviations from the mean of the plotted PCs (default FALSE)
label_outliers	Use ggrepel to label samples lying outside the limits set by the outlierLines (default TRUE)
quiet	Logical. If TRUE, the resulting plot is not printed
verbose	Logical. Whether warnings about the number of samples and/or assays dropped or imputed should be printed to the console.
...	coloroption passed to specify color order.

Details

The values are by default scaled and centered in the PCA and proteins with missing NPX values are by default removed from the corresponding assay. Unique sample names are required. Imputation by the median is done for assays with missingness <10% for multi-plate projects and <5% for single plate projects. The plot is printed, and a list of ggplot objects is returned.

If byPanel = TRUE, the data processing (imputation of missing values etc) and subsequent PCA is performed separately per panel. A faceted plot is printed, while the individual ggplot objects are returned.

The arguments outlierDefX and outlierDefY can be used to identify outliers in the PCA. Samples more than +/-outlierDef[X,Y] standard deviations from the mean of the plotted PC will be labelled. Both arguments have to be specified.

Value

A list of objects of class "ggplot", each plot contains scatter plot of PCs

Examples

library(dplyr)
npx_data <- npx_data1 %>%
    filter(!grepl('CONTROL', SampleID))

#PCA using all the data
olink_pca_plot(df=npx_data, color_g = "QC_Warning")

#PCA per panel
g <- olink_pca_plot(df=npx_data, color_g = "QC_Warning", byPanel = TRUE)
g[[2]] #Plot only the second panel

#Label outliers
olink_pca_plot(df=npx_data, color_g = "QC_Warning",
               outlierDefX = 2, outlierDefY = 4) #All data
olink_pca_plot(df=npx_data, color_g = "QC_Warning",
               outlierDefX = 2.5, outlierDefY = 4, byPanel = TRUE) #Per panel

#Retrieve the outliers
g <- olink_pca_plot(df=npx_data, color_g = "QC_Warning",
                    outlierDefX = 2.5, outlierDefY = 4, byPanel = TRUE)
outliers <- lapply(g, function(x){x$data}) %>%
    bind_rows() %>%
    filter(Outlier == 1)

OlinkAnalyze documentation built on Nov. 4, 2023, 1:07 a.m.