olink_qc_plot: Function to plot an overview of a sample cohort per Panel

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Function to plot an overview of a sample cohort per Panel

Description

Generates a facet plot per Panel using ggplot2::ggplot and ggplot2::geom_point and stats::IQR plotting IQR vs. median for all samples. Horizontal dashed lines indicate +/-IQR_outlierDef standard deviations from the mean IQR (default 3). Vertical dashed lines indicate +/-median_outlierDef standard deviations from the mean sample median (default 3).

Usage

olink_qc_plot(
  df,
  color_g = "QC_Warning",
  plot_index = FALSE,
  label_outliers = TRUE,
  IQR_outlierDef = 3,
  median_outlierDef = 3,
  outlierLines = TRUE,
  facetNrow = NULL,
  facetNcol = NULL,
  ...
)

Arguments

df	NPX data frame in long format. Must have columns SampleID, NPX and Panel
color_g	Character value indicating which column to use as fill color (default QC_Warning)
plot_index	Boolean. If FALSE (default), a point will be plotted for a sample. If TRUE, a sample's unique index number is displayed.
label_outliers	Boolean. If TRUE, an outlier sample will be labelled with its SampleID.
IQR_outlierDef	The number of standard deviations from the mean IQR that defines an outlier (default 3)
median_outlierDef	The number of standard deviations from the mean sample median that defines an outlier. (default 3)
outlierLines	Draw dashed lines at +/-IQR_outlierDef and +/-median_outlierDef standard deviations from the mean IQR and sample median respectively (default TRUE)
facetNrow	The number of rows that the panels are arranged on
facetNcol	The number of columns that the panels are arranged on
...	coloroption passed to specify color order

Value

An object of class "ggplot". Scatterplot shows IQR vs median for all samples per panel

Examples

library(dplyr)

olink_qc_plot(npx_data1, color_g = "QC_Warning")

#Change the outlier threshold to +-4SD
olink_qc_plot(npx_data1, color_g = "QC_Warning", IQR_outlierDef = 4, median_outlierDef = 4)

#Identify the outliers
qc <- olink_qc_plot(npx_data1, color_g = "QC_Warning", IQR_outlierDef = 4, median_outlierDef = 4)
outliers <- qc$data %>% filter(Outlier == 1)

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