olink_umap_plot: Function to make a UMAP plot from the data

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Function to make a UMAP plot from the data

Description

Computes a manifold approximation and projection using umap::umap and plots the two specified components. Unique sample names are required and imputation by the median is done for assays with missingness <10\ projects and <5\

Usage

olink_umap_plot(
  df,
  color_g = "QC_Warning",
  x_val = 1L,
  y_val = 2L,
  check_log = NULL,
  config = NULL,
  label_samples = FALSE,
  drop_assays = FALSE,
  drop_samples = FALSE,
  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). Continuous color scale for Olink(R) Sample Index (OSI) columns OSITimeToCentrifugation, OSIPreparationTemperature and OSISummary is also supported.
x_val	Integer indicating which UMAP component to plot along the x-axis (default 1)
y_val	Integer indicating which UMAP component to plot along the y-axis (default 2)
check_log	A named list returned by check_npx(). If NULL, check_npx() will be run internally using df.
config	object of class umap.config, specifying the parameters for the UMAP algorithm (default umap::umap.defaults)
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.
byPanel	Perform the UMAP per panel (default FALSE)
outlierDefX	The number standard deviations along the UMAP dimension plotted on the x-axis that defines an outlier. See also 'Details"
outlierDefY	The number standard deviations along the UMAP dimension plotted on the y-axis that defines an outlier. See also 'Details"
outlierLines	Draw dashed lines at +/- outlierDefX and outlierDefY 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 plot is printed, and a list of ggplot objects is returned.

If byPanel = TRUE, the data processing (imputation of missing values etc) and subsequent UMAP 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 UMAP results. Samples more than +/- outlierDefX and outlierDefY standard deviations from the mean of the plotted UMAP component will be labelled. Both arguments have to be specified.

NOTE: UMAP is a non-linear data transformation that might not accurately preserve the properties of the data. Distances in the UMAP plane should therefore be interpreted with caution.

Value

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

Examples

if (rlang::is_installed(pkg = c("umap", "ggrepel", "ggpubr"))) {

  npx_data <- npx_data1 |>
    dplyr::mutate(
      SampleID = paste(.data[["SampleID"]], "_", .data[["Index"]], sep = "")
    )

  check_log <- check_npx(df = npx_data)

  # UMAP using all the data
  OlinkAnalyze::olink_umap_plot(
    df = npx_data,
    color_g = "QC_Warning",
    check_log = check_log
  )

  # UMAP per panel
  g <- OlinkAnalyze::olink_umap_plot(
    df = npx_data,
    color_g = "QC_Warning",
    byPanel = TRUE,
    check_log = check_log
  )
  # Plot only the Inflammation panel
  g$Inflammation

  # Label outliers
  OlinkAnalyze::olink_umap_plot(
    df = npx_data,
    color_g = "QC_Warning",
    outlierDefX = 2L,
    outlierDefY = 4L,
    check_log = check_log
  )

  OlinkAnalyze::olink_umap_plot(
    df = npx_data,
    color_g = "QC_Warning",
    outlierDefX = 3L,
    outlierDefY = 2L,
    byPanel = TRUE,
    check_log = check_log
  )

  # Retrieve outliers
  p <- OlinkAnalyze::olink_umap_plot(
    df = npx_data,
    color_g = "QC_Warning",
    outlierDefX = 3L,
    outlierDefY = 2L,
    byPanel = TRUE,
    check_log = check_log
  )
  outliers <- lapply(p, function(x) x$data) |>
    dplyr::bind_rows() |>
    dplyr::filter(
      .data[["Outlier"]] == 1L
    )
}

OlinkAnalyze documentation built on June 24, 2026, 1:06 a.m.