OlinkAnalyze
Facilitate Analysis of Proteomic Data from Olink

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Olink® Analyze Vignette

Olink® Analyze Vignette
In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink 

knitr::opts_chunk$set(
  fig.width = 6L,
  fig.height = 3L,
  fig.align = "center",
  collapse = TRUE,
  comment = "#>"
)

options(
  tibble.print_min = 4L,
  tibble.print_max = 4L
)

Olink® Analyze is an R package that provides a versatile toolbox to enable fast and easy handling of Olink® NPX data for your proteomics research. Olink® Analyze provides functions for using Olink data, including functions for importing Olink® NPX datasets, as well as quality control (QC) plot functions and functions for various statistical tests. This package is meant to provide a convenient pipeline for your Olink NPX data analysis.

Note: Starting with OlinkAnalyze v5.0, detailed analysis workflow vignettes have been moved to the new OlinkAnalyzeVignettes package, which is available on CRAN. This vignette provides an overview of the main functions in OlinkAnalyze and introduces the new v5.0 preprocessing functions check_npx() and clean_npx().

Installation

You can install Olink® Analyze from CRAN.

install.packages("OlinkAnalyze")

List of functions

For a quick overview of the main functions and typical workflows, see the Olink® Analyze R Package Cheat sheet.

Preprocessing

Statistical analysis

Visualization

Sample datasets

Usage

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/OA_v5.0_flowchart.png"
  ),
  error = FALSE
)
fcap <- paste("Schematic overview illustrating how the newly introduced",
              "functions `check_npx()` and `clean_npx()` in Olink Analyze v5.0",
              "can be used together in a typical Olink data analysis workflow.")

Introduction to Olink NPX data format

The package contains two test data files named npx_data1 and npx_data2. These are synthetic datasets that resemble Olink® data accompanied by clinical variables. Olink® data that is delivered in long format or imported with the function read_npx() contain the following columns:

Note: There are 5 additional variables in the sample datasets npx_data1 and npx_data2 that include clinical or other information, namely: Subject \<chr>, Treatment \<chr>, Site \<chr>, Time \<chr>, Project \<chr>.

The columns found in an Olink data set may vary based on the version and product.

Preprocessing

Read NPX data (read_NPX() or read_npx())

The read_npx() (or read_NPX()) function imports an NPX file into a tidy format to work with in R. This function supports Olink® NPX files generated by Olink® data software in CSV, Excel, and Parquet formats. No prior alterations to the NPX output file should be made for this function to work as expected.

Function arguments

data <- OlinkAnalyze::read_npx(
  filename = "~/NPX_file_location.xlsx"
)

# OR
data <- OlinkAnalyze::read_NPX(
  filename = "~/NPX_file_location.xlsx"
)

Function output

A tibble or ArrowObject in long format containing:

Read multiple NPX data files (read_NPX() or read_npx())

In order to import multiple NPX data files at once, the read_npx() function can be used in combination with the functions list.files(), lapply() and dplyr::bind_rows(), as seen below. The pattern argument of the list.files() function specifies the NPX file format (.csv, .xlsx, .parquet, or any combination of these). This method requires that all NPX files are stored in the same folder and have identical column names. No prior alterations to the NPX output file should be made for this method to work as expected.

# Read in multiple NPX files in .csv format
data <- list.files(
  path = "path/to/dir/with/NPX/files",
  pattern = "csv$",
  full.names = TRUE
) |>
  lapply(FUN = function(x) {
    df_tmp <- OlinkAnalyze::read_npx(x) |>
      # Optionally add additional columns to add file identifiers
      dplyr::mutate(
        File = .env[["x"]]
      )
    return(df_tmp)
  })  |>
  # optional to return a single data frame of all files instead of a list of dfs
  dplyr::bind_rows()

# Read in multiple NPX files in .parquet format
data <- list.files(
  path = "path/to/dir/with/NPX/files",
  pattern = "parquet$",
  full.names = TRUE
) |>
  lapply(
    OlinkAnalyze::read_npx
  )  |>
  dplyr::bind_rows()

# Read in multiple NPX files in either format
data <- list.files(
  path = "path/to/dir/with/NPX/files",
  pattern = "parquet$|csv$",
  full.names = TRUE
) |>
  lapply(
    OlinkAnalyze::read_npx
  )  |>
  dplyr::bind_rows()

Check NPX data quality (check_npx())

The check_npx() function performs various quality and format checks on NPX data imported with read_npx(). It is recommended to run this function after reading in NPX data and before downstream analysis. The result can be passed as the check_log argument to clean_npx() and all downstream Olink Analyze functions, allowing each function to skip its own internal check and improve performance.

check_npx() also allows for alternative column names to be used in the downstream analysis, both standard and non-standard. If the user wishes to analyze data with non-standard column names, or to resolve column name ambiguities, they can pass a named character vector to the preferred_names argument. For example, one might wish to use the column "PCNormalizedNPX" instead of "NPX" for downstream analysis, or they might have log~2~ transformed the values reported in "Quantified_value" and wish to use that column to analyze their data. Note: for the time being, not all functions utilize the full capacity of the preferred_names argument, but we aim to expand this in the upcoming releases.

Function arguments

# Check NPX data quality and format
check_log <- OlinkAnalyze::check_npx(
  df = data
)

Function output

A named list with the following elements:

Clean NPX data (clean_npx())

The clean_npx() function cleans an NPX data frame by applying a series of filtering and conversion steps. It removes invalid or problematic assays and samples identified by check_npx(), and optionally converts column data types. Passing the output of check_npx() via the check_log argument avoids re-running the internal checks and improves performance.

Function arguments

# Clean the NPX data using the check_npx output
data_clean <- OlinkAnalyze::clean_npx(
  df = data,
  check_log = check_log
)

Function output

A "tibble" (or "ArrowObject") in long format containing the cleaned NPX data, with invalid assays, control samples, QC-failing samples, and problematic entries removed according to the chosen arguments. Output matches that of the function read_npx().

Note: We recommend running check_npx() once again after cleaning the data to confirm that all issues have been resolved and that the data is ready for downstream analysis. A schematic illustration of the described workflow is provided (Olink Analyze v5.0 example workflow).

# Check NPX data quality and format
check_log_clean <- OlinkAnalyze::check_npx(
  df = data_clean
)

Randomize samples on plate (olink_plate_randomizer())

The olink_plate_randomizer() function randomly assigns samples to a plate well with the option to keep the same individuals on the same plate. Olink® does not recommend to force balance based on other clinical variables.

For more information on plate randomization, consult the Plate Randomization Vignette in the R package OlinkAnalyzeVignettes.

Select bridge samples (olink_bridgeselector())

The bridge selection function selects a number of bridge samples based on the input data. Bridge samples are used to normalize two data sets or projects that have been ran at different time points, hence, a batch effect is expected. It selects samples that have good detectability (if applicable), pass quality control, and cover a wide range of data points.

For more information on bridge sample selection, consult the Introduction to bridging Olink® NPX datasets tutorial in the R package OlinkAnalyzeVignettes.

Normalizing NPX data (olink_normalization())

The Olink® normalization function normalizes NPX values between two different datasets or one Olink® dataset to a set of reference medians.

The function handles four different types of normalization:

Integrating NGS NPX LOD (olink_lod())

The olink_lod() function adds LOD information to an Olink next generation sequencing (NGS) data set. This function can incorporate LOD based on either an Olink NGS data set's negative controls or using predetermined fixed LOD values, which can be downloaded from the Document Download Center at olink.com, or using both methods. The default LOD calculation method is based on the negative controls. If an NPX file is intensity normalized, both intensity normalized and PC normalized LODs are provided.

For more information on calculating LOD, consult the Calculating LOD from Olink® Explore data tutorial in the R package OlinkAnalyzeVignettes.

Statistical analysis

T-test analysis (olink_ttest())

The olink_ttest() function performs a Welch 2-sample t-test or paired t-test at confidence level 0.95 for every protein (by OlinkID) for a given grouping variable. It corrects for multiple testing using the Benjamini-Hochberg method ("fdr"). Adjusted p-values are logically evaluated towards adjusted p-value \< 0.05. The resulting t-test table is arranged by ascending p-values.

Function arguments

# Run check_npx() and clean_npx() before analysis
OlinkAnalyze::olink_ttest(
  df = data_clean,
  variable = "Treatment",
  check_log = check_log_clean
)

Function output

A tibble with the following columns:

Mann-Whitney U Test analysis (olink_wilcox())

The olink_wilcox() function performs a 2-sample Mann-Whitney U test or paired Mann-Whitney U test at confidence level 0.95 for every protein (by OlinkID) for a given grouping variable. It corrects for multiple testing using the Benjamini-Hochberg method ("fdr"). Adjusted p-values are logically evaluated towards adjusted p-value\<0.05. The resulting Mann-Whitney U table is arranged by ascending p-values.

Function arguments

OlinkAnalyze::olink_wilcox(
  df = data_clean,
  variable = "Treatment",
  check_log = check_log_clean
)

Function output

A tibble with the following columns:

Analysis of variance (ANOVA) (olink_anova())

The olink_anova() function performs an ANOVA F-test for each assay (by OlinkID) using Type III sum of squares. The function handles both factor and numerical variables, and/or confounding factors.

Samples with missing variable information or factor levels are excluded from the analysis. Character columns in the input data frame are converted to factors.

Control samples and control assays should be removed before using this function.

Crossed/interaction analysis, i.e. A*B formula notation, is inferred from the variable argument in the following cases:

For covariates, crossed analyses need to be specified explicitly, i.e. two main effects will not be expanded with a c('A', 'B') notation. Main effects present in the variable take precedence.

Adjusted p-values are calculated using the Benjamini & Hochberg (1995) method ("fdr"). The threshold is determined by logic evaluation of Adjusted_pval \< 0.05. Covariates are not included in the p-value adjustment.

Function arguments

# One-way ANOVA, no covariates
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = data_clean,
  variable = "Site",
  check_log = check_log_clean
)

# Two-way ANOVA, no covariates
anova_results_twoway <- OlinkAnalyze::olink_anova(
  df = data_clean,
  variable = c("Site", "Time"),
  check_log = check_log_clean
)

# One-way ANOVA, Treatment as covariates
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = data_clean,
  variable = "Site",
  covariates = "Treatment",
  check_log = check_log_clean
)

Function output

A tibble with the following columns:

Post-hoc ANOVA analysis (olink_anova_posthoc())

olink_anova_posthoc() performs a post-hoc ANOVA test with Tukey p-value adjustment per assay (by OlinkID) at confidence level 0.95.

The function handles both factor and numerical variables and/or covariates. The post-hoc test for a numerical variable compares the difference in means of the outcome variable (default: NPX) for 1 standard deviation (SD) difference in the numerical variable, e.g. mean NPX at mean (numerical variable) versus mean NPX at mean (numerical variable) + 1*SD (numerical variable).

Control samples and control assays (AssayType is not "assay", or Assay contains "control" or "ctrl") should be removed before using this function.

Function arguments

# calculate the p-value for the ANOVA
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = data_clean,
  variable = "Site",
  check_log = check_log_clean
)

# extracting the significant proteins
anova_results_oneway_sign <- anova_results_oneway |>
  dplyr::filter(
    .data[["Threshold"]] == "Significant"
  ) |>
  dplyr::pull(
    .data[["OlinkID"]]
  )

anova_posthoc_oneway_results <- OlinkAnalyze::olink_anova_posthoc(
  df = data_clean,
  olinkid_list = anova_results_oneway_sign,
  variable = "Site",
  effect = "Site",
  check_log = check_log_clean
)

Function output

A tibble with the following columns:

Linear mixed effects model analysis (olink_lmer())

The olink_lmer() function fits a linear mixed effects model for every protein (by OlinkID) in every panel. The function handles both factor and numerical variables and/or covariates.

Samples with missing variable information or factor levels are excluded from the analysis. Character columns in the input data frame are converted to factors.

Crossed/interaction analysis, i.e. A*B formula notation, is inferred from the variable argument in the following cases:

For covariates, crossed analyses need to be specified explicitly, i.e. two main effects will not be expanded with a c('A', 'B') notation. Main effects present in the variable take precedence.

Adjusted p-values are calculated using the Benjamini & Hochberg (1995) method ("fdr"). The threshold is determined by logic evaluation of Adjusted_pval \< 0.05. Covariates are not included in the p-value adjustment.

Function arguments

# Linear mixed model with one variable.
lmer_results_oneway <- OlinkAnalyze::olink_lmer(
  df = data_clean,
  variable = "Site",
  random = "Subject",
  check_log = check_log_clean
)

# Linear mixed model with two variables.
lmer_results_twoway <- OlinkAnalyze::olink_lmer(
  df = data_clean,
  variable = c("Site", "Treatment"),
  random = "Subject",
  check_log = check_log_clean
)

Function outcome

A tibble with the following columns:

Post-hoc linear mixed effects model analysis (olink_lmer_posthoc())

The olink_lmer_posthoc() function is similar to olink_lmer() but performs a post-hoc analysis based on a linear mixed model effects model. The function handles both factor and numerical variables and/or covariates. Differences in estimated marginal means are calculated for all pairwise levels of a given output variable. Degrees of freedom are estimated using Satterthwaite’s approximation. The post-hoc test for a numerical variable compares the difference in means of the outcome variable (default: NPX) for 1 standard deviation difference in the numerical variable, e.g. mean NPX at mean(numerical variable) versus mean NPX at mean(numerical variable) + 1*SD(numerical variable). The output tibble is arranged by ascending adjusted p-values.

Function arguments

# Linear mixed model with two variables.
lmer_results_twoway <- OlinkAnalyze::olink_lmer(
  df = data_clean,
  variable = c("Site", "Treatment"),
  random = "Subject",
  check_log = check_log_clean
)

# extracting the significant proteins
lmer_results_twoway_sign <- lmer_results_twoway |>
  dplyr::filter(
    .data[["Threshold"]] == "Significant" &
      .data[["term"]] == "Treatment"
  ) |>
  dplyr::pull(
    .data[["OlinkID"]]
  )

# performing post-hoc analysis
lmer_posthoc_twoway_results <- OlinkAnalyze::olink_lmer_posthoc(
  df = data_clean,
  olinkid_list = lmer_results_twoway_sign,
  variable = c("Site", "Treatment"),
  random = "Subject",
  effect = "Treatment",
  check_log = check_log_clean
)

Function output

A tibble with the following columns:

Additional Statistical Tests

Many other statistical functions can be found within Olink Analyze, including:

To learn more about these functions, consult their help documentation using the help() function.

Pathway Enrichment (olink_pathway_enrichment())

The olink_pathway_enrichment() function can be used to perform Gene Set Enrichment Analysis (GSEA) or Over-Representation Analysis (ORA) using MSigDB, Reactome, KEGG, or GO annotations. MSigDB includes curated gene sets (C2) and ontology gene sets (C5) which encompasses Reactome, KEGG, and GO annotations. This function performs enrichment using the gsea() or enrich() functions from the clusterProfiler R package from BioConductor. The function uses the estimate from a previous statistical analysis for one contrast for all proteins. MSigDB is subset if ontology is KEGG, GO, or Reactome. test_results must contain estimates for all assays. Post-hoc results can be used but should be filtered for one contrast to improve interpretability.

Alternative statistical results can be used as input as long as they include the columns OlinkID, Assay, and estimate. A column named Adjusted_pval is also needed for ORA. Any statistical results that contains one estimate per protein will work as long as the estimates are comparable to each other.

Function Arguments

ttest_results <- OlinkAnalyze::olink_ttest(
  df = data_clean,
  variable = "Treatment",
  alternative = "two.sided",
  check_log = check_log_clean
)

# GSEA enrichment analysis
gsea_results <- OlinkAnalyze::olink_pathway_enrichment(
  df = data_clean,
  test_results = ttest_results,
  check_log = check_log_clean
)

# ORA enrichment analysis
ora_results <- OlinkAnalyze::olink_pathway_enrichment(
  df = data_clean,
  test_results = ttest_results,
  method = "ORA",
  check_log = check_log_clean
)

Function Output

A data frame of enrichment results.

Columns for ORA include:

Columns for GSEA:

Exploratory analysis

Principal components analysis (PCA) plot (olink_pca_plot())

Generates PCA projection of all samples from NPX data along two principal components (default 'PC1' vs 'PC2') colored by the variable specified by color_g (default 'QC_Warning') and including the percentage of explained variance. By default, the values are scaled and centered in the PCA and proteins with missing NPX values removed from the corresponding assays. Unique sample names are required. Imputation by median value is performed for assays with missingness \<10% for multi-plate projects, and for missingness \<5% for single plate projects.

More information about olink_pca() can be found in the Outlier Exclusion Vignette in the R package OlinkAnalyzeVignettes.

Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) (olink_umap_plot())

Computes a manifold approximation and projection and plots the two specified components. Unique sample names are required and imputation by the median is done for assays with missingness \<10% for multi-plate projects and \<5% for single plate projects.

The arguments outlierDefX and outlierDefY can be used to identify outliers in the UMAP results. Sample outliers will be labelled.

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.

Function arguments (selection)

OlinkAnalyze::olink_umap_plot(
  df = data_clean,
  color_g = "QC_Warning",
  byPanel = TRUE,
  check_log = check_log_clean
)

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_umap_plot.png"
  ),
  error = FALSE
)

Function output

A list of objects of class ggplot is silently returned. Plots are also printed unless option quiet = TRUE is set.

Visualization

Boxplots for outcomes (olink_boxplot())

The olink_boxplot() function is used to generate boxplots of NPX values stratified on a variable for a given list of proteins. In order to annotate the plot with ANOVA posthoc analysis results (i.e. include statistical asterisks in the plot), control samples and control assays should be removed from the data.

Function arguments

plot <- data_clean |>
  # removing missing values that exist for Site
  dplyr::filter(
    !is.na(.data[["Site"]])
  ) |>
  OlinkAnalyze::olink_boxplot(
    variable = "Site",
    olinkid_list = c("OID00488", "OID01276"),
    number_of_proteins_per_plot = 2L,
    check_log = check_log_clean
  )

plot[[1L]]

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_boxplot.png"
  ),
  error = FALSE
)

anova_posthoc_results <- OlinkAnalyze::olink_anova_posthoc(
  df = data_clean,
  olinkid_list = c("OID00488", "OID01276"),
  variable = "Site",
  effect = "Site",
  check_log = check_log_clean
)

plot2 <- data_clean |>
  tidyr::drop_na() |> # removing missing values that exist for Site
  OlinkAnalyze::olink_boxplot(
    variable = "Site",
    olinkid_list = c("OID00488", "OID01276"),
    number_of_proteins_per_plot = 2L,
    posthoc_results = anova_posthoc_results,
    check_log = check_log_clean
  )

plot2[[1L]]

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_boxplot_anova_posthoc.png"
  ),
  error = FALSE
)

Function output

A list of objects of class ggplot.

Note: Please note that plots will not appear in the Viewer panel of RStudio if not assigned to a variable and printing it (see sample code above).

Boxplots for QC (olink_dist_plot())

The olink_dist_plot() function generates boxplots of NPX values for each sample, faceted by Olink panel. This is used as an initial QC step to identify potential outliers.

More information about olink_dist_plot() can be found in the Outlier Exclusion Vignette in the R package OlinkAnalyzeVignettes.

Point-range plot for LMER (olink_lmer_plot())

The function olink_lmer_plot() generates a point-range plot for a given list of proteins based on linear mixed effect model. The points illustrate the mean NPX level for each group and the error bars illustrate 95% confidence intervals. Facets are labeled by the protein name and corresponding OlinkID for the protein.

Function arguments

plot <- OlinkAnalyze::olink_lmer_plot(
  df = data_clean,
  olinkid_list = c("OID01216", "OID01217"),
  variable = c("Site", "Treatment"),
  x_axis_variable = "Site",
  col_variable = "Treatment",
  random = "Subject",
  check_log = check_log_clean
)

plot[[1L]]

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_lmer_plot.png"
  ),
  error = FALSE
)

Function output

A list of objects of class ggplot.

Note: Please note that plots will not appear in the Viewer panel of RStudio if not assigned to a variable and printing it (see sample code above).

Heatmap for visualizing pathway enrichment (olink_pathway_heatmap())

The olink_pathway_heatmap() function generates a heatmap of proteins related to pathways using the enrichment results from the olink_pathway_enrichment() function. Either the top terms can be visualized or terms containing a certain keyword. For each term, the proteins in the test_result data frame that are related to that term will be visualized by their estimate. This visualization can be used to determining how many proteins of interest are involved in a particular pathway and in which direction their estimates are.

Function arguments

OlinkAnalyze::olink_pathway_heatmap(
  enrich_results = gsea_results,
  test_results = ttest_results
)

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_pathway_heatmap_gsea.png"
  ),
  error = FALSE
)

OlinkAnalyze::olink_pathway_heatmap(
  enrich_results = ora_results,
  test_results = ttest_results,
  method = "ORA",
  keyword = "immune"
)

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_pathway_heatmap_ora.png"
  ),
  error = FALSE
)

Function output

A heatmap as a ggplot object.

Bargraph for visualizing pathway enrichment (olink_pathway_visualization())

The olink_pathway_visualization() function generates a bar graph of the top terms or terms related to a certain keyword for results from the olink_pathway_enrichment() function. The bar represents either the normalized enrichment score (NES) for GSEA results or counts (number of proteins) for ORA results colored by adjusted p-value. Pathways are ordered by unadjusted p-value. The ORA visualization also contains the number of proteins out of the total proteins in that pathway as a ratio after the bar.

Function arguments

Function output

A bar graph as a ggplot object.

Scatterplot for QC (olink_qc_plot())

The olink_qc_plot() function generates a plot faceted by Panel, plotting IQR vs. median NPX for all samples. This is a good first check to find out if any samples have a tendency to be classified as outliers. Horizontal dashed lines indicate +/-3 standard deviations from the mean IQR. Vertical dashed lines indicate +/-3 standard deviations from the mean sample median.

More information about olink_qc_plot() can be found in the Outlier Exclusion Vignette in the R package OlinkAnalyzeVignettes.

Heatmap (olink_heatmap_plot())

The olink_heatmap_plot() function generates a heatmap for a specified set of samples and proteins. By default, the heatmap centers and scales NPX across proteins and clusters samples and proteins using a dendrogram. Unique sample names are required.

The grouping variable(s) are annotated and colored in the left side of the heatmap.

Function arguments

first10 <- data_clean |>
  dplyr::pull(
    .data[["OlinkID"]]
  ) |>
  unique() |>
  utils::head(n = 10L)

first15samples <- data_clean |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique() |>
  utils::head(n = 15L)

data_clean_small <- data_clean |>
  dplyr::filter(
    .data[["OlinkID"]] %in% .env[["first10"]]
  ) |>
  dplyr::filter(
    .data[["SampleID"]] %in% .env[["first15samples"]]
  )

OlinkAnalyze::olink_heatmap_plot(
  df = data_clean_small,
  variable_row_list = "Treatment",
  check_log = check_log_clean
)

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_heatmap_plot.png"
  ),
  error = FALSE
)

Function output

An object of class ggplot.

Plot results of t-test (olink_volcano_plot())

The olink_volcano_plot() function generates a volcano plot using results from the olink_ttest() function. The estimated difference is shown in the x-axis and -log10(p-value) in the y-axis. The horizontal dotted line indicates p-value = 0.05. Dots are colored based on significance following Benjamini-Hochberg adjustment with a p-value cutoff of 0.05. Significant assays after adjustment can optionally be annotated by OlinkID.

Function arguments

# perform t-test
ttest_results <- OlinkAnalyze::olink_ttest(
  df = data_clean,
  variable = "Treatment",
  check_log = check_log_clean
)

# select names of proteins to show
top_10_name <- ttest_results |>
  dplyr::slice_head(
    n = 10L
  ) |>
  dplyr::pull(
    .data[["OlinkID"]]
  )

# volcano plot
OlinkAnalyze::olink_volcano_plot(
  p.val_tbl = ttest_results,
  x_lab = "Treatment",
  olinkid_list = top_10_name
)

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_volcano_plot.png"
  ),
  error = FALSE
)

Function output

An object of class ggplot.

Theming function (set_plot_theme())

This function sets a consistent plot theme for plots by adding it to a ggplot object. It is mainly used for aesthetic reasons.

OlinkAnalyze::npx_data1 |>
  dplyr::filter(
    !is.na(.data[["Treatment"]])
  ) |>
  dplyr::filter(
    .data[["OlinkID"]] == "OID01216"
  ) |>
  ggplot2::ggplot(
    ggplot2::aes(
      x = .data[["Treatment"]],
      y = .data[["NPX"]],
      fill = .data[["Treatment"]]
    )
  ) +
  ggplot2::geom_boxplot() +
  OlinkAnalyze::set_plot_theme()

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/set_plot_theme_boxplot.png"
  ),
  error = FALSE
)

Color theming (olink_color_discrete(), olink_color_gradient(), olink_fill_discrete(), olink_fill_gradient())

These functions set a consistent coloring theme for the plots by adding it to a ggplot object. It is mainly used for aesthetic reasons.

OlinkAnalyze::npx_data1 |>
  dplyr::filter(
    !is.na(.data[["Treatment"]])
  ) |>
  dplyr::filter(
    .data[["OlinkID"]] == "OID01216"
  ) |>
  ggplot2::ggplot(
    mapping = ggplot2::aes(
      x = .data[["Treatment"]],
      y = .data[["NPX"]],
      fill = .data[["Treatment"]]
    )
  ) +
  ggplot2::geom_boxplot() +
  OlinkAnalyze::set_plot_theme() +
  OlinkAnalyze::olink_fill_discrete()

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/olink_fill_discrete_boxplot.png"
  ),
  error = FALSE
)

Visualizing bridgeability criteria for between-product normalization (olink_bridgeability_plot())

The olink_bridgeability_plot() function generates a series of plots on a per-assay basis for a data frame generated from between-product bridging. The coloration of the figure headers indicate whether that assay has been defined as bridgeable or not bridgeable. The correlation plot, violin plot, and bar chart figures illustrate the three criteria for determining whether an assay is bridgeable. For assays determined to be bridgeable, the ECDF curve and corresponding KS statistic are used to determine which normalization approach (median centering or quantile smoothing) is most suitable for between-product normalization. For more information on the between-product bridging methodology and bridgeability criteria, consult the Bridging across NGS-based Olink^®^ products Tutorial in the R package OlinkAnalyzeVignettes.

Function arguments

npx_ht <- data_exploreht |>
  dplyr::filter(
    .data[["SampleType"]] == "SAMPLE"
  ) |>
  dplyr::mutate(
    Project = "data1"
  )

check_npx_ht <- OlinkAnalyze::check_npx(
  df = npx_ht
)

npx_3072 <- data_explore3072 |>
  dplyr::filter(
    .data[["SampleType"]] == "SAMPLE"
  ) |>
  dplyr::mutate(
    Project = "data2"
  )

check_npx_3072 <- OlinkAnalyze::check_npx(
  df = npx_3072
)

overlapping_samples <- unique(
  intersect(
    x = npx_ht |> dplyr::distinct(.data[["SampleID"]]) |> dplyr::pull(),
    y = npx_3072 |> dplyr::distinct(.data[["SampleID"]]) |> dplyr::pull()
  )
)

npx_br_data <- OlinkAnalyze::olink_normalization(
  df1 = npx_ht,
  df2 = npx_3072,
  overlapping_samples_df1 = overlapping_samples,
  df1_project_nr = "Explore HT",
  df2_project_nr = "Explore 3072",
  reference_project = "Explore HT",
  format = FALSE,
  df1_check_log = check_npx_ht,
  df2_check_log = check_npx_3072
)

check_npx_br_data <- OlinkAnalyze::check_npx(
  df = npx_br_data
)

npx_br_data_bridgeable_plt <- OlinkAnalyze::olink_bridgeability_plot(
  df = npx_br_data,
  median_counts_threshold = 150L,
  min_count = 10L,
  check_log = check_npx_br_data
)

npx_br_data_bridgeable_plt[[1L]]

knitr::include_graphics(
  path = normalizePath(
    path = "../man/figures/bridgeable_plt_MedianCenter.png"
  ),
  error = FALSE
)

Function output

A list of objects of class ggplot.

Contact Us

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Legal Disclaimer

© r format(Sys.Date(), "%Y") Olink Proteomics AB, part of Thermo Fisher Scientific.

Olink products and services are For Research Use Only. Not for use in diagnostic procedures.

All information in this document is subject to change without notice. This document is not intended to convey any warranties, representations and/or recommendations of any kind, unless such warranties, representations and/or recommendations are explicitly stated.

Olink assumes no liability arising from a prospective reader’s actions based on this document.

OLINK, NPX, PEA, PROXIMITY EXTENSION, INSIGHT and the Olink logotype are trademarks registered, or pending registration, by Olink Proteomics AB. All third-party trademarks are the property of their respective owners.

Olink products and assay methods are covered by several patents and patent applications https://www.olink.com/patents/.



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

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