# Olink® Analyze Vignette

#### Olink DS team

#### 2026-06-23

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

- `read_npx()` or `read_NPX()` Function to read NPX data into long format
- `check_npx()` Function to check the quality and format of NPX data
- `clean_npx()` Function to clean NPX data based on the output of `check_npx()`
- `olink_plate_randomizer()` Randomize samples on plates
- `olink_bridgeselector()` Select bridge samples
- `olink_normalization()` Normalization of all proteins
- `olink_lod()` Calculation of Limit of Detection (LOD) for Olink NGS data

Statistical analysis

- `olink_ttest()` Function which performs a t-test per protein
- `olink_wilcox()` Function which performs a Mann-Whitney U Test per protein
- `olink_anova()` Function which performs an ANOVA per protein
- `olink_anova_posthoc()` Function which performs an ANOVA post-hoc test per protein
- `olink_one_non_parametric()` Function which performs a Kruskal-Wallis Test or Friedman Test per protein
- `olink_one_non_parametric_posthoc()` Function which performs post-hoc test for one way non-parametric test
- `olink_ordinalRegression()` Function which performs an ordinal regression per protein
- `olink_ordinalRegression_posthoc()` Function which performs an ordinal regression post-hoc test per protein
- `olink_lmer()` Function which performs a linear mixed model per protein
- `olink_lmer_posthoc()` Function which performs a linear mixed model post-hoc per protein
- `olink_pathway_enrichment()` Function which performs GSEA or ORA pathway enrichment using outcome from other statistical tests

Visualization

- `olink_boxplot()` Function which plots boxplots of a selected variable
- `olink_dist_plot()` Function to plot the NPX distribution by panel
- `olink_lmer_plot()` Function which performs a point-range plot per protein on a linear mixed model
- `olink_pathway_visualization()` Function which plots a bar graph for pathways of interest
- `olink_pathway_heatmap()` Function which plots estimates of proteins associated with pathways of interest
- `olink_pca_plot()` Function to plot a PCA of the data
- `olink_qc_plot()` Function to plot an overview of a sample cohort per Panel
- `olink_umap_plot()` Function to plot a UMAP of the data
- `olink_volcano_plot()` Easy volcano plot with Olink theme
- `olink_heatmap_plot()` Function which generates a heatmap over all proteins
- `set_plot_theme()` Function to set `ggplot2` plot theme
- `olink_bridgeability_plot()` Function which generates plots that illustrate the three criteria for determining whether an assay is bridgeable in cross-product bridge normalization

Sample datasets

- `npx_data1` NPX Data in Long format
- `npx_data2` NPX Data in Long format, follow-up
- `manifest` A sample manifest including Sample ID, Subject ID and clinical variables

# Usage

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:

- `SampleID` <chr>: Sample names or IDs.
- `OlinkID` <chr>: Unique ID for each assay assigned by Olink. In case the assay is included in more than one panel it will have a different `OlinkID` in each one.
- `UniProt` <chr>: UniProt ID.
- `Assay` <chr>: Common gene name for the assay.
- `MissingFreq` <dbl>: Missing frequency for the `OlinkID`, i.e. frequency of samples with `NPX` value below limit of detection (LOD).
- `Panel` <chr>: Olink Panel that samples ran on. Read more about Olink Panels here: https://olink.com/products/compare.
- `Panel_Version` <chr>: Version of the panel. A new panel version might include some different or improved assays.
- `PlateID` <chr>: Name of the plate.
- `QC_Warning` or `SampleQC` <chr>: Indication whether the sample passed Olink QC. More information about Olink quality control metrics can be found in our FAQ by searching for the term “Quality control”.
- `LOD` <dbl>: Limit of detection (LOD) is the minimum level of an individual protein that can be measured. LOD is defined as 3 times the standard deviation over background.
- `NPX` <dbl>: Normalized Protein eXpression (NPX), is Olink®’s unit of protein expression level in a log2 scale. The majority of the functions of this package use NPX values for calculations. Read more about NPX in the Olink FAQ (search term “What is NPX?”) or in Olink’s Data normalization and standardization white paper.

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

- `filename`: Path to the NPX output file.
- `out_df`: Output data frame format. Either “tibble” or “ArrowObject”. Default: “tibble”.
- `long_format`: Logical. Whether the input file is in long or wide format. Default: `TRUE`.
- `olink_platform`: Olink product used to generate the input file. One of “Target 48”, “Flex”, “Target 96”, “Explore 3072”, “Explore HT”, “Focus”, or “Reveal”. Defaults to `NULL` for auto-detection.
- `data_type`: Quantification method of the input data. One of “Ct”, “NPX”, or “Quantified”. Defaults to NULL for auto-detection.
- `.ignore_files`: Character vector of files included in the zip-compressed Olink software output files that should be ignored. Used only for zip-compressed input files.
- `quiet`: Logical. Whether to suppress messages about the import process. Default: `TRUE`.
- `legacy`: Logical. Whether to use the legacy import method. Default: `FALSE`. The legacy method is less efficient and will be deprecated in future versions.

    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:

- `SampleID`: Sample names or IDs.
- `OlinkID`: Unique ID for each assay assigned by Olink. In case the assay is included in more than one panel it will have a different `OlinkID` in each one.
- `UniProt`: UniProt ID.
- `Assay`: Common gene name for the assay.
- `MissingFreq`: Missing frequency for the `OlinkID`, i.e. frequency of samples with `NPX` value below limit of detection (LOD).
- `Panel`: Olink Panel that samples ran on. Read more about Olink Panels here: https://olink.com/products/compare
- `Panel_Version`: Version of the panel. A new panel version might include some different or improved assays.
- `PlateID`: Name of the plate.
- `QC_Warning` or `SampleQC`: Indication whether the sample passed Olink QC. More information about Olink quality control metrics can be found in our FAQ (search term “Quality control”).
- `LOD`: Limit of detection (LOD) is the minimum level of an individual protein that can be measured. LOD is defined as 3 times the standard deviation over background.
- `NPX`: Normalized Protein eXpression (NPX), is Olink’s unit of protein expression level in a log2 scale. The majority of the functions of this package use NPX values for calculations. Read more about NPX in the Olink FAQ (search term “What is NPX?”) or in Olink’s Data normalization and standardization white paper.

## 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 log2 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

- `df`: NPX data frame in long format, as returned by `read_npx()`.
- `preferred_names`: Optional named character vector to resolve column name ambiguities or to map custom column names to internally expected ones.

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

### Function output

A named list with the following elements:

- `col_names` <list>: Column names from the input data frame to be used in downstream analyses.
- `oid_invalid` <chr>: OlinkID values that do not follow the expected formats (OID##### or OID#####_OID#####).
- `assay_na` <chr>: OlinkIDs of assays where all samples have NA quantification values.
- `sample_id_dups` <chr>: Duplicate SampleID values detected in the data.
- `sample_id_na` <chr>: SampleIDs of samples with NA quantification values for all assays.
- `col_class` <data.frame>: Columns with incorrect data types, including the column key, column name, detected type, and expected type.
- `assay_qc` <chr>: OlinkIDs of assays with at least one assay QC warning.
- `non_unique_uniprot` <chr>: OlinkIDs mapped to more than one UniProt ID.
- `darid_invalid` <data.frame>: Invalid combinations of “DataAnalysisRefID” and “PanelDataArchiveVersion”.

## 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

- `df`: NPX data frame in long format as returned by `read_npx()`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.
- `remove_assay_na`: Logical. Remove assays where all samples have NA values. Default: `TRUE`.
- `remove_invalid_oid`: Logical. Remove assays with invalid OlinkIDs. Default: `TRUE`.
- `remove_dup_sample_id`: Logical. Remove samples with duplicate IDs. Default: `TRUE`.
- `remove_control_assay`: Logical. Remove internal control assays. Default: `TRUE`.
- `remove_control_sample`: Logical. Remove external control samples based on “SampleType”. Default: `TRUE`.
- `remove_qc_warning`: Logical. Remove samples with QC status “FAIL”. Default: `TRUE`.
- `remove_assay_warning`: Logical. Remove assays flagged with assay warnings. Default: `TRUE`.
- `control_sample_ids`: Character vector of identifiers of samples that should be removed. Default: `NULL`.
- `convert_df_cols`: Logical. Convert columns to their expected data types. Default: `TRUE`.
- `convert_nonunique_uniprot`: Logical. Resolve non-unique “OlinkID”-“UniProt” mappings. Default: `TRUE`.
- `verbose`: Logical. Print progress messages. Default: `FALSE`.

    # 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:

- Bridging normalization: One of the data sets is adjusted to another using overlapping samples (bridge samples). The overlapping samples should have the same IDs between data sets. Adjustment is made using the median of the paired differences between the bridge samples. For more information on bridging, consult the Introduction to bridging Olink® NPX datasets tutorial in the R package `OlinkAnalyzeVignettes`.
- Subset normalization: A subset of samples is used to normalize two data sets, one of which is used as a `reference_project`. Adjustment is made using the differences of medians between the sample subsets from the two data sets. Subset normalization is useful if no bridge samples were included and one can assume that the distribution of the two datasets is very similar.
- Reference median normalization: Works only on one data set. This is effectively subset normalization, but using difference of medians to pre-recorded median values. `df1`, `overlapping_samples_df1` and `reference_medians` need to be specified.
- Cross-product bridging: Similar to bridging normalization but bridging across products, for example bridging “Olink Explore 3072” data to “Olink Explore HT” data. Overlapping samples are run on both products and used to determine which assays are bridgeable and what method should be used to bridge each assay. For more information on the between-product bridging methodology, consult the Bridging across NGS-based Olink® products Tutorial in the R package `OlinkAnalyzeVignettes`.

## 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and an outcome factor with 2 levels.
- `variable`: Character value that should represent a column in the `df` to be used as a grouping variable. Needs to have exactly 2 levels.
- `pair_id`: Character value indicating which column contains the paired sample identifier. Only used for paired t-tests.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    # 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:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink® ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink® Panel.
- `estimate` <dbl>: Difference in mean NPX between groups.
- `statistic` <dbl>: Value of the t-statistic.
- `p.value` <dbl>: P-value for the test.
- `parameter` <dbl>: Degrees of freedom for the t-statistic.
- `conf.low` <dbl>: Low bound of the confidence interval for the mean.
- `conf.high` <dbl>: High bound of the confidence interval for the mean.
- `method` <chr>: Method that was used.
- `alternative` <chr>: : Description of the alternative hypothesis.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and an outcome factor with 2 levels.
- `variable`: Character value that should represent a column in the `df` to be used as a grouping variable. Needs to have exactly 2 levels.
- `pair_id`: Character value indicating which column contains the paired sample identifier. Only used for paired Mann-Whitney U tests.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

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

### Function output

A tibble with the following columns:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink® ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink® Panel.
- `statistic` <dbl>: Value of the Mann-Whitney U statistic.
- `p.value` <dbl>: P-value for the test.
- `method` <chr>: Method that was used.
- `alternative` <chr>: : Description of the alternative hypothesis.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## 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:

- c(‘A’, ‘B’)
- c(‘A:B’)
- c(‘A:B’, ‘B’) or c(‘A:B’, ‘A’)

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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and an outcome factor with at least 3 levels.
- `variable`: Single character value or character array. In case of single character, then that should represent a column in the `df`. Otherwise, if length > 1, the included variable names will be used in crossed analyses. It can also accept the notations ‘:’ or ‘*’.
- `outcome`: Name of the column from `df` that contains the dependent variable. Default: “NPX”.
- `covariates`: Single character value or character array. Default: `NULL`. Confounding factors to include in the analysis. In case of single character then that should represent a column in the `df`. It can also accept the notations ‘:’ or ‘*’, while crossed analysis will not be inferred from main effects.
- `return.covariates`: Logical. Returns F-test results for the covariates. Default: `FALSE`. Note: Adjusted p-values will be `NA` for covariates.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    # 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:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink Panel.
- `term` <chr>: Name of the variable that was used for the p-value calculation. The “:” between variables indicates interaction between variables.
- `df` <dbl>: Numerator of degrees of freedom.
- `sumsq` <dbl>: Sum of squares.
- `meansq` <dbl>: Mean of squares.
- `statistic` <dbl>: Value of F-statistic.
- `p.value` <dbl>: P-value for the test.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and an outcome factor with at least 3 levels.
- `olinkid_list`: Character vector of `OlinkID`’s on which to perform the post-hoc analysis. If not specified, all assays in `df` are used.
- `variable`: Single character value or character array. In case of single character then that should represent a column in the `df`. Otherwise, if length > 1, the included variable names will be used in crossed analyses. It can also accept the notations ‘:’ or ‘*’.
- `covariates`: Single character value or character array. Default: `NULL`. Confounding factors to include in the analysis. In case of single character then that should represent a column in the `df`. It can also accept the notations ‘:’ or ‘*’, while crossed analysis will not be inferred from main effects.
- `outcome`: Name of the column from `df` that contains the dependent variable. Default: “NPX”.
- `effect`: Character vector. Term on which to perform the post-hoc analysis. Must be subset of or identical to the variable and no adjustment is performed.
- `mean_return`: Logical. If true, returns the mean of each factor level rather than the difference in means (default). Note that no p-value is returned for `mean_return = TRUE`.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    # 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:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink Panel.
- `term` <chr>: Name of the variable that was used for the p-value calculation. The “:” between variables indicates interaction between variables.
- `contrast` <chr>: Variables (in term) that are compared.
- `estimate` <dbl>: Difference in mean NPX between variables (from contrast).
- `conf.low` <dbl>: Low bound of the confidence interval for the mean.
- `conf.high` <dbl>: High bound of the confidence interval for the mean.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## 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:

- c(‘A’, ‘B’)
- c(‘A:B’)
- c(‘A:B’, ‘B’) or c(‘A:B’, ‘A’)

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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and 1-2 variables with at least 2 levels and subject identifiers (`SubjectID`).
- `variable`: Single character value or character array. In case of single character then that should represent a column in the `df`. Otherwise, if length > 1, the included variable names will be used in crossed analyses. It can also accept the notations ‘:’ or ‘*’.
- `outcome`: Name of the column from `df` that contains the dependent variable. Default: “NPX”.
- `random`: Single character value or character array with random effects.
- `covariates`: Single character value or character array. Default: `NULL`. Confounding factors to include in the analysis. In case of single character then that should represent a column in the `df`. It can also accept the notations ‘:’ or ‘*’, while crossed analysis will not be inferred from main effects.
- `return.covariates`: Logical. Returns F-test results for the covariates. Note: Adjusted p-values will be NA for covariates. Default: `FALSE`.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    # 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:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink Panel.
- `term` <chr>: Name of the variable that was used for the p-value calculation. The “:” between variables indicates interaction between variables.
- `sumsq` <dbl>: Sum of squares.
- `meansq` <dbl>: Mean of squares.
- `NumDF` <dbl>: Numerator of degrees of freedom.
- `DenDF` <dbl>: Denominator of degrees of freedom.
- `statistic` <dbl>: Value of F-statistic.
- `p.value` <dbl>: P-value for the test.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and 1-2 variables with at least 2 levels and subject identifiers (`SubjectID`).
- `variable`: Single character value or character array. In case of single character then that should represent a column in the `df`. Otherwise, if length > 1, the included variable names will be used in crossed analyses. It can also accept the notations ‘:’ or ‘*’.
- `olinkid_list`: Character vector of `OlinkID`’s on which to perform the post-hoc analysis. If not specified, all assays in `df` are used.
- `effect`: Character vector. Term on which to perform the post-hoc analysis. Must be subset of or identical to the variable.
- `outcome`: Name of the column from `df` that contains the dependent variable. Default: “NPX”.
- `random`: Single character value or character array with random effects.
- `covariates`: Single character value or character array. Default: `NULL`. Confounding factors to include in the analysis. In case of single character then that should represent a column in the `df`. It can also accept the notations ‘:’ or ‘*’, while crossed analysis will not be inferred from main effects.
- `mean_return`: Logical. If true, returns the mean of each factor level rather than the difference in means (default). Note that no p-value is returned for `mean_return = TRUE` and no adjustment is performed.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    # 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:

- `Assay` <chr>: Assay name.
- `OlinkID` <chr>: Unique Olink ID.
- `UniProt` <chr>: UniProt ID.
- `Panel` <chr>: Olink Panel.
- `term` <chr>: Name of the variable that was used for the p-value calculation. The “:” between variables indicates interaction between variables.
- `contrast` <chr>: Variables (in term) that are compared.
- `estimate` <dbl>: Difference in mean NPX between variables (from contrast).
- `conf.low` <dbl>: Low bound of the confidence interval for the mean.
- `conf.high` <dbl>: High bound of the confidence interval for the mean.
- `Adjusted_pval` <dbl>: Adjusted p-value for the test (Benjamini & Hochberg).
- `Threshold` <chr>: Text indication if assay is significant (adjusted p-value < 0.05).

## Additional Statistical Tests

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

- `olink_one_non_parametric()` Function which performs a Kruskal-Wallis Test or Friedman Test per protein.
- `olink_one_non_parametric_posthoc()` Function which performs post-hoc test for one way non-parametric test.
- `olink_ordinalRegression()` Function which performs an ordinal regression per protein.
- `olink_ordinalRegression_posthoc()` Function which performs an ordinal regression post-hoc test per protein.

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

- `df`: NPX data frame in long format with columns `Assay`, `OlinkID`, `UniProt`, `SampleID`, `QC_Warning` or `SampleQC`, and `NPX`.
- `test_results`: a data frame of statistical test results including `Adjusted_pval` and estimate columns.
- `method`: String of method name. Must be either “GSEA” (default) or “ORA”.
- `ontology`: String of database to query. Must be either “MSigDb”, “KEGG”, “GO”, or “Reactome”.
- `organism`: String of name of organism. Must be either “human” (default) or “mouse”.

    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:

- `ID` <chr>: Pathway ID from MSigDB.
- `Description` <chr>: Description of Pathway from MSigDB.
- `GeneRatio` <chr>: ratio of input proteins that are annotated in a term.
- `BgRatio` <chr>: ratio of all genes that are annotated in this term.
- `pvalue` <dbl>: p-value of enrichment.
- `p.adjust` <dbl>: Adjusted p-value (Benjamini-Hochberg).
- `qvalue` <dbl>: false discovery rate, the estimated probability that the normalized enrichment score represents a false positive finding.
- `geneID`: <chr> list of input proteins (Gene Symbols) annotated in a term delimited by “/”.
- `Count` <dbl>: Number of input proteins that are annotated in a term.

Columns for GSEA:

- `ID` <chr>: Pathway ID from MSigDB.
- `Description` <chr>: Description of Pathway from MSigDB.
- `setSize` <dbl>: ratio of input proteins that are annotated in a term.
- `enrichmentScore` <dbl>: Enrichment score, degree to which a gene set is over-represented at the top or bottom of the ranked list of genes.
- `NES` <dbl>: Normalized Enrichment Score, normalized to account for differences in gene set size and in correlations between gene sets and expression data sets. NES can be used to compare analysis results across gene sets.
- `pvalue` <dbl>: p-value of enrichment.
- `p.adjust` <dbl>: Adjusted p-value (Benjamini-Hochberg).
- `qvalue` <dbl>: false discovery rate, the estimated probability that the normalized enrichment score represents a false positive finding.
- `rank` <dbl>: the position in the ranked list where the maximum enrichment score occurred.
- `leading_edge` <chr>: contains tags, list, and signal. Tags gives an indication of the percentage of genes contributing to the enrichment score. List gives an indication of where in the list the enrichment score is obtained. Signal represents the enrichment signal strength and combines the tag and list.
- `core_enrichment` <chr>: list of input proteins (Gene Symbols) annotated in a term delimited by “/”.

# 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)

- `df`: NPX data frame in long format should minimally contain `SampleID`, `NPX` and column that will be used for grouping/coloring.
- `color_g`: Character value indicating the column name that should be used as fill color. 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.
- `config`: Object of class `umap.config`, specifying the parameters for the UMAP algorithm.
- `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`: Logical. Perform the UMAP per panel. Default `FALSE`.
- `outlierDefX`: (Optional) The number standard deviations along the UMAP dimension plotted on the x-axis that defines an outlier.
- `outlierDefY`: (Optional) The number standard deviations along the UMAP dimension plotted on the y-axis that defines an outlier.
- `OutlierLines`: Logical. Draw dashed lines at `+/-outlierDef[X,Y]` standard deviations from the mean of the plotted UMAP dimensions. Default `FALSE`.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `quiet`: Logical. If `TRUE`, the resulting plot is not printed. Default: `FALSE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

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

### 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt` and a grouping variable.
- `variable`: Single character value indicating the column name to use as a grouping variable in the x axis.
- `olinkid_list`: Character vector of OlinkID’s that should be used for the boxplot. If not specified, all assays in `df` are used.
- `posthoc_results`: Data frame from ANOVA posthoc analysis. This data frame need to be generated using the `olink_anova_posthoc()` function.
- `ttest_results`: Data frame from t-test analysis. This data frame need to be generated using the `olink_ttest()` function.
- `verbose`: Logical. Flag indicating if plots shall be printed additionally to assigned to a list variable. Default: `FALSE`.
- `number_of_proteins_per_plot`: Number of boxplots to include in the facets plot. Default 6.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    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]]

    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]]

### 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

- `df`: NPX data frame in long format should minimally contain protein name (`Assay`), `OlinkID`, `UniProt`, `Panel` and 1-2 variables with at least 2 levels and subject ID (`SubjectID`).
- `variable`: Single character value or character array. In case of single character then that should represent a column in the `df.` Otherwise, if length > 1, the included variable names will be used in crossed analyses. It can also accept the notations ‘:’ or ‘*’.
- `outcome`: Name of the column from `df` that contains the dependent variable. Default: “NPX”.
- `random`: Single character value or character array with random effects.
- `covariates`: Single character value or character array. Default: `NULL.` Confounding factors to include in the analysis. In case of single character then that should represent a column in the `df`. It can also accept the notations ‘:’ or ‘*’, while crossed analysis will not be inferred from main effects.
- `x_axis_variable`: Character. Which main effect to use as x-axis in the plot.
- `col_variable`: Character. If provided, the interaction effect `col_variable:x_axis_variable` will be plotted with `x_axis_variable` on the x-axis and `col_variable` as color.
- `number_of_proteins_per_plot`: Number plots to include in the list of point-range plots. Defaults to 6 plots per figure.
- `verbose`: Logical. If information about removed samples, factor conversion and final model formula is to be printed to the console. Default: `TRUE`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    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]]

### 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

- `enrich_results`: data frame of enrichment results from `olink_pathway_enrichment()`.
- `test_results`: filtered results from statistical test with `Assay`, `OlinkID`, and `estimate` columns.
- `method`: method used in `olink_pathway_enrichment()`, “GSEA” or “ORA”. Default is “GSEA”.
- `keyword`: (optional) keyword to filter enrichment results on, if not specified, displays top terms.
- `number_of_terms`: number of terms to display, default is 20.

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

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

### 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

- `enrich_results`: data frame of enrichment results from `olink_pathway_enrichment()`.
- `method`: method used in `olink_pathway_enrichment()` “GSEA” or “ORA”. Default is “GSEA”.
- `keyword`: (optional) keyword to filter enrichment results on, if not specified, displays top terms.
- `number_of_terms`: number of terms to display, default is 20.

### 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

- `df`: NPX data frame in long format which should minimally contain `SampleID`, `NPX`, `OlinkID`, `Assay.` Optionally, columns of choice for annotations.
- `variable_row_list`: Columns in `df` to be annotated for rows in the heatmap.
- `variable_col_list`: Columns in `df` to be annotated for columns in the heatmap.
- `center_scale`: Logical. If data should be centered and scaled across assays. Default: `TRUE`.
- `cluster_rows`: Logical. Determining if rows should be clustered. Default: `TRUE`.
- `cluster_cols`: Logical. Determining if columns should be clustered. Default: `TRUE`.
- `show_rownames`: Logical. Determining if row names are shown. Default: `TRUE`.
- `show_colnames`: Logical. Determining if column names are shown. Default: `TRUE`.
- `annotation_legend`: Logical. Determining if legend for annotations should be shown. Default: `TRUE`.
- `fontsize`: Fontsize for all text. Default: 10.
- `na_col`: Color of the cells with NA values. Default: “Black”.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    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
    )

### 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

- `p.val_tbl`: a data frame of results generated by `olink_ttest()`.
- `x_lab`: Optional. Character value to use as the x-axis label.
- `olinkid_list`: Optional. Character vector of proteins (`OlinkID`) to label in the plot. If not provided, by default the function will label all significant proteins.

    # 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
    )

### 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()

## 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()

## 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

- `df`: NPX data frame generated from between-product bridging in long format. Should minimally contain `Assay`, `OlinkID`, `OlinkID_E3072`, and all data points corresponding to bridging samples from the reference project and the new project.
- `median_counts_threshold`: Integer indicating minimum median counts allowed for each platform. If either platform has median counts below 150 for an assay, the assay fails the counts criteria when evaluating bridgeability. Default: 150.
- `min_counts`: Integer indicating minimum counts allowed for a data point. If any data point in the bridge normalized dataframe contains fewer than the defined minimum count cutoff, it is excluded from the bridgeability assessment and corresponding figures. Default: 10.
- `bridge_sampleid`: Character vector containing overlapping SampleIDs between the two bridging projects. If this argument is not provided, the function will look for overlapping `SampleID` values between the two projects in the bridged dataframe. Default: `NULL`.
- `check_log`: Named list returned by `check_npx()`. If `NULL`, `check_npx()` is run internally.

    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]]

### Function output

A list of objects of class `ggplot`.

## Contact Us

We are always happy to help. Email us with any questions:

- biostat@olink.com for statistical services and general stats questions

- support@olink.com for Olink lab product and technical support

- info@olink.com for more information

## Legal Disclaimer

© 2026 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/.