---
title: "Olink® Analyze Vignette"
author: "Olink DS team"
date: "`r Sys.Date()`"
output: 
  html_vignette:
    toc: true
    toc_depth: 2
    includes:
      in_header: ../man/figures/logo.html
vignette: >
  %\VignetteIndexEntry{Olink® Analyze Vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  fig.width = 6,
  fig.height = 3,
  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
> will be published on CRAN soon. 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.

```{r, eval=FALSE}
install.packages("OlinkAnalyze")
```

# List of functions

**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 plate
-   *olink_bridgeselector* Select bridge samples
-   *olink_normalization* Normalization of all proteins (by OlinkID)
-   *olink_lod* Calculation of Limit of Detection for Explore 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 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

## 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* (that converts the data into a long format) 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 panels 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](https://olink.com/knowledge/faq?query=quality%20control) by
searching "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, is Olink®’s unit of 
protein expression level in a log<sub>2</sub> scale. The majority of the
functions of this package use NPX values for calculations. Read more about NPX
in the Olink [FAQ](https://olink.com/knowledge/faq?query=what%20is%20npx)
(Search "What is NPX?") or in Olink's Data normalization and standardization
[white paper](https://7074596.fs1.hubspotusercontent-na1.net/hubfs/7074596/05-white%20paper%20for%20website/1096-olink-data-normalization-white-paper.pdf).

**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)

The 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.

```{r message=FALSE, eval=FALSE}
data <- OlinkAnalyze::read_NPX("~/NPX_file_location.xlsx")
```

### Function output

A tibble in long format containing:

-   SampleID: Sample names or IDs.
-   Index: Unique number for each SampleID. It is used to make up for non unique
sample IDs.
-   OlinkID: Unique ID for each assay assigned by Olink. In case the assay is
included in more than one panels 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: Indication whether the sample passed Olink QC. More information
about Olink quality control metrics can be found in our [FAQ](https://olink.com/knowledge/faq?query=quality%20control)
(Search "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, is Olink’s unit of protein expression
level in a log<sub>2</sub> scale. The majority of the functions of this package
use NPX values for calculations. Read more about NPX in the Olink [FAQ](https://olink.com/knowledge/faq?query=what%20is%20npx)
(Search "What is NPX?") or in Olink's Data normalization and standardization
[white paper](https://7074596.fs1.hubspotusercontent-na1.net/hubfs/7074596/05-white%20paper%20for%20website/1096-olink-data-normalization-white-paper.pdf).

## Read multiple NPX data files (read_NPX)

In order to import multiple NPX data files at once, the read_NPX function can be
used in combination with the list.files, lapply and dplyr::bind_rows functions,
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.

```{r message=FALSE, eval=FALSE}
# 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 = 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 OlinkAnalyze functions,
allowing each function to skip its own internal check and improve performance.

### 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.

```{r message=FALSE, eval=FALSE}
# Check NPX data quality and format
check_npx_result <- OlinkAnalyze::check_npx(
  df = OlinkAnalyze::npx_data1
)
```

### 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
    format (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 additional SampleIDs to remove.
    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`.

```{r message=FALSE, eval=FALSE}
# Clean the NPX data using the check_npx output
npx_clean <- OlinkAnalyze::clean_npx(
  df = OlinkAnalyze::npx_data1,
  check_log = check_npx_result
)
```

### 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.

**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.

```{r message=FALSE, eval=FALSE}
# Check NPX data quality and format
check_npx_clean <- OlinkAnalyze::check_npx(
  df = npx_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 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 dataframes/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 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 dataframes is adjusted to another
using overlapping samples (bridge samples). The overlapping samples should have
the same IDs between dataframes. 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
OlinkAnalyzeVignettes.
-   **Subset normalization**: A subset of samples is used to normalize two
dataframes, one of which is used as a reference_project. Adjustment is made
using the differences of medians between the sample subsets from the two
dataframes. 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 dataframe. 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 Explore 3072 data to 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
OlinkAnalyzeVignettes.

## Integrating Explore NPX LOD (olink_lod)

The olink_lod function adds LOD information to an Explore HT or Explore 3072 NPX
dataframe. This function can incorporate LOD based on either an Explore
dataset's negative controls or using predetermined fixed LOD values, which can
be downloaded from the Document Download Center at
[olink.com](https://olink.com/knowledge/documents), or using both methods. The
default LOD calculation method is based off of 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 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.

```{r message=FALSE, eval=FALSE}
# Run check_npx and clean_npx before analysis
OlinkAnalyze::olink_ttest(
  df = npx_clean,
  variable = "Treatment",
  check_log = check_npx_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.

```{r message=FALSE, eval=FALSE}
OlinkAnalyze::olink_wilcox(
  df = npx_clean,
  variable = "Treatment",
  check_log = check_npx_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 for 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. Default: False. Returns F-test results for the covariates. Note: Adjusted p-values will be NA for covariates.
-   verbose: Logical. Default: True. If information about removed samples, factor conversion and final model formula is to be printed to the console.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run internally.

```{r message=FALSE, eval=FALSE}
# One-way ANOVA, no covariates
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = npx_clean,
  variable = "Site",
  check_log = check_npx_clean
)
# Two-way ANOVA, no covariates
anova_results_twoway <- OlinkAnalyze::olink_anova(
  df = npx_clean,
  variable = c("Site", "Time"),
  check_log = check_npx_clean
)
# One-way ANOVA, Treatment as covariates
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = npx_clean,
  variable = "Site",
  covariates = "Treatment",
  check_log = check_npx_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: Term on which to perform the post-hoc analysis. Character vector.
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. Default: True. If information about removed samples,
factor conversion and final model formula is to be printed to the console.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run internally.

```{r message=FALSE, eval=FALSE}
# calculate the p-value for the ANOVA
anova_results_oneway <- OlinkAnalyze::olink_anova(
  df = npx_clean,
  variable = "Site",
  check_log = check_npx_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 = npx_clean,
  olinkid_list = anova_results_oneway_sign,
  variable = "Site",
  effect = "Site",
  check_log = check_npx_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` 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 ID.
-   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. Default: False. Returns F-test results for the
covariates. Note: Adjusted p-values will be NA for covariates.
-   verbose: Logical. Default: True. If information about removed samples,
factor conversion and final model formula is to be printed to the console.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run
internally.

```{r message=FALSE, eval=FALSE}
# Linear mixed model with one variable.
lmer_results_oneway <- OlinkAnalyze::olink_lmer(
  df = npx_clean,
  variable = "Site",
  random = "Subject",
  check_log = check_npx_clean
)

# Linear mixed model with two variables.
lmer_results_twoway <- OlinkAnalyze::olink_lmer(
  df = npx_clean,
  variable = c("Site", "Treatment"),
  random = "Subject",
  check_log = check_npx_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 ID.
-   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: Term on which to perform the post-hoc analysis. Character vector.
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. Default: True. If information about removed samples,
factor conversion and final model formula is to be printed to the console.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run
internally.

```{r message=FALSE, eval=FALSE}
# Linear mixed model with two variables.
lmer_results_twoway <- OlinkAnalyze::olink_lmer(
  df = npx_clean,
  variable = c("Site", "Treatment"),
  random = "Subject",
  check_log = check_npx_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 = npx_clean,
  olinkid_list = lmer_results_twoway_sign,
  variable = c("Site", "Treatment"),
  random = "Subject",
  effect = "Treatment",
  check_log = check_npx_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 function, 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. MSigDB includes curated gene sets (C2) and ontology gene
sets (C5) which encompasses Reactome, KEGG, and GO. This function performs
enrichment using the *gsea* or *enrich* functions from clusterProfiler 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.
Posthoc 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_pal" 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

-   data: NPX data frame in long format with columns Assay, OlinkID, UniProt,
SampleID, QC_Warning, NPX, and LOD.
-   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", and "Reactome".
-   organism: String of name of organism. Must be either "human" or "mouse".

```{r message=FALSE, eval=FALSE}
ttest_results <- OlinkAnalyze::olink_ttest(
  df = npx_df_clean,
  variable = "Treatment",
  alternative = "two.sided",
  check_log = check_npx_clean
)

gsea_results <- OlinkAnalyze::olink_pathway_enrichment(
  df = npx_df_clean,
  test_results = ttest_results
)

ora_results <- OlinkAnalyze::olink_pathway_enrichment(
  df = npx_df_clean,
  test_results = ttest_results,
  method = "ORA"
)
```

### 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 PC2 vs PC1) 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 assay(s). Unique sample names are
required. Imputation by median value is done for assays with missingness \<10%
and 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 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. Default: True. If information about removed samples,
factor conversion and final model formula is to be printed to the console.
-   quiet: Logical. Default: False. If TRUE, the resulting plot is not printed.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run
internally.

```{r message=FALSE, eval=FALSE}
OlinkAnalyze::olink_umap_plot(
  df = npx_clean,
  color_g = "QC_Warning",
  byPanel = TRUE,
  check_log = check_npx_clean
)
```

```{r message=FALSE, echo = FALSE}
knitr::include_graphics(
  path = normalizePath("../man/figures/olink_umap_plot.png"),
  error = FALSE
)
```

### Function output

A list of objects of class *ggplot* (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 ttest analysis. This data frame need to be
generated using the olink_ttest() function.
-   verbose: Logical. Default: False. Flag indicating if plots shall be printed
additionally to assigned to a list variable.
-   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.

```{r message=FALSE, eval=FALSE}
plot <- npx_clean |>
  dplyr::filter(
    !is.na(.data[["Site"]])
  ) |> # removing missing values which exist for Site
  OlinkAnalyze::olink_boxplot(
    variable = "Site",
    olinkid_list = c("OID00488", "OID01276"),
    number_of_proteins_per_plot = 2L,
    check_log = check_npx_clean
  )

plot[[1L]]
```

```{r message=FALSE, echo = FALSE}
knitr::include_graphics(
  path = normalizePath("../man/figures/olink_boxplot.png"),
  error = FALSE
)
```

```{r message=FALSE, eval=FALSE}
anova_posthoc_results <- npx_clean |>
  OlinkAnalyze::olink_anova_posthoc(
    olinkid_list = c("OID00488", "OID01276"),
    variable = "Site",
    effect = "Site",
    check_log = check_npx_clean
  )

plot2 <- npx_clean |>
  stats::na.omit() |> # removing missing values which exists 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_npx_clean
  )

plot2[[1L]]
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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 *plots* 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 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.
-   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. Default: True. If information about removed samples,
factor conversion and final model formula is to be printed to the console.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run internally.

```{r message=FALSE, eval=FALSE}
plot <- OlinkAnalyze::olink_lmer_plot(
  df = npx_clean,
  olinkid_list = c("OID01216", "OID01217"),
  variable = c("Site", "Treatment"),
  x_axis_variable = "Site",
  col_variable = "Treatment",
  random = "Subject",
  check_log = check_npx_clean
)
plot[[1L]]
```

```{r message=FALSE, fig.width=8, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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 *plots* 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" (default) or "ORA")
-   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

```{r  message=FALSE, eval=FALSE}
OlinkAnalyze::olink_pathway_heatmap(
  enrich_results = gsea_results,
  test_results = ttest_results
)
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../man/figures/olink_pathway_heatmap_gsea.png"),
  error = FALSE
)
```

```{r message=FALSE, fig.height=4, fig.width=8, eval=FALSE}
OlinkAnalyze::olink_pathway_heatmap(
  enrich_results = ora_results,
  test_results = ttest_results,
  method = "ORA",
  keyword = "immune"
)
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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

-   enrich_results: data frame of enrichment results from
`olink_pathway_enrichment()`
-   method: method used in olink_pathway_enrichment ("GSEA" (default) or "ORA")
-   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 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 OlinkAnalyzeVignettes.

## Heatmap (olink_heatmap_plot)

The olink_heatmap_plot function generates a heatmap for all samples and
proteins. By default, the heatmap centers and scales NPX across all 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. Default: True. If data should be centered and scaled
across assays.
-   cluster_rows: Logical. Default: True. Determining if rows should be
clustered.
-   cluster_cols: Logical. Default: True. Determining if columns should be
clustered.
-   show_rownames: Logical. Default: True. Determining if row names are shown.
-   show_colnames: Logical. Default: True. Determining if column names are
shown.
-   annotation_legend: Logical. Default: True. Determining if legend for
annotations should be shown.
-   fontsize. Default: 10. Fontsize for all text.
-   na_col. Default: Black. Color of the cells with NA.
-   check_log: Named list returned by `check_npx`. If `NULL`, `check_npx` is run
internally.

```{r message=FALSE, eval=FALSE}
first10 <- npx_clean |>
  dplyr::pull(
    .data[["OlinkID"]]
  ) |>
  unique() |>
  utils::head(n = 10L)

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

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

OlinkAnalyze::olink_heatmap_plot(
  df = npx_data_small,
  variable_row_list = "Treatment",
  check_log = check_npx_clean
)
```

```{r message=FALSE, fig.height=4, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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 -log<sub>10</sub>(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.

```{r message=FALSE, eval=FALSE}
# perform t-test
ttest_results <- OlinkAnalyze::olink_ttest(
  df = npx_clean,
  variable = "Treatment",
  check_log = check_npx_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
)
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../man/figures/olink_volcano_plot.png"),
  error = FALSE
)
```

### Function output

An object of class *ggplot*.

## Theming function (set_plot_theme)

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

```{r message=FALSE, eval=FALSE}
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()
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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 sets a coherent coloring theme for the plots by adding it to a
ggplot object. It is mainly used for aesthetic reasons.

```{r message=FALSE, eval=FALSE}
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()
```

```{r message=FALSE, echo=FALSE}
knitr::include_graphics(
  path = normalizePath("../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 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.

```{r message=FALSE, eval=FALSE}
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]]
```

```{r message=FALSE, echo=FALSE, out.width="600px"}
knitr::include_graphics(
  path = normalizePath("../man/figures/bridgeable_plt_MedianCenter.png"),
  error = FALSE
)
```

### 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

© 2025 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/](https://olink.com/patents/).
