Olink® Analyze

The goal of Olink® Analyze is to provide a versatile toolbox to enable easy and smooth handling of Olink NPX data to speed up your proteomic research. Olink® Analyze provides functions ranging from reading Olink NPX data as exported by NPX Manager to various statistical tests and modelling, via different QC plot functions. Thereby providing a convenient pipeline for your Olink NPX data analysis.

Installation

Olink® Analyze is now available on CRAN: https://cran.r-project.org/web/packages/OlinkAnalyze/index.html

install.packages("OlinkAnalyze")

Vignette

browseVignettes("OlinkAnalyze")

Usage

Reading Olink NPX data

# open package
library(OlinkAnalyze)

# reading Olink NPX data 
my_NPX_data <- read_NPX(filename = "path/to/my_NPX_data.xlsx")

QC plot functions

There are several plot functions, below follows two examples using the package provided npx_data1 dataset:

# visualize the NPX distribution per sample per panel, example for one panel
olink_dist_plot(npx_data1 %>% filter(Panel == 'Olink CARDIOMETABOLIC')) +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  scale_fill_manual(values = c('turquoise3', 'red'))

# visualize potential outliers by IQR vs. sample median per panel, example for one panel
olink_qc_plot(npx_data1 %>% filter(Panel == 'Olink CARDIOMETABOLIC')) +
  scale_color_manual(values = c('turquoise3', 'red'))

Normalization

Olink® Analyze provides several means of normalization when analyzing multiple datasets. Below follows an example of reference sample (aka bridge) normalization using the two package provided npx_data1 and npx_data2 datasets:

# identify bridge samples
bridge_samples <- intersect(x = npx_data1$SampleID,
               y = npx_data2$SampleID)

# bridge normalize
bridge_normalized_data <- olink_normalization(df1 = npx_data1,
                        df2 = npx_data2,
                        overlapping_samples_df1 = bridge_samples,
                        df1_project_nr = "20200001",
                        df2_project_nr = "20200002",
                        reference_project = "20200001")

Statistical tests and models

Olink® Analyze provides several statistical tests and model tools. Below follows an example of how to perform a t-test and how to visualize the t-test output in a volcano plot using the package provided npx_data1 dataset:

# t-test npx_data1
ttest_results_NPX1 <- olink_ttest(df = npx_data1,
                variable = "Treatment")

# select names of the top #10 most significant proteins
ttest_sign_NPX1 <- ttest_results_NPX1 %>%
    head(n=10) %>%
    pull(OlinkID)

# volcano plot with annotated top #10 most significant proteins
olink_volcano_plot(p.val_tbl = ttest_results_NPX1,
                olinkid_list = ttest_sign_NPX1) +
  scale_color_manual(values = c('turquoise3', 'red'))

Learn more

Please see the function specific help pages. Moreover, Olink® Analyze includes two simulated NPX datasets for your convenience to help you explore the package and its functions.

Issues

Please report any issues (good or bad) to <biostattools[a]olink.com> or use the github issue function.

Alternative install methods

To install directly from the github repository:

# install.packages("remotes")
remotes::install_github(repo ='Olink-Proteomics/OlinkRPackage/OlinkAnalyze', ref = "main", build_vignettes = TRUE)

To install Olink Analyze into a new conda environment:

conda create -n OlinkAnalyze -c conda-forge r-olinkanalyze

Credits

Olink® Analyze is developed and maintained by the Olink Proteomics Data Science Team.