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Showing content from https://github.com/Olink-Proteomics/OlinkRPackage below:

Olink-Proteomics/OlinkRPackage: Olink R package: A collection of functions to facilitate analysis of proteomic data from Olink. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

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.

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

install.packages("OlinkAnalyze")

browseVignettes("OlinkAnalyze")

# open package
library(OlinkAnalyze)

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

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'))

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")

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'))

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.

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

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

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

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