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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.
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Olink-Proteomics/OlinkRPackage
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The goal of Olink® Analyze is to provide a versatile toolbox to enableeasy and smooth handling of Olink NPX data to speed up your proteomicresearch. Olink® Analyze provides functions ranging from reading OlinkNPX data as exported by NPX Manager to various statistical tests andmodelling, via different QC plot functions. Thereby providing aconvenient 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 packagelibrary(OlinkAnalyze)# reading Olink NPX datamy_NPX_data<- read_NPX(filename="path/to/my_NPX_data.xlsx")
There are several plot functions, below follows two examples using thepackage provided npx_data1 dataset:
# visualize the NPX distribution per sample per panel, example for one panelolink_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 panelolink_qc_plot(npx_data1 %>% filter(Panel=='Olink CARDIOMETABOLIC'))+ scale_color_manual(values= c('turquoise3','red'))
Olink® Analyze provides several means of normalization when analyzingmultiple datasets. Below follows an example of reference sample (akabridge) normalization using the two package provided npx_data1 andnpx_data2 datasets:
# identify bridge samplesbridge_samples<- intersect(x=npx_data1$SampleID,y=npx_data2$SampleID)# bridge normalizebridge_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. Belowfollows an example of how to perform a t-test and how to visualize thet-test output in a volcano plot using the package provided npx_data1dataset:
# t-test npx_data1ttest_results_NPX1<- olink_ttest(df=npx_data1,variable="Treatment")# select names of the top #10 most significant proteinsttest_sign_NPX1<-ttest_results_NPX1 %>% head(n=10) %>% pull(OlinkID)# volcano plot with annotated top #10 most significant proteinsolink_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® Analyzeincludes two simulated NPX datasets for your convenience to help youexplore the package and its functions.
Please report any issues (good or bad) to <biostattools[a]olink.com>or use the githubissuefunction.
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 newconda environment:
conda create -n OlinkAnalyze -c conda-forge r-olinkanalyze
Olink® Analyze is developed and maintained by the Olink Proteomics DataScience Team.
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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.
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