Package 'OlinkAnalyze'

Description

Throw informative warnings if a dataset appears to have problems

Usage

check_data_completeness(df)

Arguments

Value

None. Used for side effects (warnings)

Examples

npx_data1 %>%
    dplyr::mutate(NPX = dplyr::if_else(
                         SampleID == "A1" & Panel == "Olink Cardiometabolic",
                         NA_real_,
                         NPX)) %>%
    OlinkAnalyze:::check_data_completeness()

Description

Sample manifest is generated randomly to demonstrate use of functions in this package.

Usage

manifest

Format

This dataset contains columns:

SubjectID

Subject Identifier, A-Z

Visit

Visit Number, 1-6

SampleID

138 unique sample IDs

Site

Site1 or Site2

Details

A tibble with 138 rows and 4 columns. This manifest contains 26 example subjects, with 6 visits and 2 sites.

Description

The function is used by norm_internal_subset and norm_internal_bridge to combine the reference dataset that has Adj_factor = 0 and the non-reference dataset that used the adjustment factors provided in adj_fct_df.

Usage

norm_internal_adjust(
  ref_df,
  ref_name,
  ref_cols,
  not_ref_df,
  not_ref_name,
  not_ref_cols,
  adj_fct_df
)

Arguments

Details

The function calls norm_internal_adjust_ref and norm_internal_adjust_not_ref and combines their outputs.

Value

Tibble or ArrowObject with the normalized dataset.

Author(s)

Klev Diamanti

Description

Add adjustment factors to a dataset

Usage

norm_internal_adjust_not_ref(df, name, cols, adj_fct_df, adj_fct_cols)

Arguments

Value

Tibble or ArrowObject with the normalized dataset with additional columns "Project" and "Adj_factor".

Author(s)

Klev Diamanti

Description

Modify the reference dataset to be combined with the non-reference normalized dataset

Usage

norm_internal_adjust_ref(ref_df, ref_name)

Arguments

Value

Tibble or ArrowObject with the reference dataset with additional columns "Project" and "Adj_factor".

Author(s)

Klev Diamanti

Description

The function computes the median value of the the quantification method for each Olink assay in the set of samples samples, and it adds the column Project.

Usage

norm_internal_assay_median(df, samples, name, cols)

Arguments

Details

This function is typically used by internal functions norm_internal_subset and norm_internal_reference_median that compute median quantification value for each assay across multiple samples specified by samples.

Value

Tibble or ArrowObject with one row per Olink assay and the columns OlinkID, Project, and assay_med

Author(s)

Klev Diamanti

Description

Internal bridge normalization function

Usage

norm_internal_bridge(
  ref_df,
  ref_samples,
  ref_name,
  ref_cols,
  not_ref_df,
  not_ref_name,
  not_ref_cols
)

Arguments

Value

Tibble or ArrowObject with the normalized dataset.

Author(s)

Klev Diamanti

Description

Internal function normalizing Olink Explore 3k to Olink Explore 3072

Usage

norm_internal_cross_product(
  ref_df,
  ref_samples,
  ref_name,
  ref_cols,
  not_ref_df,
  not_ref_name,
  not_ref_cols
)

Arguments

Value

Tibble or ArrowObject with a dataset with the following additional columns:

• OlinkID_E3072: Corresponding assay identifier from Olink Explore 3072.

• Project: Project of origin.

• BridgingRecommendation: Recommendation of whether the assay is bridgeable or not. One of "NotBridgeable", "MedianCentering", or "QuantileSmoothing".

• MedianCenteredNPX: NPX values adjusted based on the median of the pair-wise differences of NPX values between bridge samples.

• QSNormalizedNPX: NPX values adjusted based on the quantile smoothing normalization among bridge samples.

Author(s)

Klev Diamanti

Description

Internal reference median normalization function

Usage

norm_internal_reference_median(
  ref_df,
  ref_samples,
  ref_name,
  ref_cols,
  reference_medians
)

Arguments

Value

Tibble or ArrowObject with the normalized dataset.

Author(s)

Klev Diamanti

Description

This function handles cases when specific columns referring to the same thing are named differently in df1 and df2 normalization datasets. It only renames columns panel_version, qc_warn, and assay_warn based on their names in the reference dataset.#'

Usage

norm_internal_rename_cols(ref_cols, not_ref_cols, not_ref_df)

Arguments

Value

not_ref_df with updated column names.

Author(s)

Klev Diamanti

Description

This function performs subset normalization using a subset of the samples from either or both reference and non-reference datasets. When all samples from each dataset are used, the function performs intensity normalization.

Usage

norm_internal_subset(
  ref_df,
  ref_samples,
  ref_name,
  ref_cols,
  not_ref_df,
  not_ref_samples,
  not_ref_name,
  not_ref_cols
)

Arguments

Value

Tibble or ArrowObject with the normalized dataset.

Author(s)

Klev Diamanti

Description

Data is generated randomly to demonstrate use of functions in this package.

Usage

npx_data1

Format

In addition to standard read_NPX() columns, this dataset also contains columns:

Subject

Subject Identifier

Treatment

Treated or Untreated

Site

Site indicator, 5 unique values

Time

Baseline, Week.6 and Week.12

Project

Project ID number

Details

A tibble with 29,440 rows and 17 columns. Dataset npx_data1 is an Olink NPX data file (tibble) in long format with 158 unique Sample ID's (including 2 repeats each of control samples: CONTROL_SAMPLE_AS 1 CONTROL_SAMPLE_AS 2). The data also contains 1104 assays (uniquely identified using OlinkID) over 2 Panels.

Description

Data is generated randomly to demonstrate use of functions in this package. The format is very similar to data(npx_data1). Both datasets can be used together to demonstrate the use of normalization functionality.

Usage

npx_data2

Format

In addition to standard read_NPX() columns, this dataset also contains columns:

Subject

Subject Identifier

Treatment

Treated or Untreated

Site

Site indicator, 5 unique values

Time

Baseline, Week.6 and Week.12

Project

Project ID number

Details

A tibble with 32,384 rows and 17 columns. npx_data2 is an Olink NPX data file (tibble) in long format with 174 unique Sample ID's (including 2 repeats each of control samples: CONTROL_SAMPLE_AS 1 CONTROL_SAMPLE_AS 2). The data also contains 1104 assays (uniquely identified using OlinkID) over 2 Panels. This dataset also contain 16 bridge samples with SampleID's that are also present in data(npx_data1). These sample ID's are: A13, A29, A30, A36, A45, A46, A52, A63, A71, A73, B3, B4, B37, B45, B63, B75

Description

Performs an ANOVA F-test for each assay (by OlinkID) in every panel using car::Anova and Type III sum of squares. The function handles both factor and numerical variables and/or covariates.

Samples that have no variable information or missing factor levels are automatically removed from the analysis (specified in a message if verbose = TRUE). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = TRUE). Numerical variables are not converted to factors. Control samples should be removed before using this function. Control assays (AssayType is not "assay", or Assay contains "control" or "ctrl") should be removed before using this function. If a numerical variable is to be used as a factor, this conversion needs to be done on the dataframe before the function call.

Crossed 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')

Inference is specified in a message if verbose = TRUE.
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 takes precedence. The formula notation of the final model is specified in a message if verbose = TRUE.

Adjusted p-values are calculated by stats::p.adjust according to 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.

Usage

olink_anova(
  df,
  variable,
  outcome = "NPX",
  covariates = NULL,
  model_formula,
  return.covariates = FALSE,
  verbose = TRUE
)

Arguments

Value

A "tibble" containing the ANOVA results for every protein. The tibble is arranged by ascending p-values. Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• df: "numeric" degrees of freedom

• sumsq: "numeric" sum of square

• meansq: "numeric" mean of square

• statistic: "numeric" value of the statistic

• p.value: "numeric" nominal p-value

• Adjusted_pval: "numeric" adjusted p-value for the test (Benjamini&Hochberg)

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)

npx_df <- npx_data1 |> filter(!grepl('control|ctrl',SampleID, ignore.case = TRUE))

#One-way ANOVA, no covariates.
#Results in a model NPX~Time
anova_results <- olink_anova(df = npx_df, variable = "Time")

#Two-way ANOVA, one main effect covariate.
#Results in model NPX~Treatment*Time+Site.
anova_results <- olink_anova(df = npx_df,
                             variable=c("Treatment:Time"),
                             covariates="Site")

#One-way ANOVA, interaction effect covariate.
#Results in model NPX~Treatment+Site:Time+Site+Time.
anova_results <- olink_anova(df = npx_df,
                             variable="Treatment",
                             covariates="Site:Time")

Description

Performs a post hoc ANOVA test using emmeans::emmeans with Tukey p-value adjustment per assay (by OlinkID) for each panel at confidence level 0.95. See olink_anova for details of input notation.

The function handles both factor and numerical variables and/or covariates. Control samples should be removed before using this function. Control assays (AssayType is not "assay", or Assay contains "control" or "ctrl") should be removed before using this function. The posthoc 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).

Usage

olink_anova_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  covariates = NULL,
  outcome = "NPX",
  model_formula,
  effect,
  effect_formula,
  mean_return = FALSE,
  post_hoc_padjust_method = "tukey",
  verbose = TRUE
)

Arguments

Value

A "tibble" of posthoc tests for specified effect, arranged by ascending adjusted p-values. Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• contrast: "character" the groups that were compared

• estimate: "numeric" difference in mean NPX between groups

• conf.low: "numeric" confidence interval for the mean (lower end)

• conf.high: "numeric" confidence interval for the mean (upper end)

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)

npx_df <- npx_data1 |> filter(!grepl('control|ctrl',SampleID, ignore.case = TRUE))

#Two-way ANOVA, one main effect (Site) covariate.
#Results in model NPX~Treatment*Time+Site.
anova_results <- olink_anova(df = npx_df,
                             variable=c("Treatment:Time"),
                             covariates="Site")

#Posthoc test for the model NPX~Treatment*Time+Site,
#on the interaction effect Treatment:Time with covariate Site.

#Filtering out significant and relevant results.
significant_assays <- anova_results |>
filter(Threshold == 'Significant' & term == 'Treatment:Time') |>
select(OlinkID) |>
distinct() |>
pull()

#Posthoc, all pairwise comparisons
anova_posthoc_results <- olink_anova_posthoc(npx_df,
variable=c("Treatment:Time"),
covariates="Site",
olinkid_list = significant_assays,
effect = "Treatment:Time")


#Posthoc, treated vs untreated at each timepoint, adjusted for Site effect
anova_posthoc_results <- olink_anova_posthoc(npx_df,
model_formula = "NPX~Treatment*Time+Site",
olinkid_list = significant_assays,
effect_formula = "pairwise~Treatment|Time")

Description

Generates faceted boxplots of NPX vs. grouping variable(s) for a given list of proteins (OlinkIDs) using ggplot and ggplot2::geom_boxplot.

Usage

olink_boxplot(
  df,
  variable,
  olinkid_list,
  verbose = FALSE,
  number_of_proteins_per_plot = 6,
  posthoc_results = NULL,
  ttest_results = NULL,
  ...
)

Arguments

Value

A list of objects of class “ggplot” (the actual ggplot object is entry 1 in the list). Box and whisker plot of NPX (y-axis) by variable (x-axis) for each Assay

Examples

library(dplyr)
npx_df <- npx_data1 |> filter(!grepl('control|ctrl',SampleID, ignore.case = TRUE))
anova_results <- olink_anova(npx_df, variable = "Site")
significant_assays <- anova_results |>
    filter(Threshold == 'Significant') |>
    pull(OlinkID)
olink_boxplot(npx_df,
              variable = "Site",
              olinkid_list = significant_assays,
              verbose = TRUE,
              number_of_proteins_per_plot = 3)

Description

The bridge selection function will select a number of bridge samples based on the input data. It selects samples with good detection, which passes QC and cover a good range of the data. If possible, Olink recommends 8-16 bridge samples. When running the selector, Olink recommends starting at sampleMissingFreq = 0.10 which represents a maximum of 10\ data below LOD per sample. If there are not enough samples output, increase to 20\ The function accepts NPX Excel files with data < LOD replaced.

Usage

olink_bridgeselector(df, sampleMissingFreq, n)

Arguments

Value

A "tibble" with sample IDs and mean NPX for a defined number of bridging samples. Columns include:

• SampleID: Sample ID

• PercAssaysBelowLOD: Percent of Assays that are below LOD for the sample

• MeanNPX: Mean NPX for the sample

Examples

bridge_samples <- olink_bridgeselector(npx_data1, sampleMissingFreq = 0.1, n = 20)

Description

Olink color scale for discrete ggplots

Usage

olink_color_discrete(..., alpha = 1, coloroption = NULL)

Arguments

Value

No return value, called for side effects

Examples

library(ggplot2)

ggplot(mtcars, aes(x=wt, y=mpg, color=as.factor(cyl))) +
geom_point(size = 4) +
olink_color_discrete() +
theme_bw()

ggplot(mtcars, aes(x=wt, y=mpg, color=as.factor(cyl))) +
geom_point(size = 4) +
olink_color_discrete(coloroption = c('lightblue', 'red', 'green')) +
theme_bw()

Description

Olink color scale for continuous ggplots

Usage

olink_color_gradient(..., alpha = 1, coloroption = NULL)

Arguments

Value

No return value, called for side effects

Examples

library(ggplot2)

dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))

ggplot(dsub, aes(x, y, colour=diff)) +
geom_point() +
 theme_bw() +
 olink_color_gradient()

Description

Displays a bar chart for each plate representing the distribution of the given grouping variable on each plate using ggplot2::ggplot and ggplot2::geom_bar.

Usage

olink_displayPlateDistributions(data, fill.color)

Arguments

Value

An object of class "ggplot" showing the percent distribution of fill.color in each plate (x-axis)

See Also

• olink_plate_randomizer() for generating a plating scheme

• olink_displayPlateLayout() for visualizing the generated plate layouts

Examples

randomized.manifest <- olink_plate_randomizer(manifest)
olink_displayPlateDistributions(data=randomized.manifest,fill.color="Site")

Description

Displays each plate in a facet with cells colored by the given variable using ggplot and ggplot2::geom_tile.

Usage

olink_displayPlateLayout(
  data,
  fill.color,
  PlateSize = 96,
  num_ctrl = 8,
  rand_ctrl = FALSE,
  Product,
  include.label = FALSE
)

Arguments

Value

An object of class "ggplot" showing each plate in a facet with the cells colored by values in column fill.color in input data.

See Also

• olink_plate_randomizer() for generating a plating scheme

• olink_displayPlateDistributions() for validating that sites are properly randomized

Examples

randomized.manifest <- olink_plate_randomizer(manifest)
olink_displayPlateLayout(data = randomized.manifest, fill.color="Site")

Description

Generates boxplots of NPX vs. SampleID colored by QC_Warning (default) or any other grouping variable and faceted by Panel using ggplot and ggplot2::geom_boxplot.

Usage

olink_dist_plot(df, color_g = "QC_Warning", ...)

Arguments

Value

An object of class "ggplot" which displays NPX distribution for each sample per panel

Examples

olink_dist_plot(npx_data1, color_g = "QC_Warning")

Description

Olink fill scale for discrete ggplots

Usage

olink_fill_discrete(..., alpha = 1, coloroption = NULL)

Arguments

Value

No return value, called for side effects

Examples

library(ggplot2)

dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))

ggplot(dsub, aes(x, y, colour=diff)) +
geom_point() +
 theme_bw() +
 olink_fill_discrete()

Description

Olink fill scale for continuous ggplots

Usage

olink_fill_gradient(..., alpha = 1, coloroption = NULL)

Arguments

Value

No return value, called for side effects

Examples

library(ggplot2)

dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
ggplot(dsub, aes(x, y, colour=diff)) +
geom_point() +
 theme_bw() +
 olink_fill_gradient()

Description

Generates a heatmap using pheatmap::pheatmap of all samples from NPX data.

Usage

olink_heatmap_plot(
  df,
  variable_row_list = NULL,
  variable_col_list = NULL,
  center_scale = TRUE,
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  show_rownames = TRUE,
  show_colnames = TRUE,
  colnames = "both",
  annotation_legend = TRUE,
  fontsize = 10,
  na_col = "black",
  ...
)

Arguments

Details

The values are by default scaled across and centered in the heatmap. Columns and rows are by default sorted by by dendrogram. Unique sample names are required.

Value

An object of class ggplot, generated from the gtable returned by pheatmap::pheatmap.

Examples

library(dplyr)
npx_data <- npx_data1 %>%
      filter(!stringr::str_detect(SampleID,'CONT'))
try({ # This will fail if ggplotify is not installed
#Heatmap
  olink_heatmap_plot(df=npx_data)

#Heatmap with annotation
  olink_heatmap_plot(df=npx_data, variable_row_list = c('Time','Site'))

#Heatmap with calls from pheatmap
  olink_heatmap_plot(df=npx_data, cutree_rows = 3)
})

Description

Compute inter-quartile range (IQR) of multiplied by a fixed value

Usage

olink_iqr(df, quant_col, iqr_group, iqr_sd)

Arguments

Value

Input dataset with two additional columns, iqr and iqr_sd

Description

Fits a linear mixed effects model for every protein (by OlinkID) in every panel, using lmerTest::lmer and stats::anova. The function handles both factor and numerical variables and/or covariates.

Samples that have no variable information or missing factor levels are automatically removed from the analysis (specified in a message if verbose = TRUE). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = TRUE). Numerical variables are not converted to factors. If a numerical variable is to be used as a factor, this conversion needs to be done on the dataframe before the function call.

Crossed 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')

Inference is specified in a message if verbose = TRUE.
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 takes precedence.
The random variable only takes main effect(s).
The formula notation of the final model is specified in a message if verbose = TRUE.

Output p-values are adjusted by stats::p.adjust according to the Benjamini-Hochberg method (“fdr”). Adjusted p-values are logically evaluated towards adjusted p-value<0.05.

Usage

olink_lmer(
  df,
  variable,
  outcome = "NPX",
  random,
  covariates = NULL,
  model_formula,
  return.covariates = FALSE,
  verbose = TRUE
)

Arguments

Value

A "tibble" containing the results of fitting the linear mixed effects model to every protein by OlinkID, ordered by ascending p-value. Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• sumsq: "numeric" sum of square

• meansq: "numeric" mean of square

• NumDF: "integer" numerator of degrees of freedom

• DenDF: "numeric" denominator of decrees of freedom

• statistic: "numeric" value of the statistic

• p.value: "numeric" nominal p-value

• Adjusted_pval: "numeric" adjusted p-value for the test (Benjamini&Hochberg)

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

if (requireNamespace("lme4", quietly = TRUE) & requireNamespace("lmerTest", quietly = TRUE)){
  # Results in model NPX~Time*Treatment+(1|Subject)+(1|Site)
  lmer_results <- olink_lmer(df = npx_data1,
  variable=c("Time", 'Treatment'),
  random = c('Subject', 'Site'))
}

Description

Generates a point-range plot faceted by Assay using ggplot and ggplot2::geom_pointrange based on a linear mixed effects model using lmerTest:lmer and emmeans::emmeans. See olink_lmer for details of input notation.

Usage

olink_lmer_plot(
  df,
  variable,
  outcome = "NPX",
  random,
  olinkid_list = NULL,
  covariates = NULL,
  x_axis_variable,
  col_variable = NULL,
  number_of_proteins_per_plot = 6,
  verbose = FALSE,
  ...
)

Arguments

Value

A list of objects of class "ggplot" showing point-range plot of NPX (y-axis) over x_axis_variable for each assay (facet), colored by col_variable if provided.

Examples

library(dplyr)
if (requireNamespace("lme4", quietly = TRUE) & requireNamespace("lmerTest", quietly = TRUE)){
lmer_results <- olink_lmer(df = npx_data1,
                           variable=c("Time", 'Treatment'),
                           random = c('Subject'))

assay_list <- lmer_results %>%
    filter(Threshold == 'Significant' & term == 'Time:Treatment') %>%
    select(OlinkID) %>%
    distinct() %>%
    pull()

list_of_pointrange_plots <- olink_lmer_plot(df = npx_data1,
                                            variable=c("Time", 'Treatment'),
                                            random = c('Subject'),
                                            x_axis_variable = 'Time',
                                            col_variable = 'Treatment',
                                            verbose=TRUE,
                                            olinkid_list = assay_list,
                                            number_of_proteins_per_plot = 10)
}

Description

Similar to olink_lmer but performs a post hoc analysis based on a linear mixed model effects model using lmerTest::lmer and emmeans::emmeans on proteins. See olink_lmer for details of input notation.

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 variable. Degrees of freedom are estimated using Satterthwaite’s approximation. The posthoc 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 Tukey adjusted p-values.

Usage

olink_lmer_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  outcome = "NPX",
  random,
  model_formula,
  effect,
  effect_formula,
  covariates = NULL,
  mean_return = FALSE,
  post_hoc_padjust_method = "tukey",
  verbose = TRUE
)

Arguments

Value

A "tibble" containing the results of the pairwise comparisons between given variable levels for proteins specified in olinkid_list (or full df). Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• contrast: "character" the groups that were compared

• estimate: "numeric" difference in mean NPX between groups

• conf.low: "numeric" confidence interval for the mean (lower end)

• conf.high: "numeric" confidence interval for the mean (upper end)

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)
if (requireNamespace("lme4", quietly = TRUE) & requireNamespace("lmerTest", quietly = TRUE)){

lmer_results <- olink_lmer(df = npx_data1,
                           variable=c("Time", 'Treatment'),
                           random = c('Subject'))

assay_list <- lmer_results %>%
    filter(Threshold == 'Significant' & term == 'Time:Treatment') %>%
    select(OlinkID) %>%
    distinct() %>%
    pull()

results_lmer_posthoc <- olink_lmer_posthoc(df = npx_data1,
                                           olinkid_list = assay_list,
                                           variable=c("Time", 'Treatment'),
                                           effect = 'Time:Treatment',
                                           random = 'Subject',
                                           verbose = TRUE)

#Estimate treated vs untreated at each timepoint


results_lmer_posthoc <- olink_lmer_posthoc(df = npx_data1,
                                           olinkid_list = assay_list,
                                           model_formula = "NPX~Time*Treatment+(1|Subject)",
                                           effect_formula = "pairwise~Treatment|Time",
                                           verbose = TRUE)
}

Description

Calculate LOD using Negative Controls or Fixed LOD

Usage

olink_lod(data, lod_file_path = NULL, lod_method = "NCLOD")

Arguments

Value

A dataframe with 2 additional columns, LOD and PCNormalizedLOD if lod_method is FixedLOD or NCLOD. When Normalization = "Plate Control", LOD and PCNormalizedLOD are identical.

If lod_method is "Both", 4 additional columns will be added:

• NCLOD - LOD calculated from negative controls and normalized based on normalization column

• NCPCNormalizedLOD - PC Normalized LOD calculated from negative controls

• FixedLOD - LOD calculated from fixed LOD file and normalized based on normalization column

• FixedPCNormalizedLOD - PC Normalized LOD calculated from fixed LOD file

Examples

## Not run: 
   \donttest{
  try({ # This will fail if the files do not exist.

  # Import NPX data
    npx_data <- read_NPX("path/to/npx_file")

  # Estimate LOD from negative controls
    npx_data_lod_NC <- olink_lod(data = npx_data,  lod_method = "NCLOD")

  # Estimate LOD from fixed LOD
  ## Locate the fixed LOD file
    lod_file_path <- "path/to/lod_file"

    npx_data_lod_Fixed <- olink_lod(data = npx_data,
                                    lod_file_path = lod_file_path,
                                    lod_method = "FixedLOD")
     })
  }

## End(Not run)

Description

Compute median of quantified value

Usage

olink_median(df, quant_col, median_group)

Arguments

Value

Input dataset with one additional columns, median

Description

Compute outliers based on median +/- iqr_sd * IQR

Usage

olink_median_iqr_outlier(df, quant_col, group, iqr_sd)

Arguments

Value

Boolean vector with length equal to the number of input rows indicating outlier.

Description

Check datasets and reference_medians for Olink identifiers not shared across datasets.

Usage

olink_norm_input_assay_overlap(
  lst_df,
  reference_medians,
  lst_cols,
  norm_mode = norm_mode
)

Arguments

Value

A named list containing lst_df and reference_medians will assays shared across all datasets.

Author(s)

Klev Diamanti

Check inputs of olink_normalization function.

Description

This function is a wrapper of multiple help functions which check the inputs of the olink_normalization function.

Usage

olink_norm_input_check(
  df1,
  df2,
  overlapping_samples_df1,
  overlapping_samples_df2,
  df1_project_nr,
  df2_project_nr,
  reference_project,
  reference_medians
)

Arguments

Details

The following checks are performed:

• olink_norm_input_validate:

  • Determines the normalization to be performed by intersecting inputs with internal global variable olink_norm_mode_combos.

  • Returns the type of normalization to be performed from olink_norm_modes.

  • Message with the normalization type.

  • Error message if input is invalid.

• olink_norm_input_class:

  • Checks if all inputs are of the expected class:

    • df1, df2 and reference_medians: tibble or R6 ArrowObject

    • overlapping_samples_df1, overlapping_samples_df2, df1_project_nr, df2_project_nr and reference_project: Character vector

  • Also checks the validity of names of project and reference project.

  • Error if invalid input classes are detected.

• olink_norm_input_check_df_cols:

  • Detects the column names of input datasets df1 and df2 to allow for alternative names.

  • Returns named list of column names to use downstream.

  • Warning if Normalization column missing from all datasets.

  • Warning if LOD is missing or if there are multiple LOD columns.

  • Error if required columns are missing.

  • Error if not all input datasets have or lack Normalization column.

  • Error if input datasets have been quantified with different methods.

• olink_norm_input_ref_medians:

  • Checks validity of dataset containing reference_medians.

  • Error if required columns are missing based on olink_norm_ref_median_cols.

  • Error if columns are not of the correct class bases on olink_norm_ref_median_cols.

  • Error if there duplicate assay identifiers.

• olink_norm_input_check_samples:

  • Check character vectors of reference sample identifiers for:

    • Being present in df1 and/or df2.

    • Duplicate identifiers.

• olink_norm_input_clean_assays:

  • Returns a named list with the updated df1, df2 and/or reference_medians.

  • Removes assays that are not of the format OID followed by 5 digits.

  • Removes assays that are marked with Normalization = EXCLUDED.

• olink_norm_input_assay_overlap:

  • Returns a named list with the updated df1, df2 and/or reference_medians.

  • Remove assays not shared between df1 and df2, or between df1 and reference_medians.

• olink_norm_input_norm_method:

  • Check if all assays in df1 and df2 have been originally normalized with the same method "Intensity" or "Plate control".

  • Warning is thrown if not.

Value

Named list of updated inputs to use for normalization:

• df1: dataset df1.

• df2: NULL if reference median normalization, or dataset df2.

• overlapping_samples_df1: character vector of reference samples from df1.

• overlapping_samples_df2: NULL if reference median normalization, or character vector of reference samples from df1.

• df1_project_nr: name of df1 project.

• df2_project_nr: NULL if reference median normalization, or name of df2 project.

• reference_project: NULL if reference median normalization, or name of reference project.

• reference_medians: NULL if bridge or subset normalization, or dataset with reference_medians.

• df1_cols: column names of df1 to use downstream.

• df2_cols: NULL if reference median normalization, or column names of df2 to use downstream.

• norm_mode: one of bridge, subset, ref_median, and norm_ht_3k indicating the normalization to be performed.

Author(s)

Klev Diamanti

Description

This function takes as input a named list of datasets and checks if their columns allow the normalization to be performed. The input may contain "tibble", "ArrowTable" or a mixture of them.

Usage

olink_norm_input_check_df_cols(lst_df)

Arguments

Value

Named list of vectors with the required column names for each dataset in lst_df if no error.

Author(s)

Klev Diamanti

Examples

# One dataset
OlinkAnalyze:::olink_norm_input_check_df_cols(
  lst_df = list(
    "p1" = npx_data1
  ) |>
    lapply(function(l_df) {
      l_df |>
        dplyr::select(
          -dplyr::any_of(c("Normalization"))
        )
     })
  )

# Two datasets
OlinkAnalyze:::olink_norm_input_check_df_cols(
  lst_df = list(
    "p1" = npx_data1,
    "p2" = npx_data2
  ) |>
    lapply(function(l_df) {
      l_df |>
        dplyr::select(
          -dplyr::any_of(c("Normalization"))
        )
     })
  )

# Multiple datasets
OlinkAnalyze:::olink_norm_input_check_df_cols(
  lst_df = list(
    "p1" = npx_data1,
    "p2" = npx_data2,
    "p3" = npx_data1,
    "p4" = npx_data2
  ) |>
    lapply(function(l_df) {
      l_df |>
        dplyr::select(
          -dplyr::any_of(c("Normalization"))
        )
     })
  )

Description

This function takes as input a two named lists of character vectors with matching names and checks the validity of the reference samples. In case of 1 set of df samples, then all checks are skipped as reference median normalization is to be performed.

Usage

olink_norm_input_check_samples(lst_df_samples, lst_ref_samples, norm_mode)

Arguments

Value

NULL if no warning or error.

Author(s)

Klev Diamanti

Examples

# Reference median normalization
OlinkAnalyze:::olink_norm_input_check_samples(
  lst_df_samples = list(
    "p1" = unique(npx_data1$SampleID)
  ),
  lst_ref_samples = list(
    "p1" = npx_data1 |>
      dplyr::filter(
        !grepl(pattern = "CONTROL_SAMPLE",
        x = .data[["SampleID"]],
        fixed = TRUE)
      ) |>
      dplyr::pull(.data[["SampleID"]]) |>
      unique() |>
      sort() |>
      head(n = 6L)
  ),
  norm_mode = "ref_median"
)

# Bridge normalization
ref_samples_bridge <- intersect(x = npx_data1$SampleID,
                                y = npx_data2$SampleID) |>
  (\(x) x[!grepl(pattern = "CONTROL_SAMPLE", x = x, fixed = TRUE)])()

OlinkAnalyze:::olink_norm_input_check_samples(
  lst_df_samples = list(
    "p1" = unique(npx_data1$SampleID),
    "p2" = unique(npx_data2$SampleID)
  ),
  lst_ref_samples = list(
    "p1" = ref_samples_bridge,
    "p2" = ref_samples_bridge
  ),
  norm_mode = "bridge"
)

# Subset normalization
ref_samples_subset_1 <- npx_data1 |>
  dplyr::filter(
    !grepl(pattern = "CONTROL_SAMPLE",
           x = .data[["SampleID"]],
           fixed = TRUE)
    & .data[["QC_Warning"]] == "Pass"
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()
ref_samples_subset_2 <- npx_data2 |>
  dplyr::filter(
    !grepl(pattern = "CONTROL_SAMPLE",
           x = .data[["SampleID"]],
           fixed = TRUE)
    & .data[["QC_Warning"]] == "Pass"
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()

OlinkAnalyze:::olink_norm_input_check_samples(
  lst_df_samples = list(
    "p1" = unique(npx_data1$SampleID),
    "p2" = unique(npx_data2$SampleID)
  ),
  lst_ref_samples = list(
    "p1" = ref_samples_subset_1,
    "p2" = ref_samples_subset_2
  ),
  norm_mode = "subset"
)

Description

Check if df1, df2 and/or reference_medians are tibble or ArrowDataset datasets; if overlapping_samples_df1 and/or overlapping_samples_df2 are character vectors; and if df1_project_nr, df2_project_nr and/or reference_project are scalar character vectors.

Usage

olink_norm_input_class(
  df1,
  df2,
  overlapping_samples_df1,
  overlapping_samples_df2,
  df1_project_nr,
  df2_project_nr,
  reference_project,
  reference_medians,
  norm_mode
)

Arguments

Value

NULL unless there is an error

Author(s)

Klev Diamanti

Description

Check datasets and reference_medians for unexpected Olink identifiers or excluded assays

Usage

olink_norm_input_clean_assays(lst_df, reference_medians, lst_cols, norm_mode)

Arguments

Value

A named list containing lst_df and reference_medians stripped from unexpected Olink identifiers or excluded assays

Author(s)

Klev Diamanti

Description

A function to check whether we are to perform simple bridge normalization, or cross-platform (Olink Explore 3072 - Olink Explore HT) normalization.

The function uses the internal dataset eHT_e3072_mapping to determine the product source of each dataset. If both datasets originate from the same Olink product, then it will return bridge. If the datasets to be normalized originate from Olink Explore HT and Olink Explore 3072, it will return norm_ht_3k. In any other case an error is thrown.

Usage

olink_norm_input_cross_product(lst_df, lst_cols, reference_project)

Arguments

Value

Character string indicating the type of normalization to be performed. One of bridge, subset, ref_median or norm_ht_3k. # nolint And the updated list of datasets in case of cross-platform normalization.

Author(s)

Klev Diamanti

Description

Check datasets and reference_medians for Olink identifiers not shared across datasets.

Usage

olink_norm_input_norm_method(lst_df, lst_cols)

Arguments

Value

NULL if all assays are normalized with the same approach.

Author(s)

Klev Diamanti; Kathleen Nevola

Description

Check datasets of reference_medians

Usage

olink_norm_input_ref_medians(reference_medians)

Arguments

Value

NULL otherwise error.

Author(s)

Klev Diamanti

Description

This function takes as input some of the inputs of the Olink normalization function and checks the validity of the input.

Usage

olink_norm_input_validate(
  df1,
  df2,
  overlapping_samples_df1,
  overlapping_samples_df2,
  reference_medians
)

Arguments

Details

Depending on the input the function will return:

• Error: if the required components are lacking from the input or if the normalization cannot be performed.

• Warning: if the normalization can be determined but extra inputs are provided. This will be followed by a message and the type of normalization to be performed.

• Message: Information about the type of normalization to be performed.

Note that input are passed directly from the main olink_normalization function.

Value

Scalar character from olink_norm_modes if normalization can be determined from the input, otherwise see details.

Author(s)

Klev Diamanti

Description

Normalizes two Olink datasets to each other, or one Olink dataset to a reference set of medians values.

Usage

olink_normalization(
  df1,
  df2 = NULL,
  overlapping_samples_df1,
  overlapping_samples_df2 = NULL,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1",
  reference_medians = NULL
)

Arguments

Details

The function handles three different types of normalization:

• Bridge normalization: One of the datasets is adjusted to another using overlapping samples (bridge samples). Overlapping samples need to have the same identifiers in both datasets. Normalization is performed using the median of the pair-wise differences between the bridge samples in the two datasets. The two datasets are provided as df1 and df2, and the one being adjusted to is specified in the input reference_project; overlapping samples are specified in overlapping_samples_df1. Only overlapping_samples_df1 should be provided regardless of the dataset used as reference_project.

• Subset normalization: One of the datasets is adjusted to another using a subset of samples from each. Normalization is performed using the differences of the medians between the subsets from the two datasets. Both overlapping_samples_df1 and overlapping_samples_df2 need to be provided, and sample identifiers do not need to be the same.

  • A special case of subset normalization occurs when all samples (except control samples and samples with QC warnings) from each dataset are used for normalization; this special case is called intensity normalization. In intensity normalization all unique sample identifiers from df1 are provided as input in overlapping_samples_df1 and all unique sample identifiers from df2 are provided as input in overlapping_samples_df2.

• Reference median normalization: One of the datasets (df1) is adjusted to a predefined set of adjustment factors. This is effectively subset normalization, but using differences of medians to pre-recorded median values. df1, overlapping_samples_df1, df1_project_nr and reference_medians need to be specified. Dataset df1 is normalized using the differences in median between the overlapping samples and the reference medians.

• Cross-product normalization: One of the datasets is adjusted to another using the median of pair-wise differences of overlapping samples (bridge samples) or quantile smoothing using overlapping samples as reference to adjust the distributions. Overlapping samples need to have the same identifiers in both datasets. The two datasets are provided as df1 and df2, and the one being adjusted to is specified in the input reference_project; Note that in cross-product normalization the reference project is predefined, and in case the argument reference_project does not match the expected reference project an error will be returned. Overlapping samples are specified in overlapping_samples_df1. Only overlapping_samples_df1 should be provided regardless of the dataset used as reference_project. This functionality does not modify the column with original quantification values (e.g. NPX), instead it normalizes it with 2 different approaches in columns "MedianCenteredNPX" and "QSNormalizedNPX", and provides a recommendation in "BridgingRecommendation" about which of the two columns is to be used.

The output dataset is df1 if reference median normalization, or df2 appended to df1 if bridge, subset or cross-product normalization. The output dataset contains all original columns from the original dataset(s), and the columns:

• "Project" and "Adj_factor" in case of reference median, bridge and subset normalization. The former marks the project of origin based on df1_project_nr and df2_project_nr, and the latter the adjustment factor that was applied to the non-reference dataset.

• "Project", "OlinkID_E3072", "MedianCenteredNPX", "QSNormalizedNPX", "BridgingRecommendation" in case of cross-product normalization. The columns correspond to the project of origin based on df1_project_nr and df2_project_nr, the assay identifier in the non-reference project, the bridge-normalized quantification value, the quantile smoothing-normalized quantification value, and the recommendation about which of the two normalized values is more suitable for downstream analysis.

Value

Tibble or ArrowObject with the normalized dataset.

Examples

# prepare datasets
npx_df1 <- npx_data1 |>
  dplyr::mutate(
    Normalization = "Intensity"
  )
npx_df2 <- npx_data2 |>
  dplyr::mutate(
    Normalization = "Intensity"
  )

# bridge normalization

# overlapping samples - exclude control samples
overlap_samples <- intersect(x = npx_df1$SampleID,
                             y = npx_df2$SampleID) |>
  (\(x) x[!grepl("^CONTROL_SAMPLE", x)])()

# normalize
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = overlap_samples,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# subset normalization

# find a suitable subset of samples from each dataset:
# exclude control samples
# exclude samples that do not pass QC
df1_samples <- npx_df1 |>
  dplyr::group_by(
    dplyr::pick(
      dplyr::all_of("SampleID")
    )
  )|>
  dplyr::filter(
    all(.data[["QC_Warning"]] == 'Pass')
  ) |>
  dplyr::ungroup() |>
  dplyr::filter(
    !grepl(pattern = "^CONTROL_SAMPLE", x = .data[["SampleID"]])
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()
df2_samples <- npx_df2 |>
  dplyr::group_by(
    dplyr::pick(
      dplyr::all_of("SampleID")
    )
  )|>
  dplyr::filter(
    all(.data[["QC_Warning"]] == 'Pass')
  ) |>
  dplyr::ungroup() |>
  dplyr::filter(
    !grepl(pattern = "^CONTROL_SAMPLE", x = .data[["SampleID"]])
  ) |>
  dplyr::pull(
    .data[["SampleID"]]
  ) |>
  unique()

# select a subset of samples from each set from above
df1_subset <- sample(x = df1_samples, size = 16L)
df2_subset <- sample(x = df2_samples, size = 20L)

# normalize
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = df1_subset,
  overlapping_samples_df2 = df2_subset,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# special case of subset normalization using all samples
olink_normalization(
  df1 = npx_df1,
  df2 = npx_df2,
  overlapping_samples_df1 = df1_samples,
  overlapping_samples_df2 = df2_samples,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1"
)

# reference median normalization

# For the sake of this example, set the reference median to 1
ref_med_df <- npx_data1 |>
  dplyr::select(
    dplyr::all_of(
      c("OlinkID")
    )
  ) |>
  dplyr::distinct() |>
  dplyr::mutate(
    Reference_NPX = runif(n = dplyr::n(),
                          min = -1,
                          max = 1)
  )

# normalize
olink_normalization(
  df1 = npx_df1,
  overlapping_samples_df1 = df1_subset,
  reference_medians = ref_med_df
)

# cross-product normalization

# get reference samples
overlap_samples_product <- intersect(
  x = unique(OlinkAnalyze:::data_ht_small$SampleID),
  y = unique(OlinkAnalyze:::data_3k_small$SampleID)
) |>
  (\(.) .[!grepl("CONTROL", .)])()

# normalize
olink_normalization(
  df1 = OlinkAnalyze:::data_ht_small,
  df2 = OlinkAnalyze:::data_3k_small,
  overlapping_samples_df1 = overlap_samples_product,
  df1_project_nr = "proj_ht",
  df2_project_nr = "proj_3k",
  reference_project = "proj_ht"
)

Description

Normalizes two NPX projects (data frames) using shared samples.

Usage

olink_normalization_bridge(
  project_1_df,
  project_2_df,
  bridge_samples,
  project_1_name = "P1",
  project_2_name = "P2",
  project_ref_name = "P1"
)

Arguments

Details

This function is a wrapper of olink_normalization.

In bridging normalization one of the projects is adjusted to another using shared samples (bridge samples). It is not necessary for the shared samples to be named the same in each project. Adjustment between the two projects is made using the median of the paired differences between the shared samples. The two data frames are inputs project_1_df and project_2_df, the one being adjusted to is specified in the input project_ref_name and the shared samples are specified in bridge_samples.

Value

A "tibble" of NPX data in long format containing normalized NPX values, including adjustment factors and name of project.

Examples

npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find overlapping samples, but exclude Olink control
overlap_samples <- dplyr::intersect(unique(npx_df1$SampleID),
                                    unique(npx_df2$SampleID))
overlap_samples_list <- list("DF1" = overlap_samples,
                             "DF2" = overlap_samples)

# Normalize
olink_normalization_bridge(project_1_df = npx_df1,
                           project_2_df = npx_df2,
                           bridge_samples = overlap_samples_list,
                           project_1_name = "P1",
                           project_2_name = "P2",
                           project_ref_name = "P1")

Description

The function uses a dataset from Olink Explore 3072 and a dataset from Olink Explore HT, and examines if the matched assays between the two products can be normalized to each other. The input datasets should be exported from Olink software and should not be altered prior to importing them to this function.

Usage

olink_normalization_bridgeable(lst_df, ref_cols, seed = 1)

Arguments

Details

All processes below assume that the first element from lst_df is the reference dataset (e.g. Olink Explore HT), and the other element of the list is the non-reference dataset (e.g. Olink Explore 3072). The input datasets have to be pre-processed by olink_norm_input_check which will take care of mapping of assay identifiers and various checks. Also, the input datasets should exclusively contain datapoints from bridge samples. When this function is called from the function olink_normalization, then the list is created seamlessly in the background, and the datasets have been already processed by olink_norm_input_check.

The input ref_cols is a named list masking column names of the reference dataset. This list is generated automatically from olink_norm_input_check when it is called from olink_normalization. In addition, olink_normalization has also utilized norm_internal_rename_cols to rename the columns of the non-reference dataset according to the ones of the reference dataset, hence all column names should match.

Value

A "tibble" in long format with the following columns:

• OlinkID: Underscore-separated Olink identifiers of matching assays between Olink Explore HT and Olink Explore 3072.

• BridgingRecommendation: A character vector indicating whether the matching assays are considered as bridgeable or not, and the recommended type of normalization to perform.

Author(s)

Amrita Kar Marianne Sandin Danai G. Topouza Klev Diamanti

Examples

# check input datasets
data_explore_check <- OlinkAnalyze:::olink_norm_input_check(
  df1 = OlinkAnalyze:::data_3k_small,
  df2 = OlinkAnalyze:::data_ht_small,
  overlapping_samples_df1 = intersect(
    x = unique(OlinkAnalyze:::data_3k_small$SampleID),
    y = unique(OlinkAnalyze:::data_ht_small$SampleID)
  ) |>
    (\(x) x[!grepl("CONTROL", x)])() |>
    head(20L),
  overlapping_samples_df2 = NULL,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P2",
  reference_medians = NULL
)

# create lst_df
lst_df <- list(
  data_explore_check$ref_df,
  data_explore_check$not_ref_df
)
names(lst_df) <- c(data_explore_check$ref_name,
                   data_explore_check$not_ref_name)

# create ref_cols
ref_cols <- data_explore_check$ref_cols

# run olink_normalization_bridgeable
is_bridgeable_result <- OlinkAnalyze:::olink_normalization_bridgeable(
  lst_df = lst_df,
  ref_cols = ref_cols,
  seed = 1
)

Description

This function normalizes pairs of NPX projects (data frames) using shared samples or subsets of samples.

Usage

olink_normalization_n(norm_schema)

Arguments

Details

This function is a wrapper of olink_normalization_bridge and olink_normalization_subset.

The input of this function is a tibble that contains all the necessary information to normalize multiple NPX projects. This tibble is called the normalization schema. The basic idea is that every row of the data frame is a separate project to be normalized. We assume that there is always one baseline project that does not normalize to any other. All other project normalize to one or more projects. The function handles projects that are normalized in a chain, for example:

• 1. project 2 normalizes to project 1, and project 3 normalizes to project 2.

• 2. project 2 normalizes to project 1, and project 3 normalizes to the combined data frame of projects 1 and 2 (that is already normalized).

The function can also handle a mixed schema of bridge and subset normalization.

Specifications of the normalization schema data frame:

• order: should strictly be a numeric or integer array with unique identifiers for each project. It is necessary that this array starts from 1 and that it contains no NAs.

• name: should strictly be a character array with unique identifiers for each project. Each entry should represent the name of the project located in the same row. No NAs are allowed.

• data: a named list of NPX data frames representing the projects to be normalized. Names of the items of the list should be identical to "names". No NAs are allowed.

• samples: a two-level nested named list of sample identifiers from each NPX project from "data". Names of the first level of the nested list should be identical to "names" and to the names of the list from "data". Projects that will be used only as reference should have their corresponding element in the list as NA, while all other projects should contain a named list of 2 arrays containing identifiers of samples to be used for the calculation of adjustment factor. The names of the two arrays should be DF1 and DF2 corresponding to the reference project and the project in the current row, respectively. For bridge normalization arrays should be of equal length and the index of each entry should correspond to the same sample. For subset normalization arrays do not need to be of equal length and the order the samples appear in does not matter. DF1 might contain sample identifiers from more than one project as long as the project in the current row is to be normalized to multiple other projects.

• normalization_type: a character array containing the flags "Bridge" or "Subset". Projects that will be used only as reference should have their corresponding element in the array as NA, while all other projects should contain a flag. For the time being the flag "Median" is not supported.

• normalize_to: a character array pointing to the project this project is to be normalized to. Elements of the array should be exclusively from the "order" column. Elements of the array may be comma-separated if the project is to be normalized to multiple projects.

Value

A "tibble" of NPX data in long format containing normalized NPX values, including adjustment factors and name of project.

Examples

#### Bridge normalization of two projects

# prepare datasets
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find overlapping samples, but exclude Olink control
overlap_samples <- dplyr::intersect(unique(npx_df1$SampleID),
                                    unique(npx_df2$SampleID))
overlap_samples_list <- list("DF1" = overlap_samples,
                             "DF2" = overlap_samples)

# create tibble for input
norm_schema_bridge <- dplyr::tibble(
  order              = c(1, 2),
  name               = c("NPX_DF1", "NPX_DF2"),
  data               = list("NPX_DF1" = npx_df1,
                            "NPX_DF2" = npx_df2),
  samples            = list("NPX_DF1" = NA_character_,
                            "NPX_DF2" = overlap_samples_list),
  normalization_type = c(NA_character_, "Bridge"),
  normalize_to       = c(NA_character_, "1")
)

# normalize
olink_normalization_n(norm_schema = norm_schema_bridge)

#### Subset normalization of two projects

# datasets
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find a suitable subset of samples from both projects, but exclude Olink
# controls and samples that fail QC.
df1_samples <- npx_df1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique() |>
  sample(size = 16, replace = FALSE)
df2_samples <- npx_df2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique() |>
  sample(size = 16, replace = FALSE)

# create named list
subset_samples_list <- list("DF1" = df1_samples,
                            "DF2" = df2_samples)

# create tibble for input
norm_schema_subset <- dplyr::tibble(
  order              = c(1, 2),
  name               = c("NPX_DF1", "NPX_DF2"),
  data               = list("NPX_DF1" = npx_df1,
                            "NPX_DF2" = npx_df2),
  samples            = list("NPX_DF1" = NA_character_,
                            "NPX_DF2" = subset_samples_list),
  normalization_type = c(NA_character_, "Subset"),
  normalize_to       = c(NA_character_, "1")
)

# Normalize
olink_normalization_n(norm_schema = norm_schema_subset)

#### Subset normalization  of two projects using all samples

# datasets
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find a suitable subset of samples from both projects, but exclude Olink
# controls and samples that fail QC.
df1_samples_all <- npx_df1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique()
df2_samples_all <- npx_df2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique()

# create named list
subset_samples_all_list <- list("DF1" = df1_samples_all,
                                "DF2" = df2_samples_all)

# create tibble for input
norm_schema_subset_all <- dplyr::tibble(
  order              = c(1, 2),
  name               = c("NPX_DF1", "NPX_DF2"),
  data               = list("NPX_DF1" = npx_df1,
                            "NPX_DF2" = npx_df2),
  samples            = list("NPX_DF1" = NA_character_,
                            "NPX_DF2" = subset_samples_all_list),
 normalization_type = c(NA_character_, "Subset"),
 normalize_to       = c(NA_character_, "1")
)

# Normalize
olink_normalization_n(norm_schema = norm_schema_subset_all)

#### Multi-project normalization using bridge and subset samples

## NPX data frames to bridge
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# manipulating the sample NPX datasets to create another two random ones
npx_df3 <- npx_data2 |>
  dplyr::mutate(SampleID = paste(SampleID, "_mod", sep = ""),
                PlateID = paste(PlateID, "_mod", sep = ""),
                NPX = sample(x = NPX, size = dplyr::n(), replace = FALSE)) |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

npx_df4 <- npx_data1 |>
  dplyr::mutate(SampleID = paste(SampleID, "_mod2", sep = ""),
                PlateID = paste(PlateID, "_mod2", sep = ""),
                NPX = sample(x = NPX, size = dplyr::n(), replace = FALSE)) |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

## samples to use for normalization
# Bridge samples with same identifiers between npx_df1 and npx_df2
overlap_samples <- dplyr::intersect(unique(npx_df1$SampleID),
                                    unique(npx_df2$SampleID))
overlap_samples_df1_df2 <- list("DF1" = overlap_samples,
                                "DF2" = overlap_samples)
rm(overlap_samples)

# Bridge samples with different identifiers between npx_df2 and npx_df3
overlap_samples_df2_df3 <- list("DF1" = sample(x = unique(npx_df2$SampleID),
                                               size = 10,
                                               replace = FALSE),
                                "DF2" = sample(x = unique(npx_df3$SampleID),
                                               size = 10,
                                               replace = FALSE))

# Samples to use for intensity normalization between npx_df4 and the
# normalized dataset of npx_df1 and npx_df2
overlap_samples_df12_df4 <- list("DF1" = sample(x = c(unique(npx_df1$SampleID),
                                                      unique(npx_df2$SampleID)),
                                                size = 100,
                                                replace = FALSE) |>
                                   unique(),
                                 "DF2" = sample(x = unique(npx_df4$SampleID),
                                                size = 40,
                                                replace = FALSE))

# create tibble for input
norm_schema_n <- dplyr::tibble(
  order              = c(1, 2, 3, 4),
  name               = c("NPX_DF1", "NPX_DF2", "NPX_DF3", "NPX_DF4"),
  data               = list("NPX_DF1" = npx_df1,
                            "NPX_DF2" = npx_df2,
                            "NPX_DF3" = npx_df3,
                            "NPX_DF4" = npx_df4),
  samples            = list("NPX_DF1" = NA_character_,
                            "NPX_DF2" = overlap_samples_df1_df2,
                            "NPX_DF3" = overlap_samples_df2_df3,
                            "NPX_DF4" = overlap_samples_df12_df4),
  normalization_type = c(NA_character_, "Bridge", "Bridge", "Subset"),
  normalize_to       = c(NA_character_, "1", "2", "1,2")
)

olink_normalization_n(norm_schema = norm_schema_n)

Description

An internal function to perform checks on the input of the function olink_normalization_n.

Usage

olink_normalization_n_check(norm_schema)

Arguments

Value

a character message. If the message is "TRUE" then all checks passed, otherwise an error message will be printed.

Description

An internal function to perform checks on the input project names in the functions olink_normalization_bridge and olink_normalization_subset. The function is expected to run all checks on project names to make sure that normalization can be performed smoothly. It should work independently of the function calling it.

Usage

olink_normalization_project_name_check(
  project_1_name,
  project_2_name,
  project_ref_name
)

Arguments

Value

a character message. If the message is "TRUE" then all checks passed, otherwise an error message will be printed.

Description

This function uses bridge samples to map quantiles of the non-reference dataset to the ones of the reference dataset. Mapped quantiles are used to transform the quantifications of the the non-reference dataset to the reference.

Usage

olink_normalization_qs(lst_df, ref_cols, bridge_samples)

Arguments

Details

In the case when a study is separated into multiple projects, an additional normalization step is needed to allow the data to be comparable across projects. Across different Olink products, some of the assays exist in corresponding but distinct NPX spaces. For those assays, the median of paired differences is insufficient for bridging as it only considers one anchor point (the median/50% quantile). Instead, quantile smoothing (QS) using multiple anchor points (5%, 10%, 25%, 50%, 75%, 90% and 95% quantiles) is favored to map the Explore 3072 data to the Explore HT distribution. The olink_normalization_qs() performs quantile smoothing bridging normalization between datasets from two Olink products (for example Olink Explore 3072 and Olink Explore HT) by performing the following steps:

• An empirical cumulative distribution function is used to map datapoints for the bridging samples from one product to the equivalent space in the other product.

• A spline regression model is constructed using unmapped and mapped data from one product, using anchor points from the quantiles defined above.

• The spline regression model is used to predict the normalized NPX values for all datapoints

More information on quantile smoothing and between product normalization can be found in the Bridging Olink Explore 3072 to Olink Explore HT tutorial.

Value

A "tibble" of Olink data in long format containing both input datasets with the quantile normalized quantifications.

Author(s)

Amrita Kar Marianne Sandin Masoumeh Sheikhi Klev Diamanti

Examples

# Bridge samples
bridge_samples <- intersect(
  x = unique(OlinkAnalyze:::data_ht_small$SampleID),
  y = unique(OlinkAnalyze:::data_3k_small$SampleID)
) |>
  (\(x) x[!grepl("CONTROL", x)])()

# Run the internal function olink_norm_input_check
check_norm <- OlinkAnalyze:::olink_norm_input_check(
  df1 = OlinkAnalyze:::data_ht_small,
  df2 = OlinkAnalyze:::data_3k_small,
  overlapping_samples_df1 = bridge_samples,
  overlapping_samples_df2 = NULL,
  df1_project_nr = "P1",
  df2_project_nr = "P2",
  reference_project = "P1",
  reference_medians = NULL
)

# Named list of input datasets
lst_df <- list(
  check_norm$ref_df,
  check_norm$not_ref_df
)
names(lst_df) <- c(check_norm$ref_name, check_norm$not_ref_name)

ref_cols <- check_norm$ref_cols

qs_result <- OlinkAnalyze:::olink_normalization_qs(
 lst_df = lst_df,
 ref_cols = ref_cols,
 bridge_samples = bridge_samples
)

Description

An internal function to perform checks on the input samples in the functions olink_normalization_bridge and olink_normalization_subset. The function is expected to run all checks on SampleID to make sure that normalization can be performed smoothly. It should work independently of the function calling it.

Usage

olink_normalization_sample_check(
  list_samples,
  check_mode,
  project_1_all_samples,
  project_2_all_samples
)

Arguments

Value

a character message. If the message is "TRUE" then all checks passed, otherwise an error message will be printed.

Description

Normalizes two NPX projects (data frames) using all or a subset of samples.

Usage

olink_normalization_subset(
  project_1_df,
  project_2_df,
  reference_samples,
  project_1_name = "P1",
  project_2_name = "P2",
  project_ref_name = "P1"
)

Arguments

Details

This function is a wrapper of olink_normalization.

In subset normalization one of the projects is adjusted to another using a subset of all samples from each. Please note that the subsets of samples are not expected to be replicates of each other or to have the SampleID. Adjustment between the two projects is made using the assay-specific differences in median between the subsets of samples from the two projects. The two data frames are inputs project_1_df and project_2_df, the one being adjusted to is specified in the input project_ref_name and the shared samples are specified in reference_samples.

A special case of subset normalization is to use all samples (except control samples) from each project as a subset.

Value

A "tibble" of NPX data in long format containing normalized NPX values, including adjustment factors and name of project.

Examples

#### Subset normalization

# datasets
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find a suitable subset of samples from both projects, but exclude Olink
# controls and samples that fail QC.
df1_samples <- npx_df1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique() |>
  sample(size = 16, replace = FALSE)
df2_samples <- npx_df2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique() |>
  sample(size = 16, replace = FALSE)

# create named list
subset_samples_list <- list("DF1" = df1_samples,
                            "DF2" = df2_samples)

# Normalize
olink_normalization_subset(project_1_df = npx_df1,
                           project_2_df = npx_df2,
                           reference_samples = subset_samples_list,
                           project_1_name = "P1",
                           project_2_name = "P2",
                           project_ref_name = "P1")


#### Special case of subset normalization using all samples

# datasets
npx_df1 <- npx_data1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")
npx_df2 <- npx_data2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::select(-Project) |>
  dplyr::mutate(Normalization = "Intensity")

# Find a suitable subset of samples from both projects, but exclude Olink
# controls and samples that fail QC.
df1_samples_all <- npx_df1 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique()
df2_samples_all <- npx_df2 |>
  dplyr::filter(!stringr::str_detect(SampleID, "CONTROL_")) |>
  dplyr::group_by(SampleID) |>
  dplyr::filter(all(QC_Warning == 'Pass')) |>
  dplyr::pull(SampleID) |>
  unique()

# create named list
subset_samples_all_list <- list("DF1" = df1_samples_all,
                            "DF2" = df2_samples_all)

# Normalize
olink_normalization_subset(project_1_df = npx_df1,
                           project_2_df = npx_df2,
                           reference_samples = subset_samples_all_list,
                           project_1_name = "P1",
                           project_2_name = "P2",
                           project_ref_name = "P1")

Description

Performs an Kruskal-Wallis Test for each assay (by OlinkID) in every panel using stats::kruskal.test. Performs an Friedman Test for each assay (by OlinkID) in every panel using rstatix::friedman_test. The function handles factor variable.

Samples that have no variable information or missing factor levels are automatically removed from the analysis (specified in a message if verbose = TRUE). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = T). Numerical variables are not converted to factors. If a numerical variable is to be used as a factor, this conversion needs to be done on the dataframe before the function call.

Inference is specified in a message if verbose = TRUE.
The formula notation of the final model is specified in a message if verbose = TRUE.

Adjusted p-values are calculated by stats::p.adjust according to the Benjamini & Hochberg (1995) method (“fdr”). The threshold is determined by logic evaluation of Adjusted_pval < 0.05.

Usage

olink_one_non_parametric(
  df,
  variable,
  dependence = FALSE,
  subject = NULL,
  verbose = TRUE
)

Arguments

Value

A tibble containing the Kruskal-Wallis Test or Friedman Test results for every protein.

Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• df: "numeric" degrees of freedom

• method: "character" which method was used

• statistic: "named numeric" the value of the test statistic with a name describing it

• p.value: "numeric" p-value for the test

• Adjusted_pval: "numeric" adjusted p-value for the test (Benjamini&Hochberg)

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)

# One-way Kruskal-Wallis Test
try({ # May fail if dependencies are not installed
kruskal_results <- olink_one_non_parametric(df = npx_data1,
                                            variable = "Site")
})

#Friedman Test
friedman_results <- olink_one_non_parametric(df = npx_data1,
                                             variable = "Time",
                                             subject = "Subject",
                                             dependence = TRUE)

Description

Performs a posthoc test using rstatix::wilcox_test or FSA::dunnTest with Benjamini-Hochberg p-value adjustment per assay (by OlinkID) for each panel at confidence level 0.95. See olink_one_non_parametric for details of input notation.

The function handles both factor and numerical variables.

Usage

olink_one_non_parametric_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  test = "kruskal",
  verbose = TRUE
)

Arguments

Value

Tibble of posthoc tests for specified effect, arranged by ascending adjusted p-values.

Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• contrast: "character" the groups that were compared

• estimate: "numeric" the value of the test statistic with a name describing it

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)

try({ # May fail if dependencies are not installed
# One-way Kruskal-Wallis Test
kruskal_results <- olink_one_non_parametric(df = npx_data1,
                                            variable = "Site")
})

#Friedman Test
friedman_results <- olink_one_non_parametric(df = npx_data1,
                                            variable = "Time",
                                            subject = "Subject",
                                            dependence = TRUE)

#Posthoc test for the results from Friedman Test
friedman_posthoc_results <- olink_one_non_parametric_posthoc(npx_data1,
                                                            variable = "Time",
                                                            test = "friedman",
                                                            olinkid_list = {friedman_results %>%
                                                              filter(Threshold == 'Significant') %>%
                                                              dplyr::select(OlinkID) %>%
                                                              distinct() %>%
                                                              pull()})

Description

Performs an ANOVA F-test for each assay (by OlinkID) in every panel using stats::Anova and Type III sum of squares. Dependent variable will be treated as ordered factor. The function handles only factor and/or covariates.

Samples that have no variable information or missing factor levels are automatically removed from the analysis (specified in a message if verbose = T). Character columns in the input dataframe are automatically converted to factors (specified in a message if verbose = T). Crossed 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')

Inference is specified in a message if verbose = T.
The formula notation of the final model is specified in a message if verbose = T.

Adjusted p-values are calculated by stats::p.adjust according to 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.

Usage

olink_ordinalRegression(
  df,
  variable,
  covariates = NULL,
  return.covariates = F,
  verbose = T
)

Arguments

Value

A tibble containing the ANOVA results for every protein. The tibble is arranged by ascending p-values.

Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• statistic: "numeric" value of the statistic

• p.value: "numeric" nominal p-value

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)

try({ # May fail if dependencies are not installed.
#Two-way Ordinal Regression with CLM.
#Results in model NPX~Treatment+Time+Treatment:Time.
   ordinalRegression_results <- olink_ordinalRegression(df = npx_data1,
                                                       variable="Treatment:Time")
})

Description

Performs a post hoc ANOVA test using emmeans::emmeans with Tukey p-value adjustment per assay (by OlinkID) for each panel at confidence level 0.95. See olink_anova for details of input notation.

The function handles both factor and numerical variables and/or covariates. The posthoc test for a numerical variable compares the difference in means of the ordinal outcome variable (default: NPX) for 1 standard deviation difference in the numerical variable, e.g. mean ordinal NPX at mean(numerical variable) versus mean NPX at mean(numerical variable) + 1*SD(numerical variable).

Usage

olink_ordinalRegression_posthoc(
  df,
  olinkid_list = NULL,
  variable,
  covariates = NULL,
  effect,
  effect_formula,
  mean_return = FALSE,
  post_hoc_padjust_method = "tukey",
  verbose = T
)

Arguments

Value

Tibble of posthoc tests for specified effect, arranged by ascending adjusted p-values.

#' Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• term: "character" term in model

• contrast: "character" the groups that were compared

• estimate: "numeric" difference in mean of the ordinal NPX between groups

• Adjusted_pval: "numeric" adjusted p-value for the test

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)

Examples

library(dplyr)
#Two-way Ordinal Regression.
#Results in model NPX~Treatment*Time.
try({ # May not work if dependencies are not installed.
ordinalRegression_results <- olink_ordinalRegression(df = npx_data1,
                              variable="Treatment:Time")

#Filtering out significant and relevant results.
significant_assays <- ordinalRegression_results %>%
  filter(Threshold == 'Significant' & term == 'Time') %>%
  select(OlinkID) %>%
  distinct() %>%
  pull()

#Posthoc test for the model NPX~Treatment*Time,
#on the effect Time.

#Posthoc
ordinalRegression_results_posthoc_results <- olink_ordinalRegression_posthoc(npx_data1,
                                                   variable=c("Treatment:Time"),
                                                   olinkid_list = significant_assays,
                                                   effect = "Time")
                                                   })

Description

Olink color panel for plotting

Usage

olink_pal(alpha = 1, coloroption = NULL)

Arguments

Value

A character vector of palette hex codes for colors

Examples

library(scales)

#Color matrices
show_col(olink_pal()(10), labels = FALSE)
show_col(olink_pal(coloroption = c('lightblue', 'green'))(2), labels = FALSE)

#Contour plot
filled.contour(volcano, color.palette = olink_pal(), asp = 1)
filled.contour(volcano, color.palette = hue_pal(), asp = 1)

Description

This function performs enrichment analysis based on statistical test results and full data using clusterProfiler's gsea and enrich functions for MSigDB.

Usage

olink_pathway_enrichment(
  data,
  test_results,
  method = "GSEA",
  ontology = "MSigDb",
  organism = "human",
  pvalue_cutoff = 0.05,
  estimate_cutoff = 0
)

Arguments

Details

MSigDB is subset if the ontology argument 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.

clusterProfiler is originally developed by Guangchuang Yu at the School of Basic Medical Sciences at Southern Medical University.

T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141. doi: 10.1016/j.xinn.2021.100141

NB: We strongly recommend to set a seed prior to running this function to ensure reproducibility of the results.

A few notes on Pathway Enrichment with Olink Data

It is important to note that sometimes the proteins that are assayed in Olink Panels are related to specific biological areas and therefore do not represent an unbiased overview of the proteome as a whole. Pathways can only interpreted based on the background/context they came from. For this reason, an estimate for all assays measured must be provided. Furthermore, certain pathways cannot come up based on Olink's coverage in this area. Additionally, if only the Inflammation panel was run, then the available pathways would be given based on a background of proteins related to inflammation. Both ORA and GSEA can provide mechanistic and disease related insight and are best to use when trying to uncover pathways/annotations of interest. It is recommended to only use pathway enrichment for hypothesis generating data, which is more well suited for data on the Explore platform or on multiple Target 96 panels. For smaller lists of proteins it may be more informative to use biological annotation in directed research, to discover which significant assay are related to keywords of interest.

Value

A data frame of enrichment results. Columns for ORA include:

• ID: "character" Pathway ID from MSigDB

• Description: "character" Description of Pathway from MSigDB

• GeneRatio: "character" ratio of input proteins that are annotated in a term

• BgRatio: "character" ratio of all genes that are annotated in this term

• pvalue: "numeric" p-value of enrichment

• p.adjust: "numeric" Adjusted p-value (Benjamini-Hochberg)

• qvalue: "numeric" false discovery rate, the estimated probability that the normalized enrichment score represents a false positive finding

• geneID: "character" list of input proteins (Gene Symbols) annotated in a term delimited by "/"

• Count: "integer" Number of input proteins that are annotated in a term

Columns for GSEA:

• ID: "character" Pathway ID from MSigDB

• Description: "character" Description of Pathway from MSigDB

• setSize: "integer" ratio of input proteins that are annotated in a term

• enrichmentScore: "numeric" Enrichment score, degree to which a gene set is over-represented at the top or bottom of the ranked list of genes

• NES: "numeric" 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: "numeric" p-value of enrichment

• p.adjust: "numeric" Adjusted p-value (Benjamini-Hochberg)

• qvalue: "numeric" false discovery rate, the estimated probability that the normalized enrichment score represents a false positive finding

• rank: "numeric" the position in the ranked list where the maximum enrichment score occurred

• leading_edge: "character" 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: "character" list of input proteins (Gene Symbols) annotated in a term delimited by "/"

See Also

• olink_pathway_heatmap for generating a heat map of results

• olink_pathway_visualization for generating a bar graph of results

Examples

library(dplyr)
npx_df <- npx_data1 %>% filter(!grepl("control", SampleID, ignore.case = TRUE))
ttest_results <- olink_ttest(
  df = npx_df,
  variable = "Treatment",
  alternative = "two.sided"
)
try({ # This expression might fail if dependencies are not installed
gsea_results <- olink_pathway_enrichment(data = npx_data1, test_results = ttest_results)
ora_results <- olink_pathway_enrichment(
  data = npx_data1,
  test_results = ttest_results, method = "ORA"
)
}, silent = TRUE)

Description

Creates a heatmap of proteins related to pathways using enrichment results from olink_pathway_enrichment.

Usage

olink_pathway_heatmap(
  enrich_results,
  test_results,
  method = "GSEA",
  keyword = NULL,
  number_of_terms = 20
)

Arguments

Value

A heatmap as a ggplot object

See Also

• olink_pathway_enrichment for generating enrichment results

• olink_pathway_visualization for generating a bar graph of results

Examples

library(dplyr)
# Run t-test results (see olink_ttest documentation)
npx_df <- npx_data1 %>% filter(!grepl('control',SampleID, ignore.case = TRUE))
ttest_results <- olink_ttest(df=npx_df,
                             variable = 'Treatment',
                            alternative = 'two.sided')

try({ # This expression might fail if dependencies are not installed
#  Run olink_pathway_enrichment (see documentation)
gsea_results <- olink_pathway_enrichment(data = npx_data1, test_results = ttest_results)
ora_results <- olink_pathway_enrichment(data = npx_data1,
                                        test_results = ttest_results, method = "ORA")
olink_pathway_heatmap(enrich_results = gsea_results, test_results = ttest_results)
olink_pathway_heatmap(enrich_results = ora_results, test_results = ttest_results,
                      method = "ORA", keyword = "cell")
})

Description

Pathways are ordered by increasing p-value (unadjusted)

Usage

olink_pathway_visualization(
  enrich_results,
  method = "GSEA",
  keyword = NULL,
  number_of_terms = 20
)

Arguments

Value

A bargraph as a ggplot object

See Also

• olink_pathway_enrichment for generating enrichment results

• olink_pathway_heatmap for generating a heat map of results

Examples

library(dplyr)
# Run olink_ttest or other stats test (see documentaiton )
npx_df <- npx_data1 %>% filter(!grepl('control',SampleID, ignore.case = TRUE))
ttest_results <- olink_ttest(df=npx_df,
                             variable = 'Treatment',
                             alternative = 'two.sided')

try({ # This expression might fail if dependencies are not installed
# Run olink_pathway_enrichment (see documentation)
gsea_results <- olink_pathway_enrichment(data = npx_data1, test_results = ttest_results)
ora_results <- olink_pathway_enrichment(data = npx_data1,
                                       test_results = ttest_results, method = "ORA")
olink_pathway_visualization(enrich_results = gsea_results)
olink_pathway_visualization(enrich_results = gsea_results, keyword = "immune")
olink_pathway_visualization(enrich_results = ora_results, method = "ORA", number_of_terms = 15)
})

Description

Generates a PCA projection of all samples from NPX data along two principal components (default PC2 vs. PC1) including the explained variance and dots colored by QC_Warning using stats::prcomp and ggplot2::ggplot.

Usage

olink_pca_plot(
  df,
  color_g = "QC_Warning",
  x_val = 1,
  y_val = 2,
  label_samples = FALSE,
  drop_assays = FALSE,
  drop_samples = FALSE,
  n_loadings = 0,
  loadings_list = NULL,
  byPanel = FALSE,
  outlierDefX = NA,
  outlierDefY = NA,
  outlierLines = FALSE,
  label_outliers = TRUE,
  quiet = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

The values are by default scaled and centered in the PCA and proteins with missing NPX values are by default removed from the corresponding assay. Unique sample names are required. Imputation by the median is done for assays with missingness <10\ The plot is printed, and a list of ggplot objects is returned.

If byPanel = TRUE, the data processing (imputation of missing values etc) and subsequent PCA is performed separately per panel. A faceted plot is printed, while the individual ggplot objects are returned.

The arguments outlierDefX and outlierDefY can be used to identify outliers in the PCA. Samples more than +/- outlierDefX and outlierDefY standard deviations from the mean of the plotted PC will be labelled. Both arguments have to be specified.

Value

A list of objects of class "ggplot", each plot contains scatter plot of PCs

Examples

library(dplyr)
npx_data <- npx_data1 %>%
    filter(!grepl('CONTROL', SampleID))

#PCA using all the data
olink_pca_plot(df=npx_data, color_g = "QC_Warning")

#PCA per panel
g <- olink_pca_plot(df=npx_data, color_g = "QC_Warning", byPanel = TRUE)
g[[2]] #Plot only the second panel

#Label outliers
olink_pca_plot(df=npx_data, color_g = "QC_Warning",
               outlierDefX = 2, outlierDefY = 4) #All data
olink_pca_plot(df=npx_data, color_g = "QC_Warning",
               outlierDefX = 2.5, outlierDefY = 4, byPanel = TRUE) #Per panel

#Retrieve the outliers
g <- olink_pca_plot(df=npx_data, color_g = "QC_Warning",
                    outlierDefX = 2.5, outlierDefY = 4, byPanel = TRUE)
outliers <- lapply(g, function(x){x$data}) %>%
    bind_rows() %>%
    filter(Outlier == 1)

Description

Generates a scheme for how to plate samples with an option to keep subjects on the same plate.

Usage

olink_plate_randomizer(
  Manifest,
  PlateSize = 96,
  Product,
  SubjectColumn,
  iterations = 500,
  available.spots,
  num_ctrl = 8,
  rand_ctrl = FALSE,
  seed
)

Arguments

Details

Variables of interest should if possible be randomized across plates to avoid confounding with potential plate effects. In the case of multiple samples per subject (e.g. in longitudinal studies), Olink recommends keeping each subject on the same plate. This can be achieved using the SubjectColumn argument.

Value

A "tibble" including SampleID, SubjectID etc. assigned to well positions. Columns include same columns as Manifest with additional columns:

• plate: Plate number

• column: Column on the plate

• row: Row on the plate

• well: Well location on the plate

See Also

• olink_displayPlateLayout() for visualizing the generated plate layouts

• olink_displayPlateDistributions() for validating that sites are properly randomized

Examples

#Generate randomization scheme using complete randomization
randomized.manifest_a <- olink_plate_randomizer(manifest, seed=12345)

#Generate randomization scheme that keeps subjects on the same plate (for longitudinal studies)
randomized.manifest_b <- olink_plate_randomizer(manifest,SubjectColumn="SubjectID",
                                                        available.spots=c(88,88), seed=12345)

#Visualize the generated plate layouts
olink_displayPlateLayout(randomized.manifest_a, fill.color = 'Site')
olink_displayPlateLayout(randomized.manifest_a, fill.color = 'SubjectID')
olink_displayPlateLayout(randomized.manifest_b, fill.color = 'Site')
olink_displayPlateLayout(randomized.manifest_b, fill.color = 'SubjectID')

#Validate that sites are properly randomized
olink_displayPlateDistributions(randomized.manifest_a, fill.color = 'Site')
olink_displayPlateDistributions(randomized.manifest_b, fill.color = 'Site')

Description

Generates a facet plot per Panel using ggplot2::ggplot and ggplot2::geom_point and stats::IQR plotting IQR vs. median for all samples. Horizontal dashed lines indicate +/-IQR_outlierDef standard deviations from the mean IQR (default 3). Vertical dashed lines indicate +/-median_outlierDef standard deviations from the mean sample median (default 3).

Usage

olink_qc_plot(
  df,
  color_g = "QC_Warning",
  plot_index = FALSE,
  label_outliers = TRUE,
  IQR_outlierDef = 3,
  median_outlierDef = 3,
  outlierLines = TRUE,
  facetNrow = NULL,
  facetNcol = NULL,
  ...
)

Arguments

Value

An object of class "ggplot". Scatterplot shows IQR vs median for all samples per panel

Examples

library(dplyr)

olink_qc_plot(npx_data1, color_g = "QC_Warning")

#Change the outlier threshold to +-4SD
olink_qc_plot(npx_data1, color_g = "QC_Warning", IQR_outlierDef = 4, median_outlierDef = 4)

#Identify the outliers
qc <- olink_qc_plot(npx_data1, color_g = "QC_Warning", IQR_outlierDef = 4, median_outlierDef = 4)
outliers <- qc$data %>% filter(Outlier == 1)

Description

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 using stats::t.test and corrects for multiple testing by the Benjamini-Hochberg method (“fdr”) using stats::p.adjust. Adjusted p-values are logically evaluated towards adjusted p-value<0.05. The resulting t-test table is arranged by ascending p-values.

Usage

olink_ttest(df, variable, pair_id, ...)

Arguments

Value

A "tibble" containing the t-test results for every protein. Columns include:

• Assay: "character" Protein symbol

• OlinkID: "character" Olink specific ID

• UniProt: "character" UniProt ID

• Panel: "character" Name of Olink Panel

• estimate: "numeric" difference in mean NPX between groups

• Group 1: "numeric" Column is named first level of variable when converted to factor, contains mean NPX for that group

• Group 2: "numeric" Column is named second level of variable when converted to factor, contains mean NPX for that group

• statistic: "named numeric" value of the t-statistic

• p.value: "numeric" p-value for the test

• parameter: "named numeric" degrees of freedom for the t-statistic

• conf.low: "numeric" confidence interval for the mean (lower end)

• conf.high: "numeric" confidence interval for the mean (upper end)

• method: "character" which t-test method was used

• alternative: "character" describes the alternative hypothesis

• Adjusted_pval: "numeric" adjusted p-value for the test (Benjamini&Hochberg)

• Threshold: "character" if adjusted p-value is significant or not (< 0.05)