Package 'normScore'

Title: Evaluation and Ranking of Normalization Methods for Proteomics Data
Description: Provides tools to evaluate and rank normalization methods for omics datasets using a composite score derived from multiple performance metrics. The package is designed to support systematic benchmarking and comparison of normalization strategies across datasets and experimental settings. It also includes utilities for summarizing results and visualizing normalization performance.
Authors: Julia García Currás [aut, cre] (ORCID: <https://orcid.org/0009-0002-6354-5035>), Axencia Galega de Innovación (GAIN), Xunta de Galicia [fnd] (Industrial Doctorate Grant 2022-2026, Ref. 23_IN606D_2022_2707220)
Maintainer: Julia García Currás <[email protected]>
License: GPL-2
Version: 0.99.1
Built: 2026-10-11 10:34:06 UTC
Source: https://github.com/bioc/normScore

Help Index

Rank Normalization Methods Using a Composite Scoring Framework

Description

Computes a composite score to rank normalization methods based on multiple quality metrics derived from normalized data matrices.

The function evaluates each normalization method using six criteria: pooled coefficient of variation (PCV), within-group sample correlation, MA-plot trend deviation, mean-SD trend deviation, relative log expression (RLE) consistency, and total intensity consistency. These metrics are scaled, combined into a final score, and used to rank the normalization methods.

In addition, a correction factor derived from the coefficient of variation of raw sample intensities is applied to the "Log" normalization method. Optionally, bootstrap resampling is used to estimate mean composite scores and percentile-based confidence intervals.

Usage

normScore(
  normalizedDataList,
  groupData,
  rawData,
  refGroup = NULL,
  altGroup = NULL,
  returnDetails = TRUE,
  nBoot = 1000
)

Arguments

normalizedDataList

Named list of normalized numeric matrices or data frames. Each element represents one normalization method, with rows corresponding to features (e.g. proteins) and columns to samples.
groupData

data.frame or matrix describing the sample-group assignment. It must contain two columns, which are interpreted as sample names and group labels.
rawData

data.frame or matrix of raw numeric intensities, with rows representing features and columns representing samples.
refGroup

character or NULL. Reference group used for the MA-plot metric. If NULL, the last group in groupData is used.
altGroup

character or NULL. Alternative group used for the metric. If NULL, the first group in groupData is used.
returnDetails

logical. If TRUE, only the final ranking is MA-plot returned. If FALSE, detailed scores, bootstrap results, and a summaryplot are also returned. Default is FALSE.
nBoot

integer. Number of bootstrap resamples used to estimate mean normScore values and confidence intervals. Default is 1000.

Details

The function computes the following six item scores for each normalization method:

  1. Item1: mean pooled coefficient of variation across groups (PVC).

  2. Item2: within-group Spearman correlation summary, transformed so that lower values indicate better performance (Correlation).

  3. Item3: shape-corrected area-based deviation from the expected MA trend (logFC = 0), based on MAplot.

  4. Item4: area-based deviation of the mean-SD trend from horizontality, based on meanSD plot.

  5. Item5: RLE-based mean absolute percentage error relative to 1.

  6. Item6: quantile-based total intensity consistency metric.

Each item is scaled to the range 0 to 1 using min-max scaling and then summed into a total score. Lower scores indicate better normalization performance.

A correction factor based on the coefficient of variation of total raw sample intensities is applied only to the normalization method named "Log".

When returnDetails = FALSE, bootstrap resampling of the six item scores is performed.The results can be visualized using plotBootstrapNormScore.

Value

If returnDetails = TRUE, returns a list with:

finalRanking

Named numeric vector with the final normalization ranking.

If returnDetails = FALSE, returns a list with:

finalRanking

Named numeric vector with the final normalization ranking.

detailRanking

data.frame with scaled item-wise scores, total score, and corrected total score for each normalization method.

bootstrapScore

data.frame with bootstrap mean scores and 95\ percentile confidence intervals for each normalization method.

graphic

A ggplot2 object showing the bootstrap mean scores and confidence intervals.

See Also

plotNormScoreDiagnostics, plotBootstrapNormScore, simulateData

Examples

# Simulate proteomic data
simData <- simulateData(nProteins = 1000)

# Artificial normalization (just adding shifts to raw data)
normalizedDataList <- list(
    Norm1 = simData$logData + 0.1,
    Norm2 = simData$logData + 1,
    Norm3 = simData$logData - 1,
    Norm4 = simData$logData * 1.1,
    Norm5 = simData$logData * 0.9,
    Norm6 = simData$logData * runif(ncol(simData$logData), 0.8, 1.2)
)

# Compute ranking
result <- normScore(
    normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    returnDetails = TRUE,
    nBoot = 100
)
result

Plot bootstrap normScore results

Description

Generates a point-range plot showing the mean bootstrap normScore and its 95% confidence interval for each normalization method.

Usage

plotBootstrapNormScore(x)

Arguments

x

A normScore result object containing a bootstrapScore element. This element should be a data frame with normalization names, mean normScore values, and lower and upper 95% confidence limits. It can beobtained by setting returnDetails = TRUE at normScore main function.

Value

A ggplot object showing bootstrap mean normScore values and 95% confidence intervals for each normalization method.

Examples

bootstrapScore <- data.frame(
    normalization = c("Norm1", "Norm2", "Norm3"),
    meanNormScore = c(0.8, 1.2, 1.5),
    ll95 = c(0.6, 1.0, 1.2),
    ul95 = c(1.0, 1.4, 1.8)
)

result <- list(bootstrapScore = bootstrapScore)

plotBootstrapNormScore(result)

Plot MA diagnostic plots (Item 3)

Description

Generates MA plots for each normalization method using the selected reference and alternative groups. These plots are used to assess intensity-dependent differences between groups.

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Usage

plotDiagnosticMA(normalizedDataList, groupData, refGroup, altGroup)

Arguments

normalizedDataList

A named list of normalized data matrices.
groupData

A data frame with sample names and group labels.
refGroup

Character string indicating the reference group.
altGroup

Character string indicating the alternative group.

Value

An arranged ggplot object containing one MA plot per normalization method.

Examples

# Simulate proteomic data
simData <- simulateData(nProteins = 1000, propDE = 0.5) 

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = cbind(simData$logData[, 1:20] + 
        -0.8 * (rowMeans(simData$logData, na.rm = TRUE) - 
            mean(rowMeans(simData$logData, na.rm = TRUE))), 
        simData$logData[, 21:40]),
    Norm2 = cbind(simData$logData[, 1:20] + 
        0.3 * (rowMeans(simData$logData, na.rm = TRUE) - 
            mean(rowMeans(simData$logData, na.rm = TRUE))), 
        simData$logData[, 21:40])
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot MA diagnostic plots
plotDiagnosticMA(
    normalizedDataList = inputs$normalizedDataList,
    groupData = inputs$groupData,
    refGroup = inputs$refGroup,
    altGroup = inputs$altGroup
) # Best to Worst: Log > Norm2 > Norm1

Plot mean-SD diagnostic plots (Item 4)

Description

Generates diagnostic plots showing the relationship between sample order by mean intensity and standard deviation for each normalization method.

Usage

plotDiagnosticMeanSD(normalizedDataList)

Arguments

normalizedDataList

A named list of normalized data matrices.

Details

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

An arranged ggplot object containing one plot per normalization method.

Examples

# Simulate proteomic data
simData <- simulateData(nProteins = 1000, sampleShiftSd = 0.5,
    sampleShiftCap = 1, sampleSdStrength = -0.5, sampleSdRho = 1.5)

# Artificial normalization (just adding shifts to raw data)
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData * runif(ncol(simData$logData), 0.7, 1.2),
    Norm2 = simData$logData * runif(ncol(simData$logData), 0.8, 1.1)
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot mean-SD diagnostic plots
plotDiagnosticMeanSD(
    normalizedDataList = inputs$normalizedDataList
) # Best to Worst: Norm1 > Norm2 > Log

Plot pooled coefficient of variation (Item 1)

Description

Generates the diagnostic plot associated with the PCV item. PCV values are computed for each normalization method and group, and displayed as point-range or boxplot-based summaries depending on the number of groups.

Usage

plotDiagnosticPCV(normalizedDataList, groupData)

Arguments

normalizedDataList

A named list of normalized data matrices.
groupData

A data frame with sample names and group labels.

Details

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

A ggplot or arranged ggplot object.

Examples

# Simulate proteomic data with considerable systematic bias
simData <- simulateData(nProteins = 1000, sampleShiftSd = 1,
    sampleShiftCap = 0.9)

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData + 
        rnorm(n = ncol(simData$logData), mean = 5, sd = 0.5),
    Norm2 = sweep(simData$logData, 2, 
        apply(simData$logData, 2, median, na.rm = TRUE) - 
        median(as.matrix(simData$logData), na.rm = TRUE), "-")
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot pooled coefficient of variation
plotDiagnosticPCV(
    normalizedDataList = inputs$normalizedDataList,
    groupData = inputs$groupData
) # Best to Worst: Norm2 > Norm1 > Log

Plot RLE distributions (Item 5)

Description

Generates RLE distribution plots for each normalization method. These plots are used to inspect whether sample-wise RLE distributions are centered and comparable after normalization.

Usage

plotDiagnosticRLE(normalizedDataList)

Arguments

normalizedDataList

A named list of normalized data matrices.

Details

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

An arranged ggplot object containing one RLE plot per normalization method.

Examples

# Simulate proteomic data with considerable systematic bias
simData <- simulateData(nProteins = 1000, sampleShiftSd = 1,
    sampleShiftCap = 0.9)

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData + 
        rnorm(n = ncol(simData$logData), mean = 5, sd = 0.5),
    Norm2 = sweep(simData$logData, 2, 
        apply(simData$logData, 2, median, na.rm = TRUE) - 
        median(as.matrix(simData$logData), na.rm = TRUE), "-")
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot RLE distributions
plotDiagnosticRLE(
    normalizedDataList = inputs$normalizedDataList
) # Best to Worst: Norm2 > Log = Norm1

Plot functions for original criteria

Description

This file contains plot functions used as outputs for interpreting the detailed ranking results. Plot raw total intensity across samples (ITEM 0)

Usage

plotDiagnosticSystematicBias(data)

Arguments

data

A raw intensity matrix or data frame with proteins in rows and samples in columns.

Details

Generates the diagnostic plot associated with the systematic bias penalty item (item 0). Raw total intensity is computed for each sample and displayed as a bar plot.

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

A ggplot object.

Examples

# Simulate proteomic data with considerable systematic bias
simData <- simulateData(nProteins = 1000, sampleShiftSd = 1,
    sampleShiftCap = 0.9)

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData + 
        rnorm(n = ncol(simData$logData), mean = 5, sd = 0.5),
    Norm2 = sweep(simData$logData, 2, 
        apply(simData$logData, 2, median, na.rm = TRUE) - 
        median(as.matrix(simData$logData), na.rm = TRUE), "-")
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot Systematic Bias Penalty
plotDiagnosticSystematicBias(data = inputs$rawData)

Plot log-intensity distributions (Item 6)

Description

Generates sample-wise intensity distribution plots for each normalization method. These plots are used to visually inspect global distributional consistency across samples.

Usage

plotDiagnosticTotalIntensity(normalizedDataList)

Arguments

normalizedDataList

A named list of normalized data matrices.

Details

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

An arranged ggplot object containing one intensity distribution plot per normalization method.

Examples

# Simulate proteomic data with considerable systematic bias
simData <- simulateData(nProteins = 1000, sampleShiftSd = 1,
    sampleShiftCap = 0.9)

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData + 
        rnorm(n = ncol(simData$logData), mean = 5, sd = 0.5),
    Norm2 = sweep(simData$logData, 2, 
        apply(simData$logData, 2, median, na.rm = TRUE) - 
        median(as.matrix(simData$logData), na.rm = TRUE), "-")
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot log-intensity distributions
plotDiagnosticTotalIntensity(
    normalizedDataList = inputs$normalizedDataList
) # Best to Worst: Norm2 > Norm1 = Log

Plot within-group correlations (Item 2)

Description

Generates the diagnostic plot associated with the correlation item. The plot displays the distribution of within-group sample correlations for each normalization method.

Usage

plotDiagnosticWithinGroupCor(normalizedDataList, groupData)

Arguments

normalizedDataList

A named list of normalized data matrices.
groupData

A data frame with sample names and group labels.

Details

Important! First run validateNormScoreInput() function and them use the correspondent outputs to plot individual diagnostic graphs as it is shown in the example below.

Value

A ggplot object.

Examples

# Simulate proteomic data with considerable systematic bias
simData <- simulateData(nProteins = 1000, sampleShiftSd = 1,
    sampleShiftCap = 0.9)

# Adding variations to log raw data to simulate different normalization 
# situations
normalizedDataList <- list(
    Log = simData$logData,
    Norm1 = simData$logData + 
        rnorm(n = ncol(simData$logData), mean = 5, sd = 0.5),
    Norm2 = sweep(simData$logData, 2, 
        apply(simData$logData, 2, median, na.rm = TRUE) - 
        median(as.matrix(simData$logData), na.rm = TRUE), "-")
)

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
) 

# Plot within-group correlations
plotDiagnosticWithinGroupCor(
    normalizedDataList = inputs$normalizedDataList,
    groupData = inputs$groupData
) # Best to Worst: Log > Norm2 > Norm1

Plot normScore diagnostic items

Description

Generates the set of diagnostic plots associated with the individual normScore evaluation items. These plots provide a visual interpretation of the criteria used to assess normalization performance, including raw total intensity, pooled coefficient of variation, within-group correlation, MA plots, mean-SD trends, RLE distributions, and intensity distributions.

Usage

plotNormScoreDiagnostics(
  normalizedDataList,
  groupData,
  rawData,
  refGroup = NULL,
  altGroup = NULL
)

Arguments

normalizedDataList

A named list of normalized data matrices or data frames. Each element should contain proteins in rows and samples in columns.
groupData

A data frame containing sample-group annotation. The first column is assumed to contain sample names and the second column group labels.
rawData

A matrix or data frame containing raw intensity values, with proteins in rows and samples in columns.
refGroup

Character string indicating the reference group used for the MA-plot diagnostic. If NULL, it is automatically selected.
altGroup

Character string indicating the alternative group used for the MA-plot diagnostic. If NULL, it is automatically selected.

Details

The function validates and aligns the input data before plotting. If only one group is provided, the MA-plot diagnostic is skipped because it requires a comparison between two groups.

Value

A named list of diagnostic plots. Each element corresponds to one normScore item. If the input contains a single group, the MA-plot element is returned as NULL.

Examples

# Simulate proteomic data
simData <- simulateData(nProteins = 1000)

# Normalyze ysing NormalyzerDE package
normalizedDataList <- list(
    Norm1 = simData$logData + 0.1,
    Norm2 = simData$logData + 1,
    Norm3 = simData$logData - 1,
    Norm4 = simData$logData * 1.1,
    Norm5 = simData$logData * 0.9,
    Norm6 = simData$logData * runif(ncol(simData$logData), 0.8, 1.2)
)

# Compute ranking
plotNormScoreDiagnostics(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL
)

Simulate label-free proteomics-like two-group data

Description

Simulates two-group quantitative omics data with controlled signal structure, including abundance-dependent variance, differential expression, sample correlation, global shifts, mean-SD dependence, and optional MNAR missingness.

Usage

simulateData(
  nProteins = 10000,
  nPerGroup = 20,
  muMean = 18,
  muSd = 1.2,
  muClip = c(15, 25),
  rhoWithin = 0.85,
  rhoBetween = 0.55,
  loadingSd = 0.25,
  sigmaHi = 0.05,
  sigmaLo = 0.4,
  gammaSigma = 2.5,
  propDE = 0.35,
  logFCSd = 1,
  logFCMean = 0,
  heteroLogFC = TRUE,
  fcHi = 0.55,
  fcLo = 2.5,
  gammaFC = 7,
  sampleShiftSd = 0,
  sampleShiftCap = 0.2,
  sampleSdStrength = 0,
  sampleSdRho = 0.8,
  sampleSdCap = 0.35,
  addMissing = TRUE,
  targetMissing = 0.001,
  kMnar = 1.2,
  missingBySampleSd = 0.05,
  seed = 9396
)

Arguments

nProteins

integer. Number of proteins or features to simulate. Default is 10000.
nPerGroup

integer. Number of samples per group. Default is 20.
muMean

numeric. Mean of the protein-wise abundance distribution on the log2 scale. Default is 18.
muSd

numeric. Standard deviation of the protein-wise abundance distribution on the log2 scale. Default is 1.2.
muClip

numeric vector of length 2. Lower and upper bounds used to truncate simulated protein means. Default is c(15, 25).
rhoWithin

numeric. Within-group correlation target for the latent sample factor. Default is 0.85.
rhoBetween

numeric. Between-group correlation target for the latent sample factor. Default is 0.55.
loadingSd

numeric. Standard deviation of protein-specific loadings for the correlated latent factor. Default is 0.25.
sigmaHi

numeric. Residual standard deviation at high abundance. Default is 0.05.
sigmaLo

numeric. Residual standard deviation at low abundance. Default is 0.4.
gammaSigma

numeric. Controls how strongly residual variance depends on abundance. Default is 2.5.
propDE

numeric. Proportion of differentially expressed proteins. Default is 0.35.
logFCSd

numeric. Standard deviation of differential expression log-fold changes. Default is 1.
logFCMean

numeric. Mean of differential expression log-fold changes. Default is 0.
heteroLogFC

logical. If TRUE, the variance of log-fold changes depends on abundance. Default is TRUE.
fcHi

numeric. Lower multiplier for abundance-dependent logFC heterogeneity. Default is 0.55.
fcLo

numeric. Upper multiplier for abundance-dependent logFC heterogeneity. Default is 2.5.
gammaFC

numeric. Controls how strongly logFC variability depends on abundance. Default is 7.
sampleShiftSd

numeric. Standard deviation of global sample shifts. Default is 0.
sampleShiftCap

numeric. Maximum absolute value allowed for global sample shifts. Default is 0.2.
sampleSdStrength

numeric. Strength of sample-specific mean-SD dependence. A value of 0 implies independence. Default is 0.
sampleSdRho

numeric. Correlation between sample mean rank and sample-specific SD effect. Must lie between 0 and 1. Default is 0.8.
sampleSdCap

numeric. Maximum absolute value allowed for the log-multiplier controlling sample-specific SD effects. Default is 0.35.
addMissing

logical. Should MNAR missing values be added? Default is TRUE.
targetMissing

numeric. Target overall proportion of missing Default is 0.001.
kMnar

numeric. Strength of abundance dependence in the MNAR missing values. value mechanism. Default is 1.2.
missingBySampleSd

numeric. Standard deviation of sample-specific missingness shifts. Default is 0.05.
seed

integer. Random seed used for reproducibility. Default is 9396.

Details

The simulated data include several structured components:

  1. Protein-wise mean abundance on the log2 scale.

  2. A latent correlation structure inducing stronger within-group than between-group sample correlation.

  3. Abundance-dependent heteroscedastic residual noise to create an MA-like wedge pattern.

  4. Symmetric differential expression between the two groups.

  5. Optional global sample shifts affecting overall intensity.

  6. Optional sample-level mean-SD dependence.

  7. Optional MNAR missingness driven by abundance and sample-specific effects.

The output is intended for testing normalization methods and associated scoring procedures under controlled simulation settings.

Value

A list with the following elements:

logData

Numeric matrix of simulated log2-scale data.

rawData

Numeric matrix of simulated raw-scale data, obtained as 2^logData.

metadata

data.frame containing sample names and group labels.

Examples

simData <- simulateData(
    nProteins = 1000,
    nPerGroup = 5,
    propDE = 0.2,
    addMissing = TRUE,
    seed = 123
)

dim(simData$logData)
head(simData$metadata)

Validate and prepare inputs for normScore and diagnostic graphs

Description

Checks the input objects used by normScore(), including group annotation, raw data, normalized datasets, sample names, protein names, and bootstrap settings. The function also aligns the order of rows and columns across normalized datasets, adds the log-transformed dataset when required, and identifies whether the analysis contains a single group.

Usage

validateNormScoreInput(
  normalizedDataList,
  groupData,
  rawData,
  refGroup,
  altGroup,
  returnDetails = NULL,
  nBoot = NULL,
  fromMainFunction = FALSE
)

Arguments

normalizedDataList

A named list of normalized data matrices or data frames. Each element should contain proteins in rows and samples in columns.
groupData

A data frame or object coercible to a data frame containing sample-group annotation. The first column is assumed to contain sample names and the second column group labels.
rawData

A matrix or data frame containing raw intensity values, with proteins in rows and samples in columns.
refGroup

Character string indicating the reference group used for group-comparison metrics. If NULL or invalid, it is automatically set.
altGroup

Character string indicating the alternative group used for group-comparison metrics. If NULL or invalid, it is automatically set.
returnDetails

Logical value indicating whether detailed results should be returned by normScore().
nBoot

Numeric value indicating the number of bootstrap resamples.
fromMainFunction

Logical value indicating wheter inputs will be used to normScore() main function (nBoot and returnDetails are required), or only for displaying diagnostic plots. Default: FALSE.

Details

It can also be used to validate data for diagnostic plotting, using either the main global funcion plotNormScoreDiagnostic() or any individual plotting function. In this case, some input parameters are not requiered for the validation function (nBoot and returnDetails).

Value

A list containing validated and prepared inputs: normalizedDataList, groupData, rawData, refGroup, altGroup, returnDetails, nBoot, and singleGroup.

Examples

# Simulate proteomic data
simData <- simulateData(nProteins = 1000)

# Artificial normalization (just adding shifts to raw data)
normalizedDataList <- list(
    Norm1 = simData$logData + 0.1,
    Norm2 = simData$logData + 1,
    Norm3 = simData$logData - 1,
    Norm4 = simData$logData * 1.1,
    Norm5 = simData$logData * 0.9,
    Norm6 = simData$logData * runif(ncol(simData$logData), 0.8, 1.2)
)

# Validating inputs for normScore() function
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    returnDetails = TRUE,
    nBoot = 100, 
    fromMainFunction = TRUE
) 

# Validating inputs for diagnostic graphs
inputs <- validateNormScoreInput(
    normalizedDataList = normalizedDataList,
    groupData = simData$metadata,
    rawData = simData$rawData,
    refGroup = NULL,
    altGroup = NULL,
    fromMainFunction = FALSE
)