Help for package NORMAgene

Type:

Package

Title:

Data-Driven qRT-PCR Normalization Using NORMAgene

Version:

0.1.1

Description:

Enables correction for technical variance in raw quantitative reverse transcription polymerase chain reaction (qRT-PCR) data using the least squares-based NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250>. Performs normalization of raw crossing threshold values (CT) and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

Suggests:

testthat (≥ 3.0.0)

Author:

Grant C. O'Connell

[aut, cre]

Maintainer:

Grant C. O'Connell <goconnell.phd@gmail.com>

Repository:

CRAN

Depends:

R (≥ 3.5)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-02-22 15:31:06 UTC; gco6

Date/Publication:

2026-02-28 20:40:13 UTC

Data-Driven qRT-PCR Normalization Using NORMAgene

Description

The 'NORMAgene' package enables normalization of raw quantitative reverse transcription polymerase chain reaction (qRT-PCR) crossing threshold (CT) values using the NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011). The NORMAgene algorithm uses within experimental condition least squares fits to estimate per-replicate technical variance and generate corresponding scaling factors that are ultimately applied for normalization. Normalization is reference gene agnostic, and can be carried out on data from as few as five target genes.

Details

The primary user-facing function is norma_gene(), which is suitable for most standalone single experiment normalization workflows. norma_gene() applies the NORMAgene normalization algorithm to raw CT values provided via an input data frame appended with requisite experimental metadata. In addition to generating normalized CT values, the ratio of standard deviations-based relative variability metric originally described by Heckmann et al. (2011) is also calculated, both within target genes and experimental conditions, which users can employ to evaluate the effect of normalization on CT variability. This metric represents the proportional change in CT-value standard deviation pre to post normalization, with values of less than one indicating a reduction in variance as a result of normalization, and values of greater than one indicating an increase in variance as a result of normalization. The scaling factors applied for normalization can be accessed using correction_factors(), while relative variability metrics can be accessed using relative_variability().

Note: norma_gene_core() provides matrix-based execution of the NORMAgene algorithm and is internally called by norma_gene(). While use of norma_gene() is recommended in a majority of situations, directly calling norma_gene_core() may afford advanced users a lightweight option for cleaner integration into larger post-analytical pipelines. norma_gene_core() is not exported and can only be called using the internal namespace operator.

Two real-world qPCR datasets are also included, which are used in the documentation examples. The dataset multi_cond_data contains raw CT values and experimental meta-data from a case-control comparison of whole blood gene expression in human ischemic stroke published by O’Connell et al. (2017). It can be used to demonstrate or evaluate normalization workflows for use-cases involving data from multiple experimental condition. The dataset single_cond_data contains raw CT values and experimental meta-data from a single cohort study of whole blood gene expression in human ischemic stroke published by O’Connell et al. (2017). It can be used to demonstrate or evaluate normalization workflows for use-cases involving data from a single experimental condition.

Main functions

norma_gene(): Normalize CT values stored in a data frame.
relative_variability(): Extract relative variability metrics.
correction_factors(): Extract per-replicate correction factors.

Datasets

multi_cond_data: Example dataset from a real-world multi-condition experiment.
single_cond_data: Example dataset from a real-world single condition experiment.

Citation

If you use the NORMAgene package in published work, please cite:

O'Connell, GC. (2024). NORMAgene. R package version 0.1.1. Available from https://CRAN.R-project.org/package=NORMAgene.

References

Heckmann, LH., Sørensen, PB., Krogh, PH., & Sørensen, JG. (2011). NORMA-Gene: a simple and robust method for qPCR normalization based on target gene data. BMC Bioinformatics, 12, 250. doi:10.1186/1471-2105-12-250

O'Connell, GC., Treadway, MB., Petrone, AB., Tennant, CS., Lucke-Wold, N, Chantler, PD., & Barr, TL. (2017). Leukocyte dynamics influence reference gene stability in whole blood: Data-driven qRT-PCR normalization is a robust alternative for measurement of transcriptional biomarkers. Laboratory Medicine, 3, 48. doi:10.1093/labmed/lmx035

O'Connell, GC., Treadway, MB., Petrone, AB., Tennant, CS., Lucke-Wold, N, Chantler, PD., & Barr, TL. (2017). Peripheral blood AKAP7 expression as an early marker for lymphocyte-mediated post-stroke blood brain barrier disruption. Scientific Reports, 1, 7. doi:10.1038/s41598-017-01178-5

Retrieve scaling factors from NORMAgene output

Description

Retrieves the per-replicate scaling factors used for normalization.

Usage

correction_factors(x)

Arguments

x

An object returned by norma_gene().

Value

A numeric vector of correction factors. If replicate identifiers were passed to norma_gene(), the vector is named accordingly.

Examples

# load example dataset containing raw CT values and
# metadata from a multi-condition experiment

data(multi_cond_data)
raw_data<-multi_cond_data

#normalize CT values via NORMAgene

norm_data<-norma_gene(
  data = raw_data,
  conditions = "Diagnosis",
  replicates= "Sample_id"
)

# retrieve scaling factors

correction_factors(norm_data)

Example dataset from a multi-condition qPCR experiment.

Description

A real-world qRT-PCR dataset containing raw CT values for 12 genes measured in whole blood total RNA originating from 29 human subjects diagnosed with ischemic stroke and 33 neurologically normal controls, as described in O’Connell et al. (2017).

Format

A data frame structured with biological replicates in rows, replicate identifiers in a single column, stroke diagnoses in a single column, and raw CT values for each of the 12 target genes in the remaining columns.

Details

This dataset is suitable for demonstrating or evaluating normalization workflows for use-cases involving data from multiple experimental conditions.

References

Examples

#load example dataset

data(multi_cond_data)

#return dataset structure

str(multi_cond_data)

Normalize CT values using NORMAgene

Description

Applies the least squares fit-based NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011) to raw CT values provided via an input data frame appended with experimental meta-data, and returns a data frame containing normalized CT values with scaling factors and relative variability metrics attached as attributes.

Usage

norma_gene(data, conditions = NULL, replicates = NULL, ct_values = NULL)

Arguments

data

A data frame structured with biological replicates in rows, and experimental metadata and gene-wise raw CT values in columns.

conditions

A single column name in data specifying experimental condition membership in the case of a multi-condition experiment, or NULL in the case of a single condition experiment. Normalization is applied within experimental conditions when specified, or across all replicates when NULL. This argument must be explicitly provided.

replicates

A single column name in data containing replicate identifiers, or NULL if replicate identifiers are not present. If provided, replicate identifiers are used for naming of outputs only, and are not used in normalization calculations. This argument must be explicitly provided.

ct_values

Optional character vector specifying column names in data containing CT values to be normalized. If NULL, all numeric columns except conditions and replicates are used.

Details

Users must explicitly specify how experimental conditions and replicate identifiers are handled to avoid accidental normalization of numeric metadata. Scaling factors can be retrieved from the output object using correction_factors(). Relative variability metrics can be retrieved from the output object using relative_variability(). For more information on the NORMAgene algorithm or relative variability metrics, see NORMAgene-package.

Value

A data frame with the same organization as data containing normalized CT values and any provided experimental metadata. The per-replicate scaling factors used for normalization, as well as within gene and within experimental condition relative variability metrics, are attached as attributes.

References

Examples

# USE-CASE WITH MULTIPLE EXPERIMENTAL CONDITIONS

# load example dataset containing raw CT values and
# metadata from a multi-condition experiment

data(multi_cond_data)
raw_data<-multi_cond_data

#normalize CT values via NORMAgene

norm_data<-norma_gene(
  data = raw_data,
  conditions = "Diagnosis",
  replicates= "Sample_id"
)

# retrieve relative variability metrics

relative_variability(norm_data, type = "by_gene")
relative_variability(norm_data, type = "by_condition")

# USE-CASE WITH a SINGLE EXPERIMENTAL CONDITION

# load example dataset containing raw CT values and
# metadata from a single-condition experiment

data(single_cond_data)
raw_data<-single_cond_data

#normalize CT values via NORMAgene

norm_data<-norma_gene(
  data = raw_data,
  conditions = NULL,
  replicates= "Sample_id"
)

# retrieve relative variability metrics

relative_variability(norm_data, type = "by_gene")
relative_variability(norm_data, type = "by_condition")

NORMAgene core normalization engine

Description

Applies the least squares fit-based NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011) to a matrix of raw CT values, and returns a list containing a matrix of normalized CT values along with associated scaling factors and relative variability metrics.

Usage

norma_gene_core(X, conditions = NULL)

Arguments

X

A numeric matrix of raw ct values with replicates in rows and genes in columns.

conditions

A vector of factors specifying experimental condition membership for replicates in the case of a multi-condition experiment, or NULL in the case of a single condition experiment. Normalization is applied within experimental conditions when specified, or across all replicates when NULL.

Details

This function implements the core normalization and variance calculations and is primarily intended for internal use; most users should call norma_gene() instead. For more information on the NORMAgene algorithm or relative variability metrics, see NORMAgene-package.

Value

A list with the following components:

norm

A numeric matrix of normalized CT values with identical row and column order as X. Row and column names are inherited from X.

cor_fact

A numeric vector of length nrow(X) containing the per-replicate scaling factors used for normalization.

rel_var

A list containing relative variability metrics:

by_gene: A named numeric matrix of gene-level relative variability values, calculated both within experimental conditions and cumulatively across all experimental conditions.
by_cond: A named numeric vector of relative variability calculated within experimental conditions, as well as cumulatively across all experimental conditions, regardless of gene.

References

Retrieve relative variability metrics from NORMAgene output

Description

Retrieves relative variability metrics calculated during normalization.

Usage

relative_variability(x, type = c("by_gene", "by_condition"))

Arguments

x

An object returned by norma_gene().

type

Character string specifying which relative variability metric to return. One of "by_gene" or "by_condition".

Details

For more information on relative variability metrics, see NORMAgene-package.

Value

Depending on type:

by_gene: A named numeric matrix of gene-level relative variability values, calculated both within experimental conditions and cumulatively across all experimental conditions.
by_condition: A named numeric vector of relative variability calculated within experimental conditions, as well as cumulatively across all experimental conditions, regardless of gene.

Examples

# load example dataset containing raw CT values and
# metadata from a multi-condition experiment

data(multi_cond_data)
raw_data<-multi_cond_data

#normalize CT values via NORMAgene

norm_data<-norma_gene(
  data = raw_data,
  conditions = "Diagnosis",
  replicates= "Sample_id"
)

# retrieve relative variability metrics

relative_variability(norm_data, type = "by_gene")
relative_variability(norm_data, type = "by_condition")

Example dataset from a single condition qPCR experiment.

Description

A real-world qRT-PCR dataset containing raw crossing threshold (CT) values for 12 genes measured in whole blood total RNA originating a single cohort of 26 human subjects diagnosed with ischemic stroke as described in O’Connell et al. (2017).

Format

A data frame structured with biological replicates in rows, replicate identifiers in a single column, and raw CT values for each of the 12 target genes in the remaining columns.

Details

This dataset is suitable for demonstrating or evaluating normalization workflows for use-cases involving data from a single experimental condition.

References

Examples

#load example dataset

data(single_cond_data)

#return dataset structure

str(single_cond_data)

Data-Driven qRT-PCR Normalization Using NORMAgene

Description

Details

Main functions

Datasets

Citation

References

See Also

Retrieve scaling factors from NORMAgene output

Description

Usage

Arguments

Value

See Also

Examples

Example dataset from a multi-condition qPCR experiment.

Description

Format

Details

References

Examples

Normalize CT values using NORMAgene

Description

Usage

Arguments

Details

Value

References

See Also

Examples

NORMAgene core normalization engine

Description

Usage

Arguments

Details

Value

References

See Also

Retrieve relative variability metrics from NORMAgene output

Description

Usage

Arguments

Details

Value

See Also

Examples

Example dataset from a single condition qPCR experiment.

Description

Format

Details

References

Examples