Delta Delta Ct ANCOVA analysis

Description

Apply Delta Delta Ct (ddCt) analysis to each target gene and performs per-gene statistical analysis.

Usage

ANCOVA_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  mainFactor.level.order = NULL,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Arguments

Details

ddCt analysis of covariance (ANCOVA) is performed for the levels of the mainFactor.column and the other factors are treated as covariates. if the interaction between the main factor and the covariate is significant, ANCOVA is not appropriate. ANCOVA is basically used when a factor is affected by uncontrolled quantitative covariate(s). For example, suppose that wDCt of a target gene in a plant is affected by temperature. The gene may also be affected by drought. Since we already know that temperature affects the target gene, we are interested to know if the gene expression is also altered by the drought levels. We can design an experiment to understand the gene behavior at both temperature and drought levels at the same time. The drought is another factor (the covariate) that may affect the expression of our gene under the levels of the first factor i.e. temperature. The data of such an experiment can be analyzed by ANCOVA or using ANOVA based on a factorial experiment. ANCOVA is done even there is only one factor (without covariate or factor variable).

All the functions for relative expression analysis (including 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()') return the relative expression table which include fold change and corresponding statistics. The output of 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()' also include lm models, residuals, raw data and ANOVA table for each gene.

The expression table returned by 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', and 'REPEATED_DDCt()' functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), ddCt (mean of weighted delta delta Ct values), RE (relative expression or fold change = 2^-ddCt), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

Value

An object containing expression table, lm model, residuals, raw data and ANOVA table for each gene:

ddCt expression table along with per-gene statistical comparison outputs

object$relativeExpression

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm

lm_formula

object$perGene$gene_name$lm_formula

Residuals

resid(object$perGene$gene_name$lm)

References

LivakKJ, Schmittgen TD (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods, 25(4), 402–408. doi:10.1006/meth.2001.1262

Ganger MT, Dietz GD, and Ewing SJ (2017). A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC Bioinformatics, 18, 1–11.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. (2019). The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37, 761-774.

Yuan JS, Reed A, Chen F, Stewart N (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANCOVA_DDCt(x = data1,
           numOfFactors = 2,
           numberOfrefGenes = 2,
           block = "block",
           mainFactor.column = 2,
           p.adj = "none")
           
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))          
ANCOVA_DDCt(x = data2,
           numOfFactors = 1,
           numberOfrefGenes = 1,
           block = NULL,
           mainFactor.column = 1,
           p.adj = "none")
           

Delta Ct ANOVA analysis

Description

Performs Delta Ct (dCt) analysis of the data from a 1-, 2-, or 3-factor experiment. Per-gene statistical grouping is also performed for all treatment (T) combinations.

Usage

ANOVA_DCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  block,
  alpha = 0.05,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Arguments

Details

The function returns analysis of variance components and the expression table which include these columns: gene (name of target genes), factor columns, dCt (mean weighted delta Ct for each treatment combination), RE (relative expression = 2^-dCt), log2FC (log(2) of relative expression), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of the mean calculated from the weighted delta Ct (wDCt) values of each treatment combination), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), Upper.se.log2FC (The upper limit error bar for log2 RE), and sig (per-gene significance grouping letters).

Value

An object containing expression table, lm models, ANOVA table, residuals, and raw data for each gene:

dCt expression table for all treatment combinations along with the per-gene statistical grouping

object$relativeExpression

ANOVA table for treatments

object$perGene$gene_name$ANOVA_T

ANOVA table factorial

object$perGene$gene_name$ANOVA_factorial

lm ANOVA for tratments

object$perGene$gene_name$lm_T

lm ANOVA factorial

object$perGene$gene_name$lm_factorial

Residuals

resid(object$perGene$gene_name$lm_T)

Examples

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)

Delta Delta Ct ANOVA analysis

Description

Apply Delta Delta Ct (ddCt) analysis to each target gene and performs per-gene statistical analysis.

Usage

ANOVA_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  mainFactor.level.order = NULL,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Arguments

Details

ddCt analysis of variance (ANOVA) is performed for the mainFactor.column based on a full model factorial experiment by default. However, if ANCOVA_DDCt function is used, analysis of covariance is performed for the levels of the mainFactor.column and the other factors are treated as covariates. if the interaction between the main factor and the covariate is significant, ANCOVA is not appropriate.

All the functions for relative expression analysis (including 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()') return the relative expression table which include fold change and corresponding statistics. The output of 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()' also include lm models, residuals, raw data and ANOVA table for each gene.

The expression table returned by 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', and 'REPEATED_DDCt()' functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), ddCt (mean of weighted delta delta Ct values), RE (relative expression or fold change = 2^-ddCt), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

Value

An object containing expression table, lm model, residuals, raw data and ANOVA table for each gene:

ddCt expression table along with per-gene statistical comparison outputs

object$relativeExpression

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm

lm_formula

object$perGene$gene_name$lm_formula

Residuals

resid(object$perGene$gene_name$lm)

References

LivakKJ, Schmittgen TD (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods, 25(4), 402–408. doi:10.1006/meth.2001.1262

Ganger MT, Dietz GD, and Ewing SJ (2017). A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC Bioinformatics, 18, 1–11.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. (2019). The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37, 761-774.

Yuan JS, Reed A, Chen F, Stewart N (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANOVA_DDCt(x = data1,
           numOfFactors = 2,
           numberOfrefGenes = 2,
           block = "block",
           mainFactor.column = 2,
           p.adj = "none")
           
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))          
ANOVA_DDCt(x = data2,
           numOfFactors = 1,
           numberOfrefGenes = 1,
           block = NULL,
           mainFactor.column = 1,
           p.adj = "none")
           

Delta Delta Ct pairwise comparisons using a fitted model

Description

Performs relative expression (fold change) analysis based on the Delta Delta Ct (ddCt) methods using a fitted model object produced by ANOVA_DCt(), ANOVA_DDCt() or REPEATED_DDCt().

Usage

Means_DDCt(model, specs, p.adj = "none")

Arguments

Details

The Means_DDCt function performs pairwise comparisons of relative expression values fo all combinations using estimated marginal means derived from a fitted model. For ANOVA models, relative expression values can be obtained for main effects, interactions, and sliced (simple) effects. For ANCOVA models returned by the rtpcr package, only simple effects are supported.

Internally, this function relies on the emmeans package to compute marginal means and contrasts, which are then back-transformed to fold change values using the ddCt framework.

Value

A data frame containing estimated relative expression values, confidence intervals, p-values, and significance levels derived from the fitted model.

Author(s)

Ghader Mirzaghaderi

Examples

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))

# Obtain a fitted model from ANOVA_DDCt
res <- ANOVA_DDCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL)

# Relative expression values for Type main effect
lm <- res$perGene$PO$lm
Means_DDCt(lm, specs = "Type")

# Relative expression values for Concentration main effect
Means_DDCt(lm, specs = "Conc")

# Relative expression values for Concentration sliced by Type
Means_DDCt(lm, specs = "Conc | Type")

# Relative expression values for Concentration sliced by Type and SA
Means_DDCt(lm, specs = "Conc | Type * SA")

Delta Delta Ct repeated measure analysis

Description

REPEATED_DDCt function performs Delta Delta Ct (ddCt) method analysis of observations repeatedly taken over different time courses. Data may be obtained over time from a uni- or multi-factorial experiment.

Usage

REPEATED_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  mainFactor.level.order = NULL,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Arguments

Details

ddCt analysis of repeated measure data is performed for the mainFactor.column based on a full model factorial experiment.

All the functions for relative expression analysis (including 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()') return the relative expression table which include fold change and corresponding statistics. The output of 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()' also include lm models, residuals, raw data and ANOVA table for each gene.

The expression table returned by 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', and 'REPEATED_DDCt()' functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), ddCt (mean of weighted delta delta Ct values), RE (relative expression or fold change = 2^-ddCt), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

Value

An object containing expression table, lm model, residuals, raw data and ANOVA table for each gene:

ddCt expression table along with per-gene statistical comparison outputs

object$relativeExpression

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm

lm_formula

object$perGene$gene_name$lm_formula

Residuals

resid(object$perGene$gene_name$lm)

References

LivakKJ, Schmittgen TD (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods, 25(4), 402–408. doi:10.1006/meth.2001.1262

Ganger MT, Dietz GD, and Ewing SJ (2017). A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC Bioinformatics, 18, 1–11.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. (2019). The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37, 761-774.

Yuan JS, Reed A, Chen F, Stewart N (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

data1 <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
REPEATED_DDCt(
  data1,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL)



data2 <- read.csv(system.file("extdata", "data_repeated_measure_2.csv", package = "rtpcr"))
REPEATED_DDCt(
  data2,
  numOfFactors = 2,
  numberOfrefGenes = 1,
  mainFactor.column = 2, 
  block = NULL,
  p.adj = "none")
          

Delta Delta Ct method t-test analysis

Description

The TTEST_DDCt function performs fold change expression analysis based on the \Delta \Delta C_T method using Student's t-test. It supports analysis of one or more target genes evaluated under two experimental conditions (e.g. control vs treatment).

Usage

TTEST_DDCt(
  x,
  numberOfrefGenes,
  Factor.level.order = NULL,
  paired = FALSE,
  var.equal = TRUE,
  p.adj = "none",
  order = "none"
)

Arguments

Details

Relative expression values are computed using one or more reference genes for normalization. Both paired and unpaired experimental designs are supported.

Paired samples in quantitative PCR refer to measurements collected from the same individuals under two different conditions (e.g. before vs after treatment), whereas unpaired samples originate from different individuals in each condition. Paired designs allow within-individual comparisons and typically reduce inter-individual variability.

The function returns numerical summaries as well as bar plots based on either relative expression (RE) or log2 fold change (log2FC).

All the functions for relative expression analysis (including 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()') return the relative expression table which include fold change and corresponding statistics. The output of 'ANOVA_DDCt()', 'ANCOVA_DDCt()', 'ANCOVA_DDCt()', 'REPEATED_DDCt()', and 'ANOVA_DCt()' also include lm models, residuals, raw data and ANOVA table for each gene.

The expression table returned by 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', and 'REPEATED_DDCt()' functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), RE (relative expression or fold change), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

Value

A list with the following components:

Result

Table containing RE values, log2FC, p-values, significance codes, confidence intervals, standard errors, and lower/upper SE limits.

Author(s)

Ghader Mirzaghaderi

References

LivakKJ, Schmittgen TD (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods, 25(4), 402–408. doi:10.1006/meth.2001.1262

Ganger MT, Dietz GD, and Ewing SJ (2017). A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC Bioinformatics, 18, 1–11.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. (2019). The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37, 761-774.

Yuan JS, Reed A, Chen F, Stewart N (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

# Example data structure
data1 <- read.csv(system.file("extdata", "data_ttest18genes.csv", package = "rtpcr"))

# Unpaired t-test
TTEST_DDCt(
  data1,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1)

# With amplification efficiencies
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref_Eff.csv", package = "rtpcr"))

TTEST_DDCt(
  data2,
  numberOfrefGenes = 1)

# Two reference genes
data3 <- read.csv(system.file("extdata", "data_1factor_Two_ref.csv", package = "rtpcr"))
TTEST_DDCt(
  data3,
  numberOfrefGenes = 2)

Delta Delta Ct method wilcox.test analysis

Description

The WILCOX_DDCt function performs fold change expression analysis based on the \Delta \Delta C_T method using wilcox.test. It supports analysis of one or more target genes evaluated under two experimental conditions (e.g. control vs treatment).

Usage

WILCOX_DDCt(
  x,
  numberOfrefGenes,
  Factor.level.order = NULL,
  paired = FALSE,
  p.adj = "none",
  order = "none"
)

Arguments

Details

Relative expression values are computed using reference gene(s) for normalization. Both paired and unpaired experimental designs are supported.

Paired samples in quantitative PCR refer to measurements collected from the same individuals under two different conditions (e.g. before vs after treatment), whereas unpaired samples originate from different individuals in each condition. Paired designs allow within-individual comparisons and typically reduce inter-individual variability.

The function returns expression table. The expression table returned by 'TTEST_DDCt()', 'WILCOX_DDCt()', 'ANOVA_DDCt()', 'ANCOVA_DDCt()', and 'REPEATED_DDCt()' functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), RE (relative expression or fold change), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

Value

A table containing RE values, log2FC, p-values, significance, confidence intervals, standard errors, and lower/upper SE limits.

Author(s)

Ghader Mirzaghaderi

References

Yuan, J. S., Reed, A., Chen, F., and Stewart, N. (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

# Example data structure
data <- read.csv(system.file("extdata", "data_Yuan2006PMCBioinf.csv", package = "rtpcr"))

# Unpaired t-test
WILCOX_DDCt(
  data,
  paired = FALSE,
  numberOfrefGenes = 1)


# Two reference genes
data2 <- read.csv(system.file("extdata", "data_1factor_Two_ref.csv", package = "rtpcr"))
WILCOX_DDCt(
  data2,
  numberOfrefGenes = 2,
  p.adj = "none")

Cleaning data and weighted delta Ct (wDCt) calculation

Description

The compute_wDCt function cleans the data and computes wDCt. This function is automatically applied to the expression analysis functions like ANOVA_DDCt, TTEST_DDCt, etc. So it should not be applied in advance of expression analysis functions.

Usage

compute_wDCt(x, numOfFactors, numberOfrefGenes, block)

Arguments

Details

The compute_wDCt function computes weighted delta Ct (wDCt) for the input data. Missing data can be denoted by NA in the input data frame. Values such as '0' and 'undetermined' (for any E and Ct) are automatically converted to NA. For target genes, NA for E or Ct measurements cause returning NA for the corresponding delta Ct for that replicate (row). If there are more than one reference gene, NA in the place of the E or the Ct value cause skipping that gene and remaining references are geometrically averaged. The compute_wDCt function is automatically applied to the expression analysis functions.

Value

The original data frame along with the weighted delta Ct column.

Examples

           
data <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
data
compute_wDCt(x = data,
             numOfFactors = 2,
             numberOfrefGenes = 3,
             block = "block")

Sample data (one factor three levels)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_1factor

Format

A data frame with 9 observations and 6 variables:

SA

An experimental factor here called SA

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample qPCR data (one target and two reference genes under two different conditions)

Description

Sample qPCR data (one target and two reference genes under two different conditions)

Usage

data_1factor_Two_ref

Format

A data frame with 6 observations and 8 variables:

Condition

Experimental conditions

Rep

Biological replicates

DER5

amplification efficiency of DER5 gene

Ct_DER5

Ct values of DER5 gene. Each is the mean of technical replicates

Actin

Amplification efficiency of Actin gene

Ct_Actin

Ct values of Actin gene. Each is the mean of technical replicates

HPRT

Amplification efficiency of HPRT gene

Ct_HPRT

Ct values of HPRT gene

Source

Not applicable

Sample qPCR data (two different conditions)

Description

Sample qPCR data (two different conditions)

Usage

data_1factor_one_ref

Format

A data frame with 6 observations and 10 variables:

Condition

Experimental conditions

Rep

Biological replicates

C2H2_26

amplification efficiency of C2H2_26 gene

C2H2_26_Ct

Ct values of C2H2_26 gene. Each is the mean of technical replicates

C2H2_01

Amplification efficiency of C2H2_01 gene

C2H2_01_Ct

Ct values of C2H2_01 gene. Each is the mean of technical replicates

C2H2_12

Amplification efficiency of C2H2_12 gene

C2H2_12_Ct

Ct values of C2H2_12 gene

ref

Amplification efficiency of ref gene

ref_Ct

Ct values of ref gene

Source

University of Kurdistan

Sample qPCR data (two different conditions)

Description

Sample qPCR data (two different conditions)

Usage

data_1factor_one_ref_Eff

Format

A data frame with 6 observations and 6 variables:

Con

Experimental conditions

r

Biological replicates

target

Amplification efficiency of target gene

target_Ct

Ct values of target gene

Actin

Amplification efficiency of reference gene

Actin_Ct

Ct values of reference gene

Source

University of Kurdistan

Sample data (two factor)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factor

Format

A data frame with 18 observations and 7 variables:

Genotype

First experimental factor

Drought

Second experimental factor

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor)

Description

A sample qPCR data set with blocking factor. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factor3ref

Format

A data frame with 18 observations and 8 variables:

factor1

First experimental factor

factor2

Second experimental factor

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

ref2

Mean amplification efficiency of ref2 gene

Ct_ref2

Ct values of ref2 gene. Each is the mean of technical replicates

ref3

Mean amplification efficiency of ref3 gene

Ct_ref3

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor)

Description

A sample qPCR data set with blocking factor. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factorBlock

Format

A data frame with 18 observations and 8 variables:

Type

First experimental factor

Concentration

Second experimental factor

block

Second experimental factor

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor and 3 reference genes)

Description

A sample qPCR data set with blocking factor and 3 reference genes. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factorBlock3ref

Format

A data frame with 18 observations and 8 variables:

Type

First experimental factor

Concentration

Second experimental factor

block

blocking factor

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

NLM

Mean amplification efficiency of NLM gene

Ct_NLM

Ct values of NLM gene. Each is the mean of technical replicates

ref1

Mean amplification efficiency of ref1 gene

Ct_ref1

Ct values of ref1 gene. Each is the mean of technical replicates

ref2

Mean amplification efficiency of ref2 gene

Ct_ref2

Ct values of ref2 gene. Each is the mean of technical replicates

ref3

Mean amplification efficiency of ref3 gene

Ct_ref3

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data (three factor)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_3factor

Format

A data frame with 36 observations and 8 variables:

Type

First experimental factor

Conc

Second experimental factor

SA

Third experimental factor

Replicate

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (multi-group design with 6 target genes)

Description

A sample qPCR data set with four experimental groups (Uninjected, SNConly, DMSORPE, SNCRPE) and six target genes normalized to one reference gene (Gapdh). Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_Heffer2020PlosOne

Format

A data frame with 18 observations and 16 variables:

Treatment

Experimental treatment group

rep

Biological replicates

Fn1, Ct.Fn1

Efficiency and Ct values of Fn1 gene

Col1a1, Ct.Col1a1

Efficiency and Ct values of Col1a1 gene

Acta2, Ct.Acta2

Efficiency and Ct values of Acta2 gene

TgfB, Ct.TgfB

Efficiency and Ct values of TgfB gene

Tnfa, Ct.Tnfa

Efficiency and Ct values of Tnfa gene

Mcp1, Ct.Mcp1

Efficiency and Ct values of Mcp1 gene

Gapdh, Ct.Gapdh

Efficiency and Ct values of Gapdh reference gene

Details

This dataset is suitable for t-test or one-way ANOVA based expression analysis of multiple genes.

Source

Not applicable

Sample data (with technical replicates)

Description

A sample data for calculating biological replicated. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_Lee_etal2020qPCR

Format

A data frame with 72 observations and 8 variables:

factor1

experimental factor

DS

DS

biolRep

biological replicate

techRep

technical replicates

APOE

Amplification efficiency of APOE gene

Ct_APOE

Ct of APOE gene

GAPDH

Amplification efficiency of GAPDH gene

Ct_GAPDH

Ct of GAPDH gene

Source

Lee et al, (2020) <doi:10.12688/f1000research.23580.2>

Sample data in (one factor with one reference gene)

Description

A sample qPCR data set with one experimental factor (condition) and one reference gene. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_Yuan2006PMCBioinf

Format

A data frame with 24 observations and 6 variables:

condition

Experimental factor with two levels (control, treatment)

rep

Biological replicates

target

Mean amplification efficiency of target gene

Ct_target

Ct values of target gene. Each is the mean of technical replicates

ref

Mean amplification efficiency of reference gene

Ct_ref

Ct values of reference gene. Each is the mean of technical replicates

Source

Yuan2006PMCBioinf

Sample qPCR data: amplification efficiency 1

Description

A sample qPCR dataset for demonstrating efficiency calculation.

Usage

data_efficiency1

Format

A data frame with 21 observations and 4 variables:

Dilutions

Dilution factor

C2H2.26

Target gene 1

C2H2.01

Target gene 2

GAPDH

Reference gene

Source

Not applicable

Sample qPCR data: amplification efficiency 2

Description

A sample qPCR dataset for demonstrating efficiency calculation.

Usage

data_efficiency_Yuan2006PMCBioinf

Format

A data frame with 12 observations and 5 variables:

dilutions

dilution factor

ref_control

reference gene in control condition

ref_treatment

reference gene in treatment condition

target_control

target gene in control condition

target_treatment

Reference gene in treatment condition

Source

Yuan2006PMCBioinf

Repeated measure sample data

Description

A repeated measure sample data in which 3 individuals have been analysed. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate one individual. samples are taken or measurements are scored over different time points (time column) from each individual.

Usage

data_repeated_measure_1

Format

A data frame with 9 observations and 6 variables:

time

time course levels

id

experimental factor

Target

Amplification efficiency of target gene

Ct_Target

Ct of target gene

Ref

Amplification efficiency of reference gene

Ct_Ref

Ct of reference gene

Source

NA

Repeated measure sample data

Description

A repeated measure sample data in which 6 individuals have been analysed. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate one individual. samples are taken or measurements are scored over different time points (time column) from each individual.

Usage

data_repeated_measure_2

Format

A data frame with 18 observations and 7 variables:

treatment

treatment

time

time course levels

id

experimental factor

Target

Amplification efficiency of target gene

Target_Ct

Ct of target gene

Ref

Amplification efficiency of reference gene

Ref_Ct

Ct of reference gene

Source

NA

Sample data in (repeated-measures design with two reference genes)

Description

A sample qPCR data set with repeated measurements over time, a blocking factor, and two reference genes. The experiment includes two treatments (untreated and treated) with repeated measures on the same individuals (id).

Usage

data_repeated_measure_3bLock

Format

A data frame with 18 observations and 10 variables:

treatment

Experimental treatment (untreated, treated)

time

Time point of measurement

blk

Blocking factor

id

Subject/individual identifier for repeated measures

Target

Mean amplification efficiency of target gene

Ct_Target

Ct values of target gene

Ref1

Mean amplification efficiency of reference gene 1

Ct_Ref1

Ct values of reference gene 1

Ref2

Mean amplification efficiency of reference gene 2

Ct_Ref2

Ct values of reference gene 2

Details

This dataset is suitable for repeated-measures mixed-model analysis and normalization using multiple reference genes.

Source

Not applicable

Sample data in (two-group design with multiple target genes)

Description

A sample qPCR data set with two experimental groups (Control and Treatment) and 18 target genes normalized to one reference gene (GAPDH). Each line belongs to a separate individual (non-repeated measure experiment). This dataset is suitable for two-sample t-test based expression analysis.

Usage

data_ttest18genes

Format

A data frame with 8 observations and 38 variables:

Condition

Experimental group (Control, Treatment)

Rep

Biological replicates

ANGPT1, Ct.ANGPT1

Efficiency and Ct values of ANGPT1 gene

ANGPT2, Ct.ANGPT2

Efficiency and Ct values of ANGPT2 gene

CCL2, Ct.CCL2

Efficiency and Ct values of CCL2 gene

CCL5, Ct.CCL5

Efficiency and Ct values of CCL5 gene

CSF2, Ct.CSF2

Efficiency and Ct values of CSF2 gene

FGF2, Ct.FGF2

Efficiency and Ct values of FGF2 gene

IL1A, Ct.IL1A

Efficiency and Ct values of IL1A gene

IL1B, Ct.IL1B

Efficiency and Ct values of IL1B gene

IL6, Ct.IL6

Efficiency and Ct values of IL6 gene

IL8, Ct.IL8

Efficiency and Ct values of IL8 gene

PDGFA, Ct.PDGFA

Efficiency and Ct values of PDGFA gene

PDGFB, Ct.PDGFB

Efficiency and Ct values of PDGFB gene

TGFA, Ct.TGFA

Efficiency and Ct values of TGFA gene

TGFB, Ct.TGFB

Efficiency and Ct values of TGFB gene

TNF, Ct.TNF

Efficiency and Ct values of TNF gene

VEGFA, Ct.VEGFA

Efficiency and Ct values of VEGFA gene

VEGFB, Ct.VEGFB

Efficiency and Ct values of VEGFB gene

VEGFC, Ct.VEGFC

Efficiency and Ct values of VEGFC gene

GAPDH, Ct.GAPDH

Efficiency and Ct values of GAPDH reference gene

Source

Not applicable

Sample data (with technical replicates)

Description

A sample data for calculating biological replicated. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_withTechRep

Format

A data frame with 18 observations and 9 variables:

Condition

experimental factor

biolrep

biological replicate

techrep

technical replicates

target

Amplification efficiency of target gene

Ct_target

Ct of target gene

ref

Amplification efficiency of reference gene

Ct_ref

Ct of reference gene

Source

Not applicable

Amplification efficiency statistics and standard curves

Description

The efficiency function calculates amplification efficiency (E) and related statistics, including slope and coefficient of determination (R^2), and generates standard curves for qPCR assays.

Usage

efficiency(df, base_size = 12, legend_position = c(0.2, 0.2), ...)

Arguments

Details

Amplification efficiency is estimated from standard curves generated by regressing Ct values against the logarithm of template dilution. For each gene, the function reports the slope of the standard curve, amplification efficiency (E), and R^2 as a measure of goodness of fit. The function also provides graphical visualization of the standard curves.

Value

A list with the following components:

efficiency

A data frame containing slope, amplification efficiency (E), and R^2 statistics for each gene.

Slope_compare

A table comparing slopes between genes.

plot

A ggplot2 object showing standard curves for all genes.

Author(s)

Ghader Mirzaghaderi

Examples

# Load example efficiency data
data <- read.csv(system.file("extdata", "data_efficiency1.csv", package = "rtpcr"))

# Calculate amplification efficiency and generate standard curves
efficiency(data)


ef <- read.csv(system.file("extdata", "data_efficiency_Yuan2006PMCBioinf.csv", package = "rtpcr"))
efficiency(ef)

Internal global variables

Description

These objects are declared to avoid R CMD check notes about non-standard evaluation (e.g., in ggplot2).

Converts a 4-column qPCR long data format to wide format

Description

Converts a 4-column (Condition, gene, Efficiency, Ct) qPCR long data format to wide format

Usage

long_to_wide(x)

Arguments

Details

Converts a 4-column (Condition, gene, Efficiency, Ct) qPCR long data format to wide format

Value

A wide qPCR data frame

Author(s)

Ghader Mirzaghaderi

Examples

df <- read.table(header = TRUE, text = "
Condition	Gene	E	Ct
control	C2H2-26	1.8	31.26
control	C2H2-26	1.8	31.01
control	C2H2-26	1.8	30.97
treatment	C2H2-26	1.8	32.65
treatment	C2H2-26	1.8	32.03
treatment	C2H2-26	1.8	32.4
control	C2H2-01	1.75	31.06
control	C2H2-01	1.75	30.41
control	C2H2-01	1.75	30.97
treatment	C2H2-01	1.75	28.85
treatment	C2H2-01	1.75	28.93
treatment	C2H2-01	1.75	28.9
control	C2H2-12	2	28.5
control	C2H2-12	2	28.4
control	C2H2-12	2	28.8
treatment	C2H2-12	2	27.9
treatment	C2H2-12	2	28
treatment	C2H2-12	2	27.9
control	ref	1.9	28.87
control	ref	1.9	28.42
control	ref	1.9	28.53
treatment	ref	1.9	28.31
treatment	ref	1.9	29.14
treatment	ref	1.9	28.63")

long_to_wide(df)

Computing the mean of technical replicates

Description

Computes the arithmetic mean of technical replicates for each sample or group. This is often performed before ANOVA or other statistical analyses to simplify comparisons between experimental groups.

Usage

meanTech(x, groups, numOfFactors, block)

Arguments

Details

The meanTech function calculates the mean of technical replicates based on one or more grouping columns. This reduces the dataset to a single representative value per group, facilitating downstream analysis such as fold change calculation or ANOVA.

Value

A data frame with the mean of technical replicates for each group.

Author(s)

Ghader Mirzaghaderi

Examples

# Example input data frame with technical replicates
data1 <- read.csv(system.file("extdata", "data_withTechRep.csv", package = "rtpcr"))

# Calculate mean of technical replicates using first four columns as groups
meanTech(data1,
         groups = 1:2,
         numOfFactors = 1,
         block = NULL)

# Another example using different dataset and grouping columns
data2 <- read.csv(system.file("extdata", "data_Lee_etal2020qPCR.csv", package = "rtpcr"))
meanTech(data2, groups = 1:3,
         numOfFactors = 2,
         block = NULL)

Combine multiple ggplot objects into a single layout

Description

The multiplot function arranges multiple ggplot2 objects into a single plotting layout with a specified number of columns.

Usage

multiplot(..., cols = 1)

Arguments

Details

Multiple ggplot2 objects can be provided either as separate arguments via .... The function uses the grid package to control the layout.

Value

A grid object displaying multiple plots arranged in the specified layout.

Author(s)

Pedro J. (adapted from https://gist.github.com/pedroj/ffe89c67282f82c1813d)

Examples

# Example using output from TTEST_DDCt
data1 <- read.csv(system.file("extdata", "data_ttest18genes.csv", package = "rtpcr"))
out <- TTEST_DDCt(
  data1,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1)
  
p1 <- plotFactor(out, 
  x_col = "gene",
  y_col = "log2FC", 
  Lower.se_col = "Lower.se.log2FC",
  Upper.se_col = "Upper.se.log2FC", 
  letters_col = "sig")
  
p2 <- plotFactor(out, 
  x_col = "gene",
  y_col = "RE", 
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE", 
  letters_col = "sig")
  
  

# Example using output from ANOVA_DCt
data2 <- read.csv(system.file("extdata", "data_1factor.csv", package = "rtpcr"))
out2 <- ANOVA_DCt(
  data2,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  block = NULL)
  
df <- out2$relativeExpression

p3 <- plotFactor(
  df,
  x_col = "SA",
  y_col = "RE",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  letters_d = 0.1,
  col_width = 0.7,
  err_width = 0.15,
  fill_colors = "skyblue",
  alpha = 1,
  base_size = 14)

# Combine plots into a single layout
multiplot(p1, p2, cols = 2)

multiplot(p1, p3, cols = 2)

Bar plot of gene expression for 1-, 2-, or 3-factor experiments

Description

Creates a bar plot of relative gene expression (fold change) values from 1-, 2-, or 3-factor experiments, including error bars and statistical significance annotations.

Usage

plotFactor(
  data,
  x_col,
  y_col,
  Lower.se_col,
  Upper.se_col,
  group_col = NULL,
  facet_col = NULL,
  letters_col = NULL,
  letters_d = 0.2,
  col_width = 0.8,
  err_width = 0.15,
  dodge_width = 0.8,
  fill_colors = NULL,
  color = "black",
  alpha = 1,
  base_size = 12,
  legend_position = "right",
  ...
)

Arguments

Value

ggplot2 plot object

Author(s)

Ghader Mirzaghaderi

Examples

data <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))

res <- ANOVA_DDCt(x = data,
  numOfFactors = 2,
  numberOfrefGenes = 2,
  block = "block",
  mainFactor.column = 2,
  p.adj = "none")

df <- res$relativeExpression

p1 <- plotFactor(
  data = df,
  x_col = "contrast",
  y_col = "RE",
  group_col = "gene",
  facet_col = "gene",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  letters_d = 0.2,
  alpha = 1,
  col_width = 0.7,
  dodge_width = 0.7,
  base_size = 14, 
  legend_position = "none")

p1


data2 <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
#Perform analysis first
res <- ANOVA_DCt(
  data2,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)
  
df <- res$relativeExpression
# Generate three-factor bar plot
 p <- plotFactor(
  df,
  x_col = "SA",       
  y_col = "log2FC",       
  group_col = "Type",   
  facet_col = "Conc",    
  Lower.se_col = "Lower.se.log2FC",
  Upper.se_col = "Upper.se.log2FC",
  letters_col = "sig",
  letters_d = 0.3,
  col_width = 0.7, 
  dodge_width = 0.7,
  #fill_colors = c("blue", "brown"),
  color = "black",
  base_size = 14, 
  alpha = 1,
  legend_position = c(0.1, 0.2))
p