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Type:

Package

Title:

qPCR Data Analysis

Version:

2.0.5

Description:

Various methods are employed for statistical analysis and graphical presentation of real-time PCR (quantitative PCR or qPCR) data. 'rtpcr' handles amplification efficiency calculation, statistical analysis and graphical representation of real-time PCR data based on up to two reference genes. By accounting for amplification efficiency values, 'rtpcr' was developed using a general calculation method described by Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods. Based on the experimental conditions, the functions of the 'rtpcr' package use t-test (for experiments with a two-level factor), analysis of variance (ANOVA), analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, Delta Delta Ct method) or relative expression (RE, Delta Ct method). The functions further provide standard errors and confidence intervals for means, apply statistical mean comparisons and present significance. To facilitate function application, different data sets were used as examples and the outputs were explained. ‘rtpcr’ package also provides bar plots using various controlling arguments. The 'rtpcr' package is user-friendly and easy to work with and provides an applicable resource for analyzing real-time PCR data.

URL:

https://github.com/mirzaghaderi/rtpcr

License:

GPL-3

Imports:

multcomp, multcompView, ggplot2, lmerTest, purrr, reshape2, tidyr, dplyr, grid, emmeans

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.3

NeedsCompilation:

Packaged:

2025-12-12 11:43:32 UTC; GM

Depends:

R (≥ 3.5.0)

Suggests:

knitr, rmarkdown

VignetteBuilder:

knitr

Author:

Ghader Mirzaghaderi [aut, cre, cph]

Maintainer:

Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>

Repository:

CRAN

Date/Publication:

2025-12-12 12:10:02 UTC

Relative expression (`\Delta Ct` method) analysis using ANOVA

Description

Analysis of variance of relative expression (\Delta Ct method) values for all factor level combinations in which the expression level of a reference gene is used as normalizer.

Usage

ANOVA_DCt(x, numberOfrefGenes, block, alpha = 0.05, adjust = "none")

Arguments

x

a data.frame structured as described in the vignette consisting of condition columns, target gene efficiency (E), target Gene Ct, reference gene efficiency and reference gene Ct values, respectively. Each Ct in the data frame is the mean of technical replicates. Complete amplification efficiencies of 2 was assumed in the example data for all wells but the calculated efficienies can be used instead. NOTE: Each line belongs to a separate individual reflecting a non-repeated measure experiment). See vignette, section "data structure and column arrangement" for details.

numberOfrefGenes

number of reference genes (1 or 2). Up to two reference genes can be handled.

block

A string or NULL. If provided, this should be the name of the column in x that indicates the blocking factor. When qPCR is performed on different plates, variations between plates can introduce noise that obscures the true biological differences in gene expression. By using a blocking factor, each treatment and control group can be replicated across different plates, ensuring that at least one replicate of each condition is present on every plate.

alpha

significance level for cld (default 0.05)

adjust

p-value adjustment method passed to emmeans/cld

Details

The ANOVA_DCt function performs analysis of variance (ANOVA) of relative expression (RE) values for all factor level combinations using the expression level of reference gene(s) as a normalizer.

Value

A list with 4 elements:

Final_data: The row data plus weighed delta Ct (wDCt) values.
lm: The output of linear model analysis including ANOVA tables
ANOVA: ANOVA table based on CRD
Result: The result table including treatments and factors, RE (Relative Expression), LCL, UCL, letter display for pair-wise comparisons and standard error with the lower and upper limits.

Author(s)

Ghader Mirzaghaderi

References

Livak, Kenneth J, and Thomas D Schmittgen. 2001. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods 25 (4). 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.

Yuan, Joshua S, Ann Reed, Feng Chen, and Neal Stewart. 2006. Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics 7 (85). doi:10.1186/1471-2105-7-85.

Examples


# If the data include technical replicates, means of technical replicates
# should be calculated first using meanTech function.
# Applying ANOVA
ANOVA_DCt(data_3factor, numberOfrefGenes = 1, block = NULL)


ANOVA_DCt(data_2factorBlock, block = "Block", numberOfrefGenes = 1)

Relative expression (`\Delta \Delta Ct` method) analysis using ANOVA and ANCOVA

Description

Relative expression analysis using \Delta \Delta Ct method can be done by ANOVA (analysis of variance) and ANCOVA (analysis of covariance) through the ANOVA_DDCt function, for uni- or multi-factorial experiment data. The bar plot of the relative expression (RE) values along with the standard error (se) and confidence interval (ci) is returned by the ANOVA_DDCt function.

Usage

ANOVA_DDCt(
  x,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  analysisType = "anova",
  mainFactor.level.order = NULL,
  block = NULL,
  x.axis.labels.rename = "none",
  p.adj = "none",
  plot = TRUE,
  plotType = "RE"
)

Arguments

x

a data frame of condition(s), biological replicates, efficiency (E) and Ct values of target and reference genes. Each Ct value in the data frame is the mean of technical replicates. NOTE: Each line belongs to a separate individual reflecting a non-repeated measure experiment). See vignette, section "data structure and column arrangement" for details.

numberOfrefGenes

number of reference genes which is 1 or 2 (up to two reference genes can be handled).

mainFactor.column

the factor for which relative expression is calculated for its levels. If ancova is selected as analysisType, the remaining factors (if any) are considered as covariate(s).

analysisType

should be one of "ancova" or "anova". Default is "anova".

mainFactor.level.order

NULL (default) or a vector of main factor level names. If NULL, the first level of the mainFactor.column is used as calibrator. If a vector of main factor levels (in any order) is specified, the first level in the vector is used as calibrator. Calibrator is the reference level or sample that all others are compared to. Examples are untreated of time 0. The RE value of the reference or calibrator level is 1 because it is not changed compared to itself.

block

column name of the block if there is a blocking factor (for correct column arrangement see example data.). When a qPCR experiment is done in multiple qPCR plates, variation resulting from the plates may interfere with the actual amount of gene expression. One solution is to conduct each plate as a complete randomized block so that at least one replicate of each treatment and control is present on a plate. Block effect is usually considered as random and its interaction with any main effect is not considered.

x.axis.labels.rename

a vector replacing the x axis labels in the bar plot

p.adj

Method for adjusting p values

plot

if FALSE, prevents the plot.

plotType

Plot based on "RE" (relative expression) or "log2FC" (log2 fold change).

Details

ANOVA (analysis of variance) and ANCOVA (analysis of covariance) analysis of relative expression using \Delta \Delta Ct method can be done using ANOVA_DDCt function, for uni- or multi-factorial experiment data. If there are more than one factor, RE value calculations for the 'mainFactor.column' and the statistical analysis is performed based on a full model factorial experiment by default. However, if 'ancova' is defined for the 'analysisType' argument, RE values are calculated for the levels of the 'mainFactor.column' and the other factors are used as covariate(s) in the analysis of variance, but we should consider ANCOVA table: if the interaction between the main factor and covariate is significant, ANCOVA is not appropriate in this case. 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 using ANOVA_DDCt. ANOVA_DDCt function performs RE analysis even there is only one factor (without covariate or factor variable). Bar plot of relative expression (RE) values along with the standard errors are also returned by the ANOVA_DDCt function.

Value

A list with 7 elements:

Final_data: Input data frame plus the weighted Delat Ct values (wDCt)
lm_ANOVA: lm of factorial analysis-tyle
lm_ANCOVA: lm of ANCOVA analysis-type
ANOVA_table: ANOVA table
ANCOVA_table: ANCOVA table
RE Table: Table of RE (relative expression) values, log2FC (log2 fold change) values, significance and confidence interval and standard error with the lower and upper limits for the main factor levels.
Bar plot of RE values: Bar plot of the relative expression values for the main factor levels.

Author(s)

Ghader Mirzaghaderi

References

Livak, Kenneth J, and Thomas D Schmittgen. 2001. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods 25 (4). 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.

Yuan, Joshua S, Ann Reed, Feng Chen, and Neal Stewart. 2006. Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics 7 (85). doi:10.1186/1471-2105-7-85.

Examples



ANOVA_DDCt(data_1factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL)


ANOVA_DDCt(data_2factor, 
numberOfrefGenes = 1, 
mainFactor.column = 2, block = NULL, 
analysisType = "ancova")


# Data from Lee et al., 2020, Here, the data set contains technical 
# replicates so we get mean of technical replicates first:
df <- meanTech(Lee_etal2020qPCR, groups = 1:3)
ANOVA_DDCt(df, numberOfrefGenes = 1, analysisType = "ancova", block = NULL, 
mainFactor.column = 2, plotType = "log2FC")


ANOVA_DDCt(data_2factorBlock,  
    numberOfrefGenes = 1, 
    mainFactor.column = 1, 
    mainFactor.level.order = c("S", "R"), 
    block = "block", 
    analysisType = "ancova")



df <- meanTech(Lee_etal2020qPCR, groups = 1:3) 
df2 <- df[df$factor1 == "DSWHi",][-1]
ANOVA_DDCt(df2,
mainFactor.column = 1,
block = NULL, 
numberOfrefGenes = 1, 
analysisType = "anova")


addline_format <- function(x,...){gsub('\\s','\n',x)}
ANOVA_DDCt(data_1factor, numberOfrefGenes = 1, 
mainFactor.column = 1,
block = NULL, 
x.axis.labels.rename = addline_format(c("Control", 
                                        "Treatment_1 vs Control", 
                                        "Treatment_2 vs Control")))

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

Lee_etal2020qPCR

Format

A data frame with 72 observations and 8 variables:

factor1: experimental factor
DS: DS
biolRep: biological replicate
techRep: technical replicates
E_APOE: Amplification efficiency of APOE gene
Ct_APOE: Ct of APOE gene
E_GAPDH: Amplification efficiency of GAPDH gene
Ct_GAPDH: Ct of GAPDH gene

Source

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

`\Delta \Delta C_T` analysis analysis using a model

Description

Relative expression (\Delta \Delta C_T method) analysis using a model object produced by the ANOVA_DDCt or REPEATED_DDCt.

Usage

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

Arguments

model

an 'lmer' fitted model object created by ANOVA_DDCt or REPEATED_DDCt functions

specs

A character vector specifying the names of the predictors over which FC values are desired

p.adj

Method for adjusting p values

Details

The Means_DDCt function performs fold change (\Delta \Delta C_T method) analysis using a model produced by the ANOVA_DDCt or REPEATED_DDCt. The values can be returned for any effects in the model including simple effects, interactions and slicing if an ANOVA model is used, but ANCOVA models returned by rtpcr package only include simple effects.

Value

Table of FC values, significance and confidence interval.

Author(s)

Ghader Mirzaghaderi

Examples


# Returning fold change values from a fitted model.
# Firstly, result of `ANOVA_DDCt` or `REPEATED_DDCt` is 
# acquired which includes a model object:
res <- ANOVA_DDCt(data_3factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL)

# Returning fold change values of Type levels from a fitted model:
Means_DDCt(res$lm_ANOVA, specs = "Type")

# Returning fold change values of Conc levels from a fitted model:
Means_DDCt(res$lm_ANOVA, specs = "Conc")

# Returning fold change values of Conc levels sliced by Type:
Means_DDCt(res$lm_ANOVA, specs = "Conc | Type")

# Returning fold change values of Conc levels sliced by Type*SA:
Means_DDCt(res$lm_ANOVA, specs = "Conc | (Type*SA)")

Fold change (`\Delta \Delta C_T` method) analysis of repeated measure qPCR data

Description

REPEATED_DDCt function performs fold change (\Delta \Delta C_T method) analysis of observations repeatedly taken over different time courses. Data may be obtained over time from a uni- or multi-factorial experiment. The bar plot of the fold changes (FC) values along with the standard error (se) or confidence interval (ci) is also returned by the REPEATED_DDCt function.

Usage

REPEATED_DDCt(
  x,
  numberOfrefGenes,
  factor,
  block,
  x.axis.labels.rename = "none",
  p.adj = "none",
  plot = TRUE,
  plotType = "RE"
)

Arguments

x

input data frame in which the first column is id, followed by the factor column(s) which include at least time. The first level of time in data frame is used as calibrator or reference level. Additional factor(s) may also be present. Other columns are efficiency and Ct values of target and reference genes. NOTE: In the "id" column, a unique number is assigned to each individual from which samples have been taken over time, for example see data_repeated_measure_1, all the three number 1 indicate one individual which has been sampled over three different time courses. See vignette, section "data structure and column arrangement" for details.

numberOfrefGenes

number of reference genes which is 1 or 2 (Up to two reference genes can be handled). as reference or calibrator which is the reference level or sample that all others are compared to. Examples are untreated of time 0. The FC value of the reference or calibrator level is 1 because it is not changed compared to itself. If NULL, the first level of the main factor column is used as calibrator.

factor

the factor for which the FC values is analysed. The first level of the specified factor in the input data frame is used as calibrator.

block

column name of the block if there is a blocking factor (for correct column arrangement see example data.). Block effect is usually considered as random and its interaction with any main effect is not considered.

x.axis.labels.rename

a vector replacing the x axis labels in the bar plot

p.adj

Method for adjusting p values

plot

if FALSE, prevents the plot.

plotType

Plot based on "RE" (relative expression) or "log2FC" (log2 fold change).

Details

The REPEATED_DDCt function performs fold change (FC) analysis of observations repeatedly taken over time. The intended factor (could be time or any other factor) is defined for the analysis by the factor argument, then the function performs FC analysis on its levels so that the first levels (as appears in the input data frame) is used as reference or calibrator. The function returns FC values along with confidence interval and standard error for the FC values.

Value

A list with 5 elements:

Final_data: Input data frame plus the weighted Delat Ct values (wDCt)
lm: lm of factorial analysis-tyle
ANOVA_table: ANOVA table
FC Table: Table of FC values, significance, confidence interval and standard error with the lower and upper limits for the selected factor levels.
Bar plot of FC values: Bar plot of the fold change values for the main factor levels.

Author(s)

Ghader Mirzaghaderi

Examples


REPEATED_DDCt(data_repeated_measure_1,
            numberOfrefGenes = 1,
            factor = "time", block = NULL)

REPEATED_DDCt(data_repeated_measure_2,
             numberOfrefGenes = 1,
             factor = "time", block = NULL)

Expression (`\Delta \Delta C_T` method) analysis of target genes using t-test

Description

t.test based analysis of the fold change expression for any number of target genes.

Usage

TTEST_DDCt(
  x,
  numberOfrefGenes,
  paired = FALSE,
  var.equal = TRUE,
  p.adj = "BH",
  order = "none",
  plotType = "RE"
)

Arguments

x

a data frame of 4 columns including Conditions, E (efficiency), Gene and Ct values (see examples below). Biological replicates needs to be equal for all Genes. Each Ct value is the mean of technical replicates. Complete amplification efficiencies of 2 is assumed here for all wells but the calculated efficienies can be used instead. See vignette for details about "data structure and column arrangement".

numberOfrefGenes

number of reference genes. Up to two reference genes can be handled.

paired

a logical indicating whether you want a paired t-test.

var.equal

a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

p.adj

Method ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") for adjusting p values.

order

a vector determining genes order on the output graph.

plotType

Plot based on "RE" (relative expression) or "log2FC" (log2 fold change).

Details

The TTEST_DDCt function applies a t.test based analysis to calculate fold change (\Delta \Delta C_T method) expression and returns related statistics for any number of target genes that have been evaluated under control and treatment conditions. This function also returns the expression bar plot based on fold change or log2 fold change. Sampling may be paired or unpaired. One or two reference genes can be used. Unpaired and paired samples are commonly analyzed using unpaired and paired t-test, respectively. NOTE: Paired samples in quantitative PCR refer to two sample data that are collected from one set of individuals at two different conditions, for example before and after a treatment or at two different time points. While for unpaired samples, two sets of individuals are used: one under untreated and the other set under treated condition. Paired samples allow to compare gene expression changes within the same individual, reducing inter-individual variability.

Value

A list of two elements:

Result: Output table including the Fold Change values, lower and upper confidence interval, pvalue and standard error with the lower and upper limits.

For more information about the test procedure and its arguments, refer t.test, and lm. If the residuals of the model do not follow normal distribution and variances between the two groups are not homoGene, wilcox.test procedure may be concidered

Author(s)

Ghader Mirzaghaderi

References

Livak, Kenneth J, and Thomas D Schmittgen. 2001. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods 25 (4). 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.

Yuan, Joshua S, Ann Reed, Feng Chen, and Neal Stewart. 2006. Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics 7 (85). doi:10.1186/1471-2105-7-85.

Examples


# See the sample data structure
data_ttest

# Getting t.test results
TTEST_DDCt(data_ttest,
   paired = FALSE,
   var.equal = TRUE,
   numberOfrefGenes = 1)



TTEST_DDCt(Taylor_etal2019, 
          numberOfrefGenes = 2, 
          var.equal = TRUE,
          p.adj = "BH")

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

Taylor_etal2019

Format

A data frame with 18 observations and 4 variables:

Condition: Experimental conditions
Gene: Genes
E: Amplification efficiency
Ct: Ct values

Source

Not applicable

Sample data (one factor three levels)

Description

A sample dataset for demonstration purposes. 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
E_PO: Mean amplification efficiency of PO gene
Ct_PO: Ct values of PO gene. Each is the mean of technical replicates
E_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 (two factor)

Description

A sample dataset for demonstration purposes. 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
E_PO: Mean amplification efficiency of PO gene
Ct_PO: Ct values of PO gene. Each is the mean of technical replicates
E_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 (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:

factor1: First experimental factor
factor2: Second experimental factor
block: Second experimental factor
Rep: Biological replicates
E_PO: Mean amplification efficiency of PO gene
Ct_PO: Ct values of PO gene. Each is the mean of technical replicates
E_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 (three factor)

Description

A sample dataset for demonstration purposes. 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
E_PO: Mean amplification efficiency of PO gene
Ct_PO: Ct values of PO gene. Each is the mean of technical replicates
E_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: amplification efficiency

Description

A sample qPCR dataset for demonstrating efficiency calculation.

Usage

data_efficiency

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

Where the data comes from (if applicable)

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:

id: experimental factor
time: time course levels
E_target: Amplification efficiency of target gene
Ct_target: Ct of target gene
E_ref: Amplification efficiency of reference gene
Ct_ref: Ct of reference gene

Source

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:

id: experimental factor
treatment: treatment
time: time course levels
E_target: Amplification efficiency of target gene
Ct_target: Ct of target gene
E_ref: Amplification efficiency of reference gene
Ct_ref: Ct of reference gene

Source

Sample qPCR data from an experiment conducted under two different conditions

Description

Sample qPCR data from an experiment conducted under two different conditions.

Usage

data_ttest

Format

A data frame with 24 observations and 4 variables:

Condition: Experimental conditions
Gene: Genes
E: Amplification efficiency
Ct: Ct values

Source

University of Kurdistan

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:

factor1: experimental factor
factor2: experimental factor
factor3: experimental factor
biolrep: biological replicate
techrep: technical replicates
E_target: Amplification efficiency of target gene
Ct_target: Ct of target gene
E_ref: Amplification efficiency of reference gene
Ct_ref: Ct of reference gene

Source

Not applicable

Slope, R2 and Efficiency (E) statistics and standard curves

Description

The efficiency function calculates amplification efficiency and returns related statistics and standard curves.

Usage

efficiency(df)

Arguments

df

a data frame of dilutions and Ct of genes. First column is dilutions and other columns are Ct values for different genes.

Details

The efficiency function calculates amplification efficiency of genes, and present the Slope, Efficiency, and R2 statistics.

Value

A list 3 elements.

efficiency: Slope, R2 and Efficiency (E) statistics
Slope_compare: slope comparison table
plot: standard curves

Author(s)

Ghader Mirzaghaderi

Examples



# locate and read the sample data
data_efficiency

# Applying the efficiency function
efficiency(data_efficiency)

Internal global variables

Description

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

Calculating mean of technical replicates

Description

Calculating arithmetic mean of technical replicates for subsequent ANOVA analysis

Usage

meanTech(x, groups)

Arguments

x

A raw data frame including technical replicates.

groups

grouping columns based on which the mean technical replicates are calculated.

Details

The meanTech calculates mean of technical replicates. Arithmetic mean of technical replicates can be calculated in order to simplify the statistical comparison between sample groups.

Value

A data frame with the mean of technical replicates.

Author(s)

Ghader Mirzaghaderi

Examples


# See example input data frame:
data_withTechRep

# Calculating mean of technical replicates
meanTech(data_withTechRep, groups = 1:4)

# Calculating mean of technical replicates
meanTech(Lee_etal2020qPCR, groups = 1:3)

Multiple plot function

Description

multiplot function combines multiple ggplot objects into a single plate.

Usage

multiplot(..., cols = 1)

Arguments

...

ggplot objects can be passed in ... or to plotlist (as a list of ggplot objects)

cols

Number of columns in the panel

Details

Combining multiple ggplot objects into a single plate.

Value

A multiple-plots plate

Author(s)

gist.github.com/pedroj/ffe89c67282f82c1813d

Examples


a <- TTEST_DDCt(data_ttest, 
        numberOfrefGenes = 1)
p1 <- a$plot

out2 <- ANOVA_DCt(data_1factor, numberOfrefGenes = 1, block = NULL)$Result
p2 <- plotOneFactor(out2,
        x_col= 1,
        y_col= 2,
        Lower.se_col = 7,
        Upper.se_col = 8,
        letters_col = 11,
        show.groupingLetters = TRUE)
                    
multiplot(p1, p2, cols=2)

multiplot(p1, p2, cols=1)

Bar plot of gene expression from an expression table of a single-factor experiment data

Description

Bar plot of the relative expression of a gene along with the standard error (se), 95% confidence interval (ci) and significance.

Usage

plotOneFactor(
  data,
  x_col,
  y_col,
  Lower.se_col,
  Upper.se_col,
  letters_col = NULL,
  show.groupingLetters = TRUE
)

Arguments

data

A data.frame such as the expression result of ANOVA_DDCt(x) or ANOVA_DCt(x), etc. functions.

x_col

The column number of the data.frame used for x axis.

y_col

The column number of the data.frame used for y axis.

Lower.se_col

The column number of the data.frame used for the lower error bar.

Upper.se_col

The column number of the data.frame used for the upper error bar.

letters_col

The column number of the data.frame used as the result of statistical comparing and grouping.

show.groupingLetters

a logical variable. If TRUE, mean grouping letters (the results of statistical comparison) are added to the bars.

Details

The plotOneFactor function generates the bar plot of the fold change for target genes along with the significance and the 95% confidence interval as error bars.

Value

Bar plot of the average fold change for target genes along with the significance and the standard error or 95% confidence interval as error bars.

Author(s)

Ghader Mirzaghaderi

Examples


# Before plotting, the result needs to be extracted as below:
res <- ANOVA_DCt(data_1factor, numberOfrefGenes = 1, block = NULL)$Result

# Bar plot
plotOneFactor(res, 1, 2, 7, 8, 11,
    show.groupingLetters = TRUE)

Bar plot of gene expression from an expression table of a three-factorial experiment data

Description

Bar plot of the relative expression (\Delta C_T method) of a gene along with the confidence interval and significance

Usage

plotThreeFactor(
  data,
  x_col,
  y_col,
  group_col,
  facet_col,
  Lower.se_col,
  Upper.se_col,
  letters_col = NULL,
  show.groupingLetters = TRUE
)

Arguments

data

A data.frame such as the expression result of ANOVA_DDCt(x) or ANOVA_DCt(x), etc. functions.

x_col

column number of the x-axis factor.

y_col

column number of the y-axis factor.

group_col

column number of the grouping factor.

facet_col

column number of the faceting factor.

Lower.se_col

The column number of the data.frame used for the lower error bar.

Upper.se_col

The column number of the data.frame used for the upper error bar.

letters_col

The column number of the data.frame used as the result of statistical comparing and grouping.

show.groupingLetters

a logical variable. If TRUE, mean grouping letters (the results of statistical comparison) are added to the bars.

Details

The plotThreeFactor function generates the bar plot of the average fold change for target genes along with the significance, standard error (se) and the 95% confidence interval (ci).

Value

Bar plot of the average fold change for target genes along with the standard error or 95% confidence interval as error bars.

Author(s)

Ghader Mirzaghaderi

Examples




res <- ANOVA_DCt(data_3factor, 
      numberOfrefGenes = 1, 
      block = NULL)
      
data <- res$Results

plotThreeFactor(data, 
                3,   # x-axis factor
                5,   # bar height
                1,   # fill groups
                2,   # facet grid
                11,   # lower SE column
                12,   # upper SE column
                letters_col = 13,
                show.groupingLetters = TRUE)

Bar plot of gene expression from an expression table of a two-factorial experiment data

Description

Bar plot of the relative expression (\Delta C_T method) of a gene along with the standard error (se), 95% confidence interval (ci) and significance

Usage

plotTwoFactor(
  data,
  x_col,
  y_col,
  group_col,
  Lower.se_col,
  Upper.se_col,
  letters_col = NULL,
  show.groupingLetters = TRUE
)

Arguments

data

A data.frame such as the expression result of ANOVA_DDCt(x) or ANOVA_DCt(x), etc. functions.

x_col

column number of the x-axis factor.

y_col

column number of the y-axis factor.

group_col

column number of the grouping factor.

Lower.se_col

The column number of the data.frame used for the lower error bar.

Upper.se_col

The column number of the data.frame used for the upper error bar.

letters_col

The column number of the data.frame used as the result of statistical comparing and grouping.

show.groupingLetters

a logical variable. If TRUE, mean grouping letters (the results of statistical comparison) are added to the bars.

Details

The plotTwoFactor function generates the bar plot of the average fold change for target genes along with the significance, standard error (se) and the 95% confidence interval (ci) as error bars.

Value

Bar plot of the average fold change for target genes along with the standard error or 95% confidence interval as error bars.

Author(s)

Ghader Mirzaghaderi

Examples


a <- ANOVA_DCt(data_2factorBlock, block = "Block", numberOfrefGenes = 1)
data <- a$Results

plotTwoFactor(
  data = data,
  x_col = 2,
  y_col = 3,
  group_col = 1,
  Lower.se_col = 8,
  Upper.se_col = 9,
  letters_col = 12,
  show.groupingLetters = TRUE)
              
              
              
# Combining FC results of two different genes:
a <- REPEATED_DDCt(data_repeated_measure_1,
                   numberOfrefGenes = 1,
                   factor = "time", block = NULL, plot = FALSE)

b <- REPEATED_DDCt(data_repeated_measure_2,
                   factor = "time",
                   numberOfrefGenes = 1, block = NULL, plot = FALSE)

a1 <- a$FC_statistics_of_the_main_factor
b1 <- b$FC_statistics_of_the_main_factor
c <- rbind(a1, b1)
c$gene <- factor(c(1,1,1,2,2,2))
c

plotTwoFactor(
  data = c,
  x_col = 1,
  y_col = 2,
  group_col = 13,
  Lower.se_col = 9,
  Upper.se_col = 10,
  letters_col = 5,
  show.groupingLetters = TRUE)

Relative expression (\Delta Ct method) analysis using ANOVA

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Relative expression (\Delta \Delta Ct method) analysis using ANOVA and ANCOVA

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Sample data (with technical replicates)

Description

Usage

Format

Source

\Delta \Delta C_T analysis analysis using a model

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Fold change (\Delta \Delta C_T method) analysis of repeated measure qPCR data

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Expression (\Delta \Delta C_T method) analysis of target genes using t-test

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Format

Source

Sample data (one factor three levels)

Description

Usage

Format

Source

Sample data (two factor)

Description

Usage

Format

Source

Sample data (two factor with blocking factor)

Description

Usage

Format

Source

Sample data (three factor)

Description

Usage

Format

Source

Sample qPCR data: amplification efficiency

Description

Usage

Format

Source

Repeated measure sample data

Description

Relative expression (`\Delta Ct` method) analysis using ANOVA

Relative expression (`\Delta \Delta Ct` method) analysis using ANOVA and ANCOVA

`\Delta \Delta C_T` analysis analysis using a model

Fold change (`\Delta \Delta C_T` method) analysis of repeated measure qPCR data

Expression (`\Delta \Delta C_T` method) analysis of target genes using t-test