Help for package pcr

Version:

1.2.2

Title:

Analyzing Real-Time Quantitative PCR Data

Description:

Calculates the amplification efficiency and curves from real-time quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative expression from PCR data using the double delta CT and the standard curve methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for statistical significance using two-group tests and linear regression Yuan et al. (2006) <doi:10.1186/1471-2105-7-85>.

Maintainer:

Mahmoud Ahmed <mahmoud.s.fahmy@students.kasralainy.edu.eg>

URL:

https://github.com/MahShaaban/pcr

BugReports:

https://github.com/MahShaaban/pcr/issues

Depends:

R (≥ 3.4.0)

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.0.2

Imports:

ggplot2

Suggests:

testthat, knitr, rmarkdown, covr, cowplot

VignetteBuilder:

knitr

License:

GPL-3

NeedsCompilation:

Packaged:

2020-04-01 05:30:14 UTC; rstudio

Author:

Mahmoud Ahmed

[aut, cre]

Repository:

CRAN

Date/Publication:

2020-04-01 06:10:02 UTC

Calculate the amounts

Description

Calculate the amounts

Usage

.pcr_amount(vec, a, b)

Arguments

vec

A vector of numerics

a

A numeric

b

A numeric

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
pcr:::.pcr_amount(vec, 1, 1)

Get vector average by a variable

Description

Get vector average by a variable

Usage

.pcr_average(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c('group1', 'group2'), 3)
pcr:::.pcr_average(vec, var)

Get vector coefficient of variance by a variable

Description

Get vector coefficient of variance by a variable

Usage

.pcr_cv(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c('group1', 'group2'), 3)
pcr:::.pcr_cv(vec, var)

Propage two vectors

Description

Propage two vectors

Usage

.pcr_error(vec, ref)

Arguments

vec

A vector of numerics

ref

A numeric vector

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
ref <- rnorm(6, 30, .1)
pcr:::.pcr_error(vec, ref)

Calculate the intercept of a line

Description

Calculate the intercept of a line

Usage

.pcr_intercept(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A numeric

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c(.1, .5), 3)
pcr:::.pcr_intercept(vec, var)

Normalize vector by another

Description

Normalize vector by another

Usage

.pcr_normalize(vec, ref, mode = "subtract")

Arguments

vec

A vector of numerics

ref

A numeric vector

mode

Either 'subtract' or 'divide'

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
ref <- rnorm(6, 30, .1)
pcr:::.pcr_normalize(vec, ref)
pcr:::.pcr_normalize(vec, ref, mode = 'divide')

Plotting function

Description

Plotting function

Usage

.pcr_plot_analyze(df, method, facets = FALSE)

Arguments

df

A data.frame such as this returned by pcr_analyze

method

A character string. Possible input includes 'delta_delta_ct', 'delta_ct' or 'relative_curve'

facets

A logical of whether or not to use facets when applicable

Value

A ggplot object. A bar graph of caculated ge

Examples

## locate and read raw ct data
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)

# calculate all delta_delta_ct model
df <- pcr_ddct(ct1,
               group_var = group_var,
               reference_gene = 'GAPDH',
               reference_group = 'brain')

# make a plot
pcr:::.pcr_plot_analyze(df, method = 'delta_delta_ct')

# make a data.frame of two identical columns
pcr_hk <- data.frame(
  GAPDH1 = ct1$GAPDH,
  GAPDH2 = ct1$GAPDH
  )

# calculate delta_ct model
df <- pcr_dct(pcr_hk,
              group_var = group_var,
              reference_group = 'brain')

# make a plot
pcr:::.pcr_plot_analyze(df, method = 'delta_ct')
pcr:::.pcr_plot_analyze(df, method = 'delta_ct', facet = TRUE)

# calculate curve
# locate and read data
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

standard_curve <- pcr_assess(ct3,
                             amount = amount,
                             method = 'standard_curve')
intercept <- standard_curve$intercept
slope <- standard_curve$slope

# calculate the rellative_curve model
df <- pcr_curve(ct1,
                group_var = group_var,
                reference_gene = 'GAPDH',
                reference_group = 'brain',
                intercept = intercept,
                slope = slope)

# make a plot
pcr:::.pcr_plot_analyze(df, method = 'relative_curve')

Plot quality assessment graphs

Description

Plot quality assessment graphs

Usage

.pcr_plot_assess(df, amount, reference_gene, method)

Arguments

df

A data.frame

amount

A numeric vector

reference_gene

A character string

method

A character string; 'efficiency' or 'standard_curve'

Value

A plot

Examples

# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make amount/dilution variable
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# plot the standard curves
pcr:::.pcr_plot_assess(ct3,
                amount = amount,
                reference_gene = 'GAPDH',
                method = 'standard_curve')

# plot amplification efficiency
pcr:::.pcr_plot_assess(ct3,
                       amount = amount,
                       reference_gene = 'GAPDH',
                       method = 'efficiency')

Raise two to a vector power

Description

Raise two to a vector power

Usage

.pcr_relative(vec)

Arguments

vec

A vector of numerics

Value

A numeric

Examples

vec <- rnorm(6, 30, 1)
pcr:::.pcr_relative(vec)

Calculate R squared

Description

Calculate R squared

Usage

.pcr_rsquared(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A numeric

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c(.1, .5), 3)
pcr:::.pcr_rsquared(vec, var)

Get vector standard deviation by a variable

Description

Get vector standard deviation by a variable

Usage

.pcr_sd(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A vector of numerics

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c('group1', 'group2'), 3)
pcr:::.pcr_sd(vec, var)

Calculate the slope of a line

Description

Calculate the slope of a line

Usage

.pcr_slope(vec, var)

Arguments

vec

A vector of numerics

var

A grouping variable

Value

A numeric

Examples

vec <- rnorm(6, 30, 1)
var <- rep(c(.1, .5), 3)
pcr:::.pcr_slope(vec, var)

`C_T` values from qPCR (separate tubes)

Description

A dataset containing the C_T values of two genes from a qPCR experiment. Samples were prepared from human tissues; Brain and kidney (n = 6) each. Primers for each genes were run in separate reaction tubes.

Usage

ct1

Format

A data.frame with 12 rows and 2 variables:

c_myc: C_T values of the target gene c-myc
GAPDH: C_T values of the control gene GAPDH

Source

http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf

`C_T` values from qPCR (same tubes)

Description

A dataset containing the C_T values of two genes from a qPCR experiment. Samples were prepared from human tissues; Brain and kidney (n = 6) each. Primers for both genes were run in the same tubes with different reporting dyes.

Usage

ct2

Format

A data.frame with 12 rows and 2 variables:

c_myc: C_T values of the target gene c-myc
GAPDH: C_T values of the control gene GAPDH

Source

http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf

`C_T` values from qPCR (Serial dilutions)

Description

A dataset containing the C_T values of two genes from a serial dilution qPCR experiment. The original dataset shows only the averages and standard deviations of each of the 7 different dilutions (1, .5, .2, .1, .05, .02 and .01). These summaries were used to regenerate 3 replicates for each of the dilutions to be used in testing and examples of the different functions.

Usage

ct3

Format

A data.frame with 21 rows and 2 variables:

c_myc: C_T values of the target gene c-myc
GAPDH: C_T values of the control gene GAPDH

Source

http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf

`C_T` values from qPCR (Serial dilutions)

Description

A dataset containing the C_T values of two genes from a controlled serial dilution qPCR experiment. The data were prepared from four different dilutions (10, 2, 0.4 and 0.08) and two control groups; control and treatment (n = 12) each.

Usage

ct4

Format

A data.frame with 24 rows and 2 variables:

ref: C_T values of the reference gene
target: C_T values of the target gene

Source

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1395339/

`pcr` package

Description

Analyzing real-time quantitative PCR data

Details

Apply qPCR analysis methods

Description

A unified interface to invoke different analysis methods of qPCR data.

Usage

pcr_analyze(df, method = "delta_delta_ct", ...)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

method

A character string; 'delta_delta_ct' default, 'delta_ct' or 'relative_curve' for invoking a certain analysis model

...

Arguments passed to the methods

Details

The different analysis methods can be invoked using the argument method with 'delta_delta_ct' default, 'delta_ct' or 'relative_curve' for the double delta C_T, delta ct or the standard curve model respectively. Alternatively, the same methods can be applied by using the corresponding functions directly: pcr_ddct, pcr_dct or pcr_curve

Value

A data.frame by default, when plot is TRUE returns a plot. For details; pcr_ddct, pcr_dct and pcr_curve.

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). ELSEVIER. doi:10.1006/meth.2001.1262.

Examples

# applying the delta delta ct method
## locate and read raw ct data
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)

# calculate all values and errors in one step
pcr_analyze(ct1,
            group_var = group_var,
            reference_gene = 'GAPDH',
            reference_group = 'brain',
            method = 'delta_delta_ct')

# return a plot
pcr_analyze(ct1,
            group_var = group_var,
            reference_gene = 'GAPDH',
            reference_group = 'brain',
            method = 'delta_delta_ct',
            plot = TRUE)

# applying the delta ct method
# make a data.frame of two identical columns
pcr_hk <- data.frame(
  GAPDH1 = ct1$GAPDH,
  GAPDH2 = ct1$GAPDH
  )

# calculate fold change
pcr_analyze(pcr_hk,
            group_var = group_var,
            reference_group = 'brain',
            method = 'delta_ct')

# return a plot
pcr_analyze(pcr_hk,
            group_var = group_var,
            reference_group = 'brain',
            method = 'delta_ct',
            plot = TRUE)

# applying the standard curve method
# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate curve
standard_curve <- pcr_assess(ct3, amount = amount, method = 'standard_curve')
intercept <- standard_curve$intercept
slope <- standard_curve$slope

# apply the standard curve method
pcr_analyze(ct1,
           group_var = group_var,
           reference_gene = 'GAPDH',
           reference_group = 'brain',
           intercept = intercept,
           slope = slope,
           method = 'relative_curve')

# return a plot
pcr_analyze(ct1,
           group_var = group_var,
           reference_gene = 'GAPDH',
           reference_group = 'brain',
           intercept = intercept,
           slope = slope,
           method = 'relative_curve',
           plot = TRUE)

Assess qPCR data quality

Description

A unified interface to invoke different quality assessment methods of qPCR data.

Usage

pcr_assess(df, method = "standard_curve", ...)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows. Each sample are replicates of a known input/dilution given by amount

method

A character string; 'standard_curve' (default) or 'efficiency' for invoking a certain quality assessment model

...

Arguments passed to the methods

Details

The different quality assessment methods can be invoked using the argument method with 'standard_curve' or 'efficiency'. Alternatively, the same methods can be applied by using the corresponding functions: pcr_standard or pcr_efficiency for calculating the amplification efficiency of a PCR reaction or the individual standard curves respectively. Unlike the amplification efficiency calculation when, using the double delta ct model, the standard curves are required in calculating the standard curve analysis model.

Value

A data.frame or a plot. For details; pcr_standard and pcr_efficiency

Examples

#' # locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make amount/dilution variable
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate the standard curve
pcr_assess(ct3,
           amount = amount,
           method = 'standard_curve')

# retrun a plot
pcr_assess(ct3,
           amount = amount,
           method = 'standard_curve',
           plot = TRUE)

# calculate amplification efficiency
pcr_assess(ct3,
           amount = amount,
           reference_gene = 'GAPDH',
           method = 'efficiency')

# return a plot
pcr_assess(ct3,
           amount = amount,
           reference_gene = 'GAPDH',
           method = 'efficiency',
           plot = TRUE)

Calculate the standard curve model

Description

Uses the C_T values and a reference gene and a group, in addition to the intercept and slope of each gene form a serial dilution experiment, to calculate the standard curve model and estimate the normalized relative expression of the target genes.

Usage

pcr_curve(
  df,
  group_var,
  reference_gene,
  reference_group,
  mode = "separate_tube",
  intercept,
  slope,
  plot = FALSE,
  ...
)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_gene

A character string of the column name of a control gene

reference_group

A character string of the control group in group_var

mode

A character string of; 'separate_tube' (default) or 'same_tube'. This is to indicate whether the different genes were run in separate or the same PCR tube

intercept

A numeric vector of intercept and length equals the number of genes

slope

A numeric vector of slopes length equals the number of genes

plot

A logical (default is FALSE)

...

Arguments passed to customize plot

Details

this model doesn't assume perfect amplification but rather actively use the amplification in calculating the relative expression. So when the amplification efficiency of all genes are 100% both methods should give similar results. The standard curve method is applied using two steps. First, serial dilutions of the mRNAs from the samples of interest are used as input to the PCR reaction. The linear trend of the log input amount and the resulting C_T values for each gene are used to calculate an intercept and a slope. Secondly, these intercepts and slopes are used to calculate the amounts of mRNA of the genes of interest and the control/reference in the samples of interest and the control sample/reference. These amounts are finally used to calculate the relative expression.

Value

A data.frame of 7 columns

group The unique entries in group_var
gene The column names of df
normalized The normalized expression of target genes relative to a reference_gene
calibrated The calibrated expression of target genes relative to a reference_group
error The standard deviation of normalized relative expression
lower The lower interval of the normalized relative expression
upper The upper interval of the normalized relative expression

When plot is TRUE, returns a bar graph of the calibrated expression of the genes in the column and the groups in the column group. Error bars are drawn using the columns lower and upper. When more one gene are plotted the default in dodge bars. When the argument facet is TRUE a separate panel is drawn for each gene.

References

Examples

# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# make a vector of RNA amounts
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate curve
standard_curve <- pcr_assess(ct3, amount = amount, method = 'standard_curve')
intercept <- standard_curve$intercept
slope <- standard_curve$slope

# make grouping variable
group <- rep(c('brain', 'kidney'), each = 6)

# apply the standard curve method
pcr_curve(ct1,
          group_var = group,
          reference_gene = 'GAPDH',
          reference_group = 'brain',
          intercept = intercept,
          slope = slope)

# returns a plot
pcr_curve(ct1,
          group_var = group,
          reference_gene = 'GAPDH',
          reference_group = 'brain',
          intercept = intercept,
          slope = slope,
          plot = TRUE)

Calculate the delta_ct model

Description

Uses the C_T values and a reference group to calculate the delta C_T model to estimate the relative fold change of a gene between groups

Usage

pcr_dct(
  df,
  group_var,
  reference_group,
  mode = "separate_tube",
  plot = FALSE,
  ...
)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_group

A character string of the control group in group_var

mode

A character string of; 'separate_tube' (default) or 'same_tube'. This is to indicate whether the different genes were run in separate or the same PCR tube

plot

A logical (default is FALSE)

...

Arguments passed to customize plot

Details

This method is a variation of the double delta C_T model, pcr_ddct. It can be used to calculate the fold change of in one sample relative to the others. For example, it can be used to compare and choosing a control/reference genes.

Value

A data.frame of 7 columns

group The unique entries in group_var
gene The column names of df
calibrated The average C_T value of target genes after subtracting that of the reference_group
fold_change The fold change of genes relative to a reference_group
error The standard deviation of the fold_change
lower The lower interval of the fold_change
upper The upper interval of the fold_change

When plot is TRUE, returns a bar graph of the fold change of the genes in the column and the groups in the column group. Error bars are drawn using the columns lower and upper. When more one gene are plotted the default in dodge bars. When the argument facet is TRUE a separate panel is drawn for each gene.

References

Examples

# locate and read file
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# make a data.frame of two identical columns
pcr_hk <- data.frame(
  GAPDH1 = ct1$GAPDH,
  GAPDH2 = ct1$GAPDH
  )

# add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)

# calculate caliberation
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain')

# returns a plot
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain',
        plot = TRUE)

# returns a plot with facets
pcr_dct(pcr_hk,
        group_var = group_var,
        reference_group = 'brain',
        plot = TRUE,
        facet = TRUE)

Calculate the delta_delta_ct model

Description

Uses the C_T values and a reference gene and a group to calculate the delta delta C_T model to estimate the normalized relative expression of target genes.

Usage

pcr_ddct(
  df,
  group_var,
  reference_gene,
  reference_group,
  mode = "separate_tube",
  plot = FALSE,
  ...
)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_gene

A character string of the column name of a control gene

reference_group

A character string of the control group in group_var

mode

A character string of; 'separate_tube' (default) or 'same_tube'. This is to indicate whether the different genes were run in separate or the same PCR tube

plot

A logical (default is FALSE)

...

Arguments passed to customize plot

Details

The comparative C_T methods assume that the cDNA templates of the gene/s of interest as well as the control/reference gene have similar amplification efficiency. And that this amplification efficiency is near perfect. Meaning, at a certain threshold during the linear portion of the PCR reaction, the amount of the gene of the interest and the control double each cycle. Another assumptions is that, the expression difference between two genes or two samples can be captured by subtracting one (gene or sample of interest) from another (reference). This final assumption requires also that these references don't change with the treatment or the course in question.

Value

A data.frame of 8 columns:

group The unique entries in group_var
gene The column names of df. reference_gene is dropped
normalized The C_T value (or the average C_T value) of target genes after subtracting that of the reference_gene
calibrated The normalized average C_T value of target genes after subtracting that of the reference_group
relative_expression The expression of target genes normalized by a reference_gene and calibrated by a reference_group
error The standard deviation of the relative_expression
lower The lower interval of the relative_expression
upper The upper interval of the relative_expression

When plot is TRUE, returns a bar graph of the relative expression of the genes in the column and the groups in the column group. Error bars are drawn using the columns lower and upper. When more one gene are plotted the default in dodge bars. When the argument facet is TRUE a separate panel is drawn for each gene.

Examples

## locate and read raw ct data
fl <- system.file('extdata', 'ct1.csv', package = 'pcr')
ct1 <- read.csv(fl)

# add grouping variable
group_var <- rep(c('brain', 'kidney'), each = 6)

# calculate all values and errors in one step
pcr_ddct(ct1,
         group_var = group_var,
         reference_gene = 'GAPDH',
         reference_group = 'brain')

# return a plot
pcr_ddct(ct1,
         group_var = group_var,
         reference_gene = 'GAPDH',
         reference_group = 'brain',
         plot = TRUE)

Calculate amplification efficiency

Description

Uses the C_T values from a serial dilution experiment to calculate the amplification efficiency of a PCR reaction.

Usage

pcr_efficiency(df, amount, reference_gene, plot = FALSE)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows. Each sample are replicates of a known input/dilution given by amount

amount

A numeric vector of the input amounts or dilutions. The length of this vector should equal the row number of df

reference_gene

A character string of the column name of a control gene

plot

A logical (default FALSE) to indicate whether to return a data.frame or a plot

Details

Fortunately, regardless of the method used in the analysis of qPCR data, The quality assessment are done in a similar way. It requires an experiment similar to that of calculating the standard curve. Serial dilutions of the genes of interest and controls are used as input to the reaction and different calculations are made. The amplification efficiency is approximated be the linear trend between the difference between the C_T value of a gene of interest and a control/reference (\Delta C_T) and the log input amount. This piece of information is required when using the \Delta \Delta C_T model. Typically, the slope of the curve should be very small and the R^2 value should be very close to one. Other analysis methods are recommended when this is not the case.

Value

When plot is FALSE returns a data.frame of 4 columns describing the line between the \Delta C_T of target genes and the log of amount

gene The column names of df. reference_gene is dropped
intercept The intercept of the line
slope The slope of the line
r_squared The squared correlation

When plot is TRUE returns a graph instead shows the average and standard deviation of of the \Delta C_T at different input amounts. In addition, a linear trend line is drawn.

References

Examples

# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make amount/dilution variable
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate amplification efficiency
pcr_efficiency(ct3,
               amount = amount,
               reference_gene = 'GAPDH')

# plot amplification efficiency
pcr_efficiency(ct3,
               amount = amount,
               reference_gene = 'GAPDH',
               plot = TRUE)

Linear regression qPCR data

Description

Linear regression qPCR data

Usage

pcr_lm(
  df,
  group_var,
  reference_gene,
  reference_group,
  model_matrix = NULL,
  mode = "subtract",
  tidy = TRUE,
  ...
)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_gene

A character string of the column name of a control gene

reference_group

A character string of the control group in group_var

model_matrix

A model matrix for advanced experimental design. for constructing such a matrix with different variables check model.matrix

mode

A character string for the normalization mode. Possible values are "subtract" (default) or "divide".

tidy

A logical whether to return a list of lm or a tidy data.frame. Default TRUE.

...

Other arguments to lm

Value

A data.frame of 6 columns

term The term being tested
gene The column names of df. reference_gene is dropped
estimate The estimate for each term
p_value The p-value for each term
lower The low 95% confidence interval
upper The high 95% confidence interval

When tidy is FALSE, returns a list of lm objects.

Examples

# locate and read data
fl <- system.file('extdata', 'ct4.csv', package = 'pcr')
ct4 <- read.csv(fl)

# make group variable
group <- rep(c('control', 'treatment'), each = 12)

# test
pcr_lm(ct4,
       group_var = group,
       reference_gene = 'ref',
       reference_group = 'control')

# testing using lm method
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 'lm')

Calculate the standard curve

Description

Uses the C_T values from a serial dilution experiment to calculate the a curve for each gene and the log of the input amount

Usage

pcr_standard(df, amount, plot = FALSE)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows. Each sample are replicates of a known input/dilution given by amount

amount

A numeric vector of the input amounts or dilutions. The length of this vector should equal the row number of df

plot

A logical (default FALSE) to indicate whether to return a data.frame or a plot

Details

Fortunately, regardless of the method used in the analysis of qPCR data, The quality assessment are done in a similar way. It requires an experiment similar to that of calculating the standard curve. Serial dilutions of the genes of interest and controls are used as input to the reaction and different calculations are made. Curves are required for each gene using the $C_T$ value and the log of the input amount. In this case, a separate slope and intercept are required for the calculation of the relative expression when applying the standard curve model.

Value

When plot is FALSE returns a data.frame of 4 columns describing the line between the C_T of each gene and the log of amount

gene The column names of df
intercept The intercept of the line
slope The slope of the line
r_squared The squared correlation

When plot is TRUE returns a graph instead shows the average and standard deviation of of the C_T at different input amounts.

References

Examples

# locate and read file
fl <- system.file('extdata', 'ct3.csv', package = 'pcr')
ct3 <- read.csv(fl)

# make amount/dilution variable
amount <- rep(c(1, .5, .2, .1, .05, .02, .01), each = 3)

# calculate the standard curve
pcr_standard(ct3,
             amount = amount)

# plot the standard curve
pcr_standard(ct3,
             amount = amount,
             plot = TRUE)

Statistical testing of PCR data

Description

A unified interface to different statistical significance tests for qPCR data

Usage

pcr_test(df, test = "t.test", ...)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

test

A character string; 't.test' default, 'wilcox.test' or 'lm'

...

Other arguments for the testing methods

Details

The simple t-test can be used to test the significance of the difference between two conditions \Delta C_T. t-test assumes in addition, that the input C_T values are normally distributed and the variance between conditions are comparable. Wilcoxon test can be used when sample size is small and those two last assumptions are hard to achieve.

Two use the linear regression here. A null hypothesis is formulated as following,

C_{T, target, treatment} - C_{T, control, treatment} = C_{T, target, control} - C_{T, control, control} \quad \textrm{or} \quad \Delta\Delta C_T

This is exactly the \Delta\Delta C_T as explained earlier. So the \Delta\Delta C_T is estimated and the null is rejected when \Delta\Delta C_T \ne 0.

Value

A data.frame of 5 columns in addition to term when test == 'lm'

term The linear regression comparison terms
gene The column names of df. reference_gene is dropped
estimate The estimate for each term
p_value The p-value for each term
lower The low 95% confidence interval
upper The high 95% confidence interval

For details about the test methods themselves and different parameters, consult t.test, wilcox.test and lm

References

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

Examples

# locate and read data
fl <- system.file('extdata', 'ct4.csv', package = 'pcr')
ct4 <- read.csv(fl)

# make group variable
group <- rep(c('control', 'treatment'), each = 12)

# test using t-test
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 't.test')

# test using wilcox.test
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 'wilcox.test')

# testing using lm
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 'lm')

# testing advanced designs using a model matrix
# make a model matrix
group <- relevel(factor(group), ref = 'control')
dose <- rep(c(100, 80, 60, 40), each = 3, times = 2)
mm <- model.matrix(~group:dose, data = data.frame(group, dose))

# test using lm
pcr_test(ct4,
         reference_gene = 'ref',
         model_matrix = mm,
         test = 'lm')

# using linear models to check the effect of RNA quality
# make a model matrix
group <- relevel(factor(group), ref = 'control')
set.seed(1234)
quality <- scale(rnorm(n = 24, mean = 1.9, sd = .1))
mm <- model.matrix(~group + group:quality, data = data.frame(group, quality))

# testing using lm
pcr_test(ct4,
         reference_gene = 'ref',
         model_matrix = mm,
         test = 'lm')

# using linear model to check the effects of mixing separate runs
# make a model matrix
group <- relevel(factor(group), ref = 'control')
run <- factor(rep(c(1:3), 8))
mm <- model.matrix(~group + group:run, data = data.frame(group, run))

# test using lm
pcr_test(ct4,
         reference_gene = 'ref',
         model_matrix = mm,
         test = 'lm')

t-test qPCR data

Description

t-test qPCR data

Usage

pcr_ttest(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_gene

A character string of the column name of a control gene

reference_group

A character string of the control group in group_var

tidy

A logical whether to return a list of htest or a tidy data.frame. Default TRUE.

...

Other arguments to t.test

Value

A data.frame of 5 columns

gene The column names of df. reference_gene is dropped
estimate The estimate for each term
p_value The p-value for each term
lower The low 95% confidence interval
upper The high 95% confidence interval

When tidy is FALSE, returns a list of htest objects.

Examples

# locate and read data
fl <- system.file('extdata', 'ct4.csv', package = 'pcr')
ct4 <- read.csv(fl)

# make group variable
group <- rep(c('control', 'treatment'), each = 12)

# test
pcr_ttest(ct4,
          group_var = group,
          reference_gene = 'ref',
          reference_group = 'control')

# test using t.test method
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 't.test')

Wilcoxon test qPCR data

Description

Wilcoxon test qPCR data

Usage

pcr_wilcox(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)

Arguments

df

A data.frame of C_T values with genes in the columns and samples in rows rows

group_var

A character vector of a grouping variable. The length of this variable should equal the number of rows of df

reference_gene

A character string of the column name of a control gene

reference_group

A character string of the control group in group_var

tidy

A logical whether to return a list of htest or a tidy data.frame. Default TRUE.

...

Other arguments to wilcox.test

Value

A data.frame of 5 columns

gene The column names of df. reference_gene is dropped
estimate The estimate for each term
p_value The p-value for each term
lower The low 95% confidence interval
upper The high 95% confidence interval

When tidy is FALSE, returns a list of htest objects.

Examples

# locate and read data
fl <- system.file('extdata', 'ct4.csv', package = 'pcr')
ct4 <- read.csv(fl)

# make group variable
group <- rep(c('control', 'treatment'), each = 12)

# test
pcr_wilcox(ct4,
           group_var = group,
           reference_gene = 'ref',
           reference_group = 'control')

# test using wilcox.test method
pcr_test(ct4,
         group_var = group,
         reference_gene = 'ref',
         reference_group = 'control',
         test = 'wilcox.test')

Calculate the amounts

Description

Usage

Arguments

Value

Examples

Get vector average by a variable

Description

Usage

Arguments

Value

Examples

Get vector coefficient of variance by a variable

Description

Usage

Arguments

Value

Examples

Propage two vectors

Description

Usage

Arguments

Value

Examples

Calculate the intercept of a line

Description

Usage

Arguments

Value

Examples

Normalize vector by another

Description

Usage

Arguments

Value

Examples

Plotting function

Description

Usage

Arguments

Value

Examples

Plot quality assessment graphs

Description

Usage

Arguments

Value

Examples

Raise two to a vector power

Description

Usage

Arguments

Value

Examples

Calculate R squared

Description

Usage

Arguments

Value

Examples

Get vector standard deviation by a variable

Description

Usage

Arguments

Value

Examples

Calculate the slope of a line

Description

Usage

Arguments

Value

Examples

C_T values from qPCR (separate tubes)

Description

Usage

Format

Source

See Also

C_T values from qPCR (same tubes)

Description

`C_T` values from qPCR (separate tubes)

`C_T` values from qPCR (same tubes)

`C_T` values from qPCR (Serial dilutions)

`C_T` values from qPCR (Serial dilutions)

`pcr` package