Package 'qPCRtools'

Title: Tools for qPCR
Description: Provides tools for quantitative PCR (qPCR) data analysis, including standard curve calculation with amplification efficiency, relative expression calculation by the 2-'dCt', 2-'ddCt', standard curve and 'RqPCR' methods, and RNA volume calculation for reverse transcription.
Authors: Xiang LI [cre, aut]
Maintainer: Xiang LI <[email protected]>
License: MIT + file LICENSE
Version: 1.0.2
Built: 2026-10-29 17:23:49 UTC
Source: https://github.com/cran/qPCRtools

Help Index

Standard Curve Calculation.

Description

The function can calculate the standard curve. At the same time, it can get the amplification efficiency of primer(s). Based on the amplification efficiency, we can know which method can be used to calculate the expression level.

Usage

CalCurve(
  cq.table,
  concen.table,
  highest.concen,
  lowest.concen,
  dilution = 4,
  by.mean = TRUE
)

Arguments

cq.table

The data frame of the position and Cq value.
concen.table

The data frame of the position and concentration.
highest.concen

The highest concentration for calculation.
lowest.concen

The lowest concentration for calculation.
dilution

Dilution factor of cDNA template. The default value is 4.
by.mean

Calculation by mean Cq value or not. The default value is TRUE.

Value

A list.

Author(s)

Xiang LI <[email protected]>

Examples

df.1.path <- system.file("examples", "calsc.cq.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "calsc.info.txt", package = "qPCRtools")
df.1 <- read.table(df.1.path, header = TRUE)
df.2 <- read.table(df.2.path, header = TRUE)
CalCurve(
  cq.table = df.1,
  concen.table = df.2,
  lowest.concen = 4,
  highest.concen = 4096,
  dilu = 4,
  by = "mean"
) -> p

p[["table"]]
p[["figure"]]

Calculate expression using standard curve.

Description

Calculate expression using standard curve.

Usage

CalExp2dCt(cq.table, design.table, ref.gene = "Actin")

Arguments

cq.table

The data frame of the position and cq value.
design.table

The data frame of the position and corresponding information.
ref.gene

The name of reference gene.

Value

A list contain a table and a figure.

Author(s)

Xiang LI <[email protected]>

Examples

df1.path <- system.file("examples", "dct.cq.txt", package = "qPCRtools")
df2.path <- system.file("examples", "dct.design.txt", package = "qPCRtools")
cq.table <- read.table(df1.path, sep = ",", header = TRUE)
design.table <- read.table(df2.path, sep = ",", header = TRUE)
CalExp2dCt(cq.table,
           design.table,
           ref.gene = "Actin"
) -> res

Calculate expression using standard curve.

Description

Calculate expression using standard curve.

Usage

CalExp2ddCt(
  cq.table,
  design.table,
  ref.gene = "OsUBQ",
  ref.group = "CK",
  stat.method = "t.test",
  remove.outliers = TRUE,
  fig.type = "box",
  fig.ncol = NULL
)

Arguments

cq.table

The data frame of the position and cq value.
design.table

The data frame of the position and corresponding information.
ref.gene

The name of reference gene.
ref.group

The name of reference group.
stat.method

Statistical method.
remove.outliers

Remove the outliers of each group and gene, or not.
fig.type

Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'.
fig.ncol

Number of columes of figure.

Value

A list contain a table and a figure.

Author(s)

Xiang LI <[email protected]>

Examples

df1.path = system.file("examples", "ddct.cq.txt", package = "qPCRtools")
df2.path = system.file("examples", "ddct.design.txt", package = "qPCRtools")

cq.table = read.table(df1.path, header = TRUE)
design.table = read.table(df2.path, header = TRUE)

CalExp2ddCt(cq.table,
            design.table,
            ref.gene = "OsUBQ",
            ref.group = "CK",
            stat.method = "t.test",
            remove.outliers = TRUE,
            fig.type = "box",
            fig.ncol = NULL) -> res

res[["table"]]
res[["figure"]]

Calculate expression using standard curve.

Description

Calculate expression using standard curve.

Usage

CalExpCurve(
  cq.table,
  curve.table,
  design.table,
  correction = TRUE,
  ref.gene = "OsUBQ",
  stat.method = "t.test",
  ref.group = "CK",
  fig.type = "box",
  fig.ncol = NULL,
  dilution = 4
)

Arguments

cq.table

The data frame of the position and Cq value.
curve.table

The data frame of standard curve parameters by gene.
design.table

The data frame of the position and corresponding information.
correction

Correct expression value by reference gene.
ref.gene

The name of reference gene.
stat.method

Statistical method.
ref.group

The name of reference group.
fig.type

Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'.
fig.ncol

Number of columes of figure.
dilution

Dilution factor used when the standard curve was fitted; it is the base of the log transform in 'CalCurve()' (default 4). Expression is reported as concentration, i.e. 'dilution^((Cq - Intercept) / Slope)'.

Value

A list contain a table and a figure.

Author(s)

Xiang LI <[email protected]>

Examples

df1.path = system.file("examples", "cal.exp.curve.cq.txt", package = "qPCRtools")
df2.path = system.file("examples", "cal.expre.curve.sdc.txt", package = "qPCRtools")
df3.path = system.file("examples", "cal.exp.curve.design.txt", package = "qPCRtools")

cq.table = read.table(df1.path, header = TRUE)
curve.table = read.table(df2.path, sep = "\t", header = TRUE)
design.table = read.table(df3.path, header = TRUE)

CalExpCurve(
  cq.table,
  curve.table,
  design.table,
  correction = TRUE,
  ref.gene = "OsUBQ",
  stat.method = "t.test",
  ref.group = "CK",
  fig.type = "box",
  fig.ncol = NULL) -> res

res[["table"]]
res[["figure"]]

Calculate expression using standard curve.

Description

Calculate expression using standard curve.

Usage

CalExpRqPCR(
  cq.table,
  design.table,
  ref.gene = NULL,
  ref.group = "CK",
  stat.method = "t.test",
  fig.type = "box",
  fig.ncol = NULL
)

Arguments

cq.table

The data frame of the position and cq value.
design.table

The data frame of the position and corresponding information.
ref.gene

The name of reference gene.
ref.group

The name of reference group.
stat.method

Statistical method.
fig.type

Output image type, 'box' represents 'boxplot', 'bar' represents 'barplot'.
fig.ncol

Number of columes of figure.

Value

A list contain a table and a figure.

Author(s)

Xiang LI <[email protected]>

Examples

df1.path <- system.file("examples", "cal.expre.rqpcr.cq.txt", package = "qPCRtools")
df2.path <- system.file("examples", "cal.expre.rqpcr.design.txt", package = "qPCRtools")

cq.table <- read.table(df1.path, header = TRUE)
design.table <- read.table(df2.path, header = TRUE)

CalExpRqPCR(cq.table,
           design.table,
           ref.gene = NULL,
           ref.group = "CK",
           stat.method = "t.test",
           fig.type = "box",
           fig.ncol = NULL
           ) -> res

res[["table"]]
res[["figure"]]

Calculate RNA volume for reverse transcription.

Description

The first step of qPCR is usually the preparation of cDNA. We need to calculate the column of RNA for reverse transcription to cDNA. So, if we have the concentration of RNA, we can use the function 'CalRTable' to do that.

Usage

CalRTable(data, template, RNA.weight = 1)

Arguments

data

A data.frame contained the sample names and the concentration value. The default unit of concentration is ng/uL.
template

A data.frame contained the information of reverse transcription. In this data.frame there must be a column called 'all'.
RNA.weight

RNA weight required for reverse transcription. Default is 1 ug.

Value

A list contain a table and a figure.

Author(s)

Xiang LI <[email protected]>

Examples

df.1.path <- system.file("examples", "crtv.data.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "crtv.template.txt", package = "qPCRtools")
df.1 <- read.table(df.1.path, sep = "\t", header = TRUE)
df.2 <- read.table(df.2.path, sep = "\t", header = TRUE)
result <- CalRTable(data = df.1, template = df.2, RNA.weight = 2)
head(result)