| Version: | 1.0.2 |
| 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. |
| URL: | https://github.com/lixiang117423/qPCRtools |
| BugReports: | https://github.com/lixiang117423/qPCRtools/issues |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | broom, dplyr, ggplot2, ggpmisc, ggthemes, kableExtra, magrittr, multcomp, rstatix, stats, tibble, tidyr |
| Config/roxygen2/version: | 8.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-10-29 05:29:15 UTC; lixiang |
| Author: | Xiang LI [cre, aut] |
| Maintainer: | Xiang LI <lixiang117423@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-10-29 05:50:02 UTC |
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 <lixiang117423@gmail.com>
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 <lixiang117423@gmail.com>
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 <lixiang117423@gmail.com>
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 <lixiang117423@gmail.com>
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 <lixiang117423@gmail.com>
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 <lixiang117423@gmail.com>
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)