install.packages("qPCRtools")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. The function have three patameters:
data: The table of RNA concentration. The unit of concentration is ng/μl. The demo data can be found at GitHub.template: The table of reagent for reverse transcription. The demo data can be found at GitHub. The column All is the total volume for 1 μg RNA.RNA.weight: The mass of RNA. The unit is μg. The default value is 2.suppressMessages(library(tidyverse))
library(qPCRtools)
df.1.path <- system.file("examples", "crtv.data.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "crtv.template.txt", package = "qPCRtools")
df.1 <- data.table::fread(df.1.path)
df.2 <- data.table::fread(df.2.path)
result <- CalRTable(data = df.1, template = df.2, RNA.weight = 2)
result %>%
dplyr::slice(1:6) %>%
kableExtra::kable(format = "html") %>%
kableExtra::kable_styling("striped")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. The function has 6 parameters:
cq.table: The table of Cq. It must contain at least two columns:One Position and Cq. The demo data can be found at GitHub.concen.table: The table of gene(s) and concentration. It must contain at least three columns: Position, Gene and Conc. The demo data can be found at GitHub.lowest.concen: The lowest concentration used to calculate the standard curve.highest.concen: The highest concentration used to calculate the standard curve.dilu: The dilution factor of cDNA template. The default value is 4.by: Calculate the standard curve by average data or the full data. The default value is mean.library(qPCRtools)
df.1.path <- system.file("examples", "calsc.cq.txt", package = "qPCRtools")
df.2.path <- system.file("examples", "calsc.info.txt", package = "qPCRtools")
df.1 <- data.table::fread(df.1.path)
df.2 <- data.table::fread(df.2.path)
CalCurve(
cq.table = df.1,
concen.table = df.2,
lowest.concen = 4,
highest.concen = 4096,
dilu = 4,
by = "mean"
) -> p
p[["table"]] %>%
dplyr::slice(1:6) %>%
kableExtra::kable(format = "html") %>%
kableExtra::kable_styling("striped")
p[["figure"]]
After we calculated the standard curve, we can use the standard curve to calculate the expression level of genes. In qPCRtools, function CalExpCurve can get the expression using standard curve. There are several parameters in this function:
cq.table: The table of Cq. It must contain at least two columns:One Position and Cq. The demo data can be found at GitHub.curve.table: The table of standard curve calculated by CalCurve.design.table: The design information including three columns: Position, Treatment and Gene. The demo table can be found at GitHub.correction: Expression level is corrected or not with internal reference genes. The default value is TRUE.ref.gene: The name of reference gene.stat.method: The method used to calculate differential expression of genes. If we want to calculate the difference between target group and reference group, one of t.test or wilcox.test can be used. anova is for all groups. The default value is t.test.ref.group: The name of reference group. If stat.method is t.test or wilcox.test, the function need a ref.group.fig.type: The type of figure, box or bar. box represents boxplot. bar represents barplot. The default value is box.fig.ncol: The column of figure. The default value is NULL.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 = data.table::fread(df1.path)
curve.table = data.table::fread(df2.path)
design.table = data.table::fread(df3.path)
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"]] %>%
dplyr::slice(1:6) %>%
kableExtra::kable(format = "html") %>%
kableExtra::kable_styling("striped")
res[["figure"]]$2^{-{Δ}{Δ}{C_t }} $is a widely used method to calculate qPCR data[@livak2001analysis]. Our function CalExp2ddCt can do it. Seven parameters are required for this function:
cq.table: The demo file can be found at GitHub.design.table: The demo data can be found at GitHub.
Other parameters are same as the function CalExpCurve.ref.gene: The name of reference gene.ref.group: The name of reference group. If stat.method is t.test or wilcox.test, the function need a ref.group.stat.method: The method used to calculate differential expression of genes. If we want to calculate the difference between target group and reference group, one of t.test or wilcox.test can be used. anova is for all groups. The default value is t.test.fig.type: The type of figure, box or bar. box represents boxplot. bar represents barplot. The default value is box.fig.ncol: The column of figure. The default value is NULL.df1.path = system.file("examples", "ddct.cq.txt", package = "qPCRtools")
df2.path = system.file("examples", "ddct.design.txt", package = "qPCRtools")
cq.table = data.table::fread(df1.path)
design.table = data.table::fread(df2.path)
CalExp2ddCt(cq.table,
design.table,
ref.gene = "OsUBQ",
ref.group = "CK",
stat.method = "t.test",
fig.type = "bar",
fig.ncol = NULL) -> res
res[["table"]] %>%
dplyr::slice(1:6) %>%
kableExtra::kable(format = "html") %>%
kableExtra::kable_styling("striped")
res[["figure"]]The method from SATQPCR can identify the most stable reference genes (REF) across biological replicates and technical replicates[@rancurel2019satqpcr]. Our package provides a function, CalExpRqPCR, to achieve it. In the design.table, BioRep, TechRep and Eff are required. BioRep is the biological replicates. TechRep is the technical replicates. Eff is the amplification efficiency of genes. The cq.table can be found at GitHub and the design,table can be found at GitHub. If user want to give reference gene, ref.gene can be used (The default is NULL).
ref.group: The name of reference group. If stat.method is t.test or wilcox.test, the function need a ref.group.stat.method: The method used to calculate differential expression of genes. If we want to calculate the difference between target group and reference group, one of t.test or wilcox.test can be used. anova is for all groups. The default value is t.test.fig.type: The type of figure, box or bar. box represents boxplot. bar represents barplot. The default value is box.fig.ncol: The column of figure. The default value is NULL.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 <- data.table::fread(df1.path, header = TRUE)
design.table <- data.table::fread(df2.path, header = TRUE)
CalExpRqPCR(cq.table,
design.table,
ref.gene = NULL,
ref.group = "CK",
stat.method = "t.test",
fig.type = "bar",
fig.ncol = NULL
) -> res
res[["table"]] %>%
dplyr::slice(1:6) %>%
kableExtra::kable(format = "html") %>%
kableExtra::kable_styling("striped")
res[["figure"]]If this package is used in your publication, please cite qPCRtools paper: