| 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>. |
| Authors: | Mahmoud Ahmed [aut, cre] |
| Maintainer: | Mahmoud Ahmed <[email protected]> |
| License: | GPL-3 |
| Version: | 1.2.2 |
| Built: | 2024-11-12 04:26:13 UTC |
| Source: | https://github.com/mahshaaban/pcr |
A dataset containing the 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.
ct1ct1
A data.frame with 12 rows and 2 variables:
values of the target gene c-myc
values of the control gene GAPDH
http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf
A dataset containing the 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.
ct2ct2
A data.frame with 12 rows and 2 variables:
values of the target gene c-myc
values of the control gene GAPDH
http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf
A dataset containing the 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.
ct3ct3
A data.frame with 21 rows and 2 variables:
values of the target gene c-myc
values of the control gene GAPDH
http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_040980.pdf
A dataset containing the 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.
ct4ct4
A data.frame with 24 rows and 2 variables:
values of the reference gene
values of the target gene
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1395339/
pcr packageAnalyzing real-time quantitative PCR data
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>.
A unified interface to invoke different analysis methods of qPCR data.
pcr_analyze(df, method = "delta_delta_ct", ...)pcr_analyze(df, method = "delta_delta_ct", ...)
df |
A data.frame of |
method |
A character string; 'delta_delta_ct' default, 'delta_ct' or 'relative_curve' for invoking a certain analysis model |
... |
Arguments passed to the methods |
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 , 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
A data.frame by default, when plot is TRUE returns a plot.
For details; pcr_ddct, pcr_dct and pcr_curve.
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.
# 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)# 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)
A unified interface to invoke different quality assessment methods of qPCR data.
pcr_assess(df, method = "standard_curve", ...)pcr_assess(df, method = "standard_curve", ...)
df |
A data.frame of |
method |
A character string; 'standard_curve' (default) or 'efficiency' for invoking a certain quality assessment model |
... |
Arguments passed to the methods |
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.
A data.frame or a plot. For details; pcr_standard and pcr_efficiency
#' # 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)#' # 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)
Uses the 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.
pcr_curve( df, group_var, reference_gene, reference_group, mode = "separate_tube", intercept, slope, plot = FALSE, ... )pcr_curve( df, group_var, reference_gene, reference_group, mode = "separate_tube", intercept, slope, plot = FALSE, ... )
df |
A data.frame of |
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 |
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 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.
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.
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.
# 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)# 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)
Uses the values and a reference group to calculate the delta
model to estimate the relative fold change of a gene between groups
pcr_dct( df, group_var, reference_group, mode = "separate_tube", plot = FALSE, ... )pcr_dct( df, group_var, reference_group, mode = "separate_tube", plot = FALSE, ... )
df |
A data.frame of |
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 |
This method is a variation of the double delta 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.
A data.frame of 7 columns
group The unique entries in group_var
gene The column names of df
calibrated The average 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.
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.
# 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)# 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)
Uses the values and a reference gene and a group to calculate the
delta delta model to estimate the normalized relative expression
of target genes.
pcr_ddct( df, group_var, reference_gene, reference_group, mode = "separate_tube", plot = FALSE, ... )pcr_ddct( df, group_var, reference_gene, reference_group, mode = "separate_tube", plot = FALSE, ... )
df |
A data.frame of |
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 |
The comparative 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.
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 value (or the average value) of
target genes after subtracting that of the reference_gene
calibrated The normalized average 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.
## 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)## 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)
Uses the values from a serial dilution experiment to calculate the
amplification efficiency of a PCR reaction.
pcr_efficiency(df, amount, reference_gene, plot = FALSE)pcr_efficiency(df, amount, reference_gene, plot = FALSE)
df |
A data.frame of |
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 |
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
value of a gene of interest and a control/reference
() and the log input amount. This piece of information is
required when using the model. Typically, the slope
of the curve should be very small and the value should be very
close to one. Other analysis methods are recommended when this is not the
case.
When plot is FALSE returns a data.frame of 4 columns describing the
line between the 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 at different input amounts.
In addition, a linear trend line is drawn.
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.
# 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)# 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
pcr_lm( df, group_var, reference_gene, reference_group, model_matrix = NULL, mode = "subtract", tidy = TRUE, ... )pcr_lm( df, group_var, reference_gene, reference_group, model_matrix = NULL, mode = "subtract", tidy = TRUE, ... )
df |
A data.frame of |
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
|
mode |
A character string for the normalization mode. Possible values are "subtract" (default) or "divide". |
tidy |
A |
... |
Other arguments to |
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.
# 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')# 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')
Uses the values from a serial dilution experiment to calculate the
a curve for each gene and the log of the input amount
pcr_standard(df, amount, plot = FALSE)pcr_standard(df, amount, plot = FALSE)
df |
A data.frame of |
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 |
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.
When plot is FALSE returns a data.frame of 4 columns describing the
line between the 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 at different input amounts.
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.
# 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)# 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)
A unified interface to different statistical significance tests for qPCR data
pcr_test(df, test = "t.test", ...)pcr_test(df, test = "t.test", ...)
df |
A data.frame of |
test |
A character string; 't.test' default, 'wilcox.test' or 'lm' |
... |
Other arguments for the testing methods |
The simple t-test can be used to test the significance of the
difference between two conditions . t-test assumes in
addition, that the input 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,
This is exactly the as explained earlier. So the
is estimated and the null is rejected when
.
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
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.
# 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')# 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
pcr_ttest(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)pcr_ttest(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)
df |
A data.frame of |
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 |
... |
Other arguments to |
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.
# 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')# 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
pcr_wilcox(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)pcr_wilcox(df, group_var, reference_gene, reference_group, tidy = TRUE, ...)
df |
A data.frame of |
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 |
... |
Other arguments to |
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.
# 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')# 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')