pcr_analyze | R Documentation |
A unified interface to invoke different analysis methods of qPCR data.
pcr_analyze(df, method = "delta_delta_ct", ...)
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 |
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
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)
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