.pcr_plot_analyze | R Documentation |
Plotting function
.pcr_plot_analyze(df, method, facets = FALSE)
df |
A data.frame such as this returned by pcr_analyze |
method |
A character string. Possible input includes 'delta_delta_ct', 'delta_ct' or 'relative_curve' |
facets |
A logical of whether or not to use facets when applicable |
A ggplot object. A bar graph of caculated ge
## 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 delta_delta_ct model df <- pcr_ddct(ct1, group_var = group_var, reference_gene = 'GAPDH', reference_group = 'brain') # make a plot pcr:::.pcr_plot_analyze(df, method = 'delta_delta_ct') # make a data.frame of two identical columns pcr_hk <- data.frame( GAPDH1 = ct1$GAPDH, GAPDH2 = ct1$GAPDH ) # calculate delta_ct model df <- pcr_dct(pcr_hk, group_var = group_var, reference_group = 'brain') # make a plot pcr:::.pcr_plot_analyze(df, method = 'delta_ct') pcr:::.pcr_plot_analyze(df, method = 'delta_ct', facet = TRUE) # calculate curve # locate and read data 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) standard_curve <- pcr_assess(ct3, amount = amount, method = 'standard_curve') intercept <- standard_curve$intercept slope <- standard_curve$slope # calculate the rellative_curve model df <- pcr_curve(ct1, group_var = group_var, reference_gene = 'GAPDH', reference_group = 'brain', intercept = intercept, slope = slope) # make a plot pcr:::.pcr_plot_analyze(df, method = 'relative_curve')
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