pcr_lm | R Documentation |
Linear regression qPCR data
pcr_lm( df, group_var, reference_gene, reference_group, model_matrix = NULL, mode = "subtract", tidy = TRUE, ... )
df |
A data.frame of C_T values with genes in the columns and samples in rows rows |
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')
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