pcr_lm: Linear regression qPCR data

In pcr: Analyzing Real-Time Quantitative PCR Data

Description

Linear regression qPCR data

Usage

pcr_lm(
  df,
  group_var,
  reference_gene,
  reference_group,
  model_matrix = NULL,
  mode = "subtract",
  tidy = TRUE,
  ...
)

Arguments

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 model.matrix
mode	A character string for the normalization mode. Possible values are "subtract" (default) or "divide".
tidy	A logical whether to return a list of lm or a tidy data.frame. Default TRUE.
...	Other arguments to lm

Value

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.

Examples

# 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')

pcr documentation built on April 1, 2020, 9:07 a.m.