Design a batch experiment with experimental controls

To ensure that the batches are comparable some samples are processed in each batch. This function allows to take into account that effect. It uses the most different samples as controls as defined with extreme_cases().

Usage

replicates(pheno, size_subset, controls, omit = NULL, iterations = 500)

Arguments

pheno: Data.frame with the sample information.
size_subset: Numeric value of the number of sample per batch.
controls: The numeric value of the amount of technical controls per batch.
omit: Name of the columns of the pheno that will be omitted.
iterations: Numeric value of iterations that will be performed.

Value

A index with some samples duplicated in the batches.

Details

To control for variance replicates are important, see for example https://www.nature.com/articles/nmeth.3091.

Examples

samples <- data.frame(L = letters[1:25], Age = rnorm(25),
                      type = sample(LETTERS[1:5], 25, TRUE))
index <- replicates(samples, 5, controls = 2, omit = "L", iterations = 10)
head(index)
#> $SubSet1
#> [1]  1  7 12 19 21
#> 
#> $SubSet2
#> [1]  2  4 17 19 21
#> 
#> $SubSet3
#> [1]  6 18 19 21 25
#> 
#> $SubSet4
#> [1]  5 19 21 22 23
#> 
#> $SubSet5
#> [1]  3  8 11 19 21
#> 
#> $SubSet6
#> [1] 10 19 21 24
#>