Check if data represents repeated measurements design

Check if data represents repeated measurements design

checkRepeatedDesign(summarization_output)

Arguments

summarization_output

output of the dataProcess function

Value

logical, TRUE if data represent repeated measurements design

Details

This extracts information required by the group comparison workflow

Examples

QuantData1 <- dataProcess(SRMRawData, use_log_file = FALSE)
#> INFO  [2021-07-05 20:05:33] ** Features with one or two measurements across runs are removed.
#> INFO  [2021-07-05 20:05:33] ** Fractionation handled.
#> INFO  [2021-07-05 20:05:33] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO  [2021-07-05 20:05:33] ** Log2 intensities under cutoff = 3.776  were considered as censored missing values.
#> INFO  [2021-07-05 20:05:33] ** Log2 intensities = NA were considered as censored missing values.
#> INFO  [2021-07-05 20:05:33] ** Use all features that the dataset originally has.
#> INFO  [2021-07-05 20:05:33] 
#>  # proteins: 2
#>  # peptides per protein: 2-2
#>  # features per peptide: 3-3
#> INFO  [2021-07-05 20:05:33] 
#>                     1 2 3 4 5 6 7 8 9 10
#>              # runs 3 3 3 3 3 3 3 3 3  3
#>     # bioreplicates 3 3 3 3 3 3 3 3 3  3
#>  # tech. replicates 1 1 1 1 1 1 1 1 1  1
#> INFO  [2021-07-05 20:05:33]  == Start the summarization per subplot...
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
#> INFO  [2021-07-05 20:05:33]  == Summarization is done.
checkRepeatedDesign(QuantData1)
#> [1] TRUE