Get information about number of measurements for each group

Get information about number of measurements for each group

getSamplesInfo(summarization_output)

Arguments

summarization_output	output of the dataProcess function

Value

data.table

Details

This function extracts information required to compute percentages of missing and imputed values in group comparison.

Examples

QuantData <- dataProcess(DDARawData, use_log_file = FALSE)
#> INFO  [2021-07-05 20:05:57] ** Features with one or two measurements across runs are removed.
#> INFO  [2021-07-05 20:05:57] ** Fractionation handled.
#> INFO  [2021-07-05 20:05:57] ** Updated quantification data to make balanced design. Missing values are marked by NA
#> INFO  [2021-07-05 20:05:57] ** Log2 intensities under cutoff = 13.456  were considered as censored missing values.
#> INFO  [2021-07-05 20:05:57] ** Log2 intensities = NA were considered as censored missing values.
#> INFO  [2021-07-05 20:05:57] ** Use all features that the dataset originally has.
#> INFO  [2021-07-05 20:05:57] 
#>  # proteins: 6
#>  # peptides per protein: 11-32
#>  # features per peptide: 1-1
#> INFO  [2021-07-05 20:05:57] 
#>                     C1 C2 C3 C4 C5 C6
#>              # runs  3  3  3  3  3  3
#>     # bioreplicates  1  1  1  1  1  1
#>  # tech. replicates  3  3  3  3  3  3
#> INFO  [2021-07-05 20:05:58] Some features are completely missing in at least one condition:  
#>  D.GPLTGTYR_23_23_NA_NA,
#>  F.HFHWGSSDDQGSEHTVDR_402_402_NA_NA,
#>  G.PLTGTYR_8_8_NA_NA,
#>  H.SFNVEYDDSQDK_465_465_NA_NA,
#>  K.AVVQDPALKPL_156_156_NA_NA ...
#> INFO  [2021-07-05 20:05:58]  == Start the summarization per subplot...
#> 
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#> INFO  [2021-07-05 20:05:58]  == Summarization is done.
samples_info <- getSamplesInfo(QuantData)
samples_info
#>    GROUP NumRuns
#> 1:    C1       3
#> 2:    C2       3
#> 3:    C3       3
#> 4:    C4       3
#> 5:    C5       3
#> 6:    C6       3