mtdToSampleData() is the inverse of mtdFromSampleData(): it takes an
MTD section and reconstructs a sample data data.frame with one row per
ms_run, analogous to the input expected by mtdFromSampleData().
Value
data.frame with one row per "ms_run" and columns with information
on "ms_run", "assay", "sample", "study_variables",
"instrument", "protocol" sections.
See also
mtdFromSampleData() for the inverse function.
Examples
## Defining an example sample data:
## - file: the mzML file, i.e., the *MS run*.
## - name: the name of the measurement. This is also the name of the sample:
## QC is the pool of all samples, s1 to s4 the ID of the individual.
## - phenotype: defining the biological replicates, 2 for CVD, 2 for CTR.
## - age: covariate, age of the individuals.
## - injection_index: the order in which samples were measured.
sdata <- data.frame(
file = c("1.mzML", "2.mzML", "3.mzML", "4.mzML", "5.mzML", "6.mzML"),
name = c("QC", "s1", "s2", "QC", "s3", "s4"),
phenotype = c(NA, "CVD", "CTR", NA, "CTR", "CVD"),
age = c(NA, 35, 32, NA, 43, 32),
injection_index = c(1, 2, 3, 4, 5, 6))
## Add additional required columns:
sdata$polarity <- "positive"
## Add columns with optional, additional information to the individual
## samples or assays.
sdata$organism <- "[NCBITaxon, NCBITaxon:9606, Homo sapiens, ]"
sdata$assay_info <- c("run1", "run2", "run3", "run4", "run5", "run6")
## Define the columns in `sdata` that provide information on the individual
## samples.
scols <- sampleCols(sample = "name", species = "organism")
## Define the columns in `sdata` that provide MS run information
mscols <- msRunCols(location = "file", scan_polarity = "polarity")
## Define the columns in `sdata` that provide assay information; we use
## the MS run/file name also for the assay name and add an additional
## column with optional content/information.
acols <- assayCols(assay = "file", assay_info = "assay_info")
## Create the MTD section from the `sdata` `data.frame`. Parameter `groups`
## allows to define the columns in `sdata` that should be encoded as
## *study variable groups*.
m <- mtdFromSampleData(sdata, sampleCols = scols, msRunCols = mscols,
assayCols = acols, groups = c("phenotype", "age", "injection_index"))
## Reconstruct the sample data from the MTD section. The column names might
## differ from the original `sdata` `data.frame` if non-mzTabM names are
## used in the original sdata, but the content is identical.
sdata2 <- mtdToSampleData(m)
sdata2
#> id sample_ref ms_run_ref assay custom.1. location
#> 1.mzML assay[1] sample[1] ms_run[1] 1.mzML [,,assay_info, run1] 1.mzML
#> 2.mzML assay[2] sample[2] ms_run[2] 2.mzML [,,assay_info, run2] 2.mzML
#> 3.mzML assay[3] sample[3] ms_run[3] 3.mzML [,,assay_info, run3] 3.mzML
#> 4.mzML assay[4] sample[1] ms_run[4] 4.mzML [,,assay_info, run4] 4.mzML
#> 5.mzML assay[5] sample[4] ms_run[5] 5.mzML [,,assay_info, run5] 5.mzML
#> 6.mzML assay[6] sample[5] ms_run[6] 6.mzML [,,assay_info, run6] 6.mzML
#> scan_polarity.1. sample
#> 1.mzML [MS, MS:1000130, positive scan, ] QC
#> 2.mzML [MS, MS:1000130, positive scan, ] s1
#> 3.mzML [MS, MS:1000130, positive scan, ] s2
#> 4.mzML [MS, MS:1000130, positive scan, ] QC
#> 5.mzML [MS, MS:1000130, positive scan, ] s3
#> 6.mzML [MS, MS:1000130, positive scan, ] s4
#> species.1. injection_index phenotype
#> 1.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 1 <NA>
#> 2.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 2 CVD
#> 3.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 3 CTR
#> 4.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 4 <NA>
#> 5.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 5 CTR
#> 6.mzML [NCBITaxon, NCBITaxon:9606, Homo sapiens, ] 6 CVD
#> age
#> 1.mzML <NA>
#> 2.mzML 35
#> 3.mzML 32
#> 4.mzML <NA>
#> 5.mzML 43
#> 6.mzML 32