Create a mzTab-M file from a SummarizedExperiment object
Source: R/SummarizedExperiment.R
SummarizedExperiment-mzTab-M.RdThe MzTabM() method for the SummarizedExperiment::SummarizedExperiment
class compiles a MzTabM data object from the content and data of the
SummarizedExperiment object passed through the function's first parameter
(mtd). The function extracts relevant information from the
SummarizedExperiment's rowData() and colData(). The
SummarizedExperiment's rowData() is expected to contain necessary
information to characterize the individual features. The colData() should
provide experiment and sample information for the mzTab-M metadata section.
MzTabM() uses the mtdFromSampleData() and smf() method (described
below) to compile the mzTab-M's MTD and SMF sections, respectively.
MTD section: information is extracted from the
SummarizedExperiment'scolData(). If not already present, relevant and required information should be added to thecolData(). ThemtdFromSampleData()function is used to extract and compile the MTD section.SMF section: the small molecule feature section is compiled from the
SummarizedExperiment'srowData()(for feature characteristics) andassay()(abundance matrix). ThesmfCols.parameter of thesmf()andMzTabM()methods allows to define the mapping between feature fields and column names of therowData()containing the relevant information.
makeSummarizedExperimentFromMzTabM converts the content of an MzTabM
object into a SummarizedExperiment object. Sample information is extracted
from the MTD (metadata) section and used to populate the colData of the
resulting object, while feature-level quantification values and feature
annotations are extracted from the SMF section and used to populate the
assay data and rowData, respectively.
If the MzTabM object does not contain any small molecule feature data, a
SummarizedExperiment with only colData populated is returned.
Usage
# S4 method for class 'SummarizedExperiment'
MzTabM(
mtd,
id = character(),
sampleCols. = sampleCols(),
msRunCols. = msRunCols(),
assayCols. = assayCols(),
groups = character(),
group_description = character(),
group_type = character(),
group_datatype = character(),
group_unit = character(),
smfCols. = character(),
assayName = 1L,
...
)
# S4 method for class 'SummarizedExperiment'
smf(object, smfCols. = smfCols(), assayName = 1L)
makeSummarizedExperimentFromMzTabM(
mzt,
assayName = 1L,
rowIdCol = "SMF_ID",
smfCols. = smfCols()
)
smfCols(
exp_mass_to_charge = "exp_mass_to_charge",
retention_time_in_seconds = "retention_time_in_seconds",
retention_time_in_seconds_start = "retention_time_in_seconds_start",
retention_time_in_seconds_end = "retention_time_in_seconds_end",
SME_ID_REFS = "SME_ID_REFS",
SME_ID_REF_ambiguity_code = "SME_ID_REF_ambiguity_code",
charge = "charge",
adduct_ion = "adduct_ion",
isotopomer = "isotopomer",
opt_identifier = "global",
...
)Arguments
- mtd
For
MzTabM():SummarizedExperimentcontaining all information to compile theMzTabMobject.- id
For
MzTabM():character(1)with an ID for the mzTab-M file. Mandatory.- sampleCols.
named
charactervector defining the columns in theSummarizedExperiment'scolData()containing information for the individual sample fields (with names being the name of the mzTab-M field and values the respective column name incolData()). ThesampleCols()function can be used to define this parameter. See alsomtdSample()andmtdFromSampleData()for more information.- msRunCols.
named
charactervector defining the columns in theSummarizedExperiment'scolData()containing information on the MS runs (with names being the name of the mzTab-M field and values the respective column name incolData()). Required fields/parameters arelocationandscan_polarity. ThemsRunCols()function can be used to define this parameter. See alsomtdMsRun()andmtdFromSampleData()for more information.- assayCols.
named
charactervector defining the columns in theSummarizedExperiment'scolData()containing information for the individual assays fields (with names being the name of the mzTab-M field and values the respective column name incolData()). Parameter/fieldassayis mandatory. TheassayCols()function can be used to assist compiling this parameter. SeemtdAssay()andmtdFromSampleData()for more information.- groups
For
MzTabM():characterwith the name(s) of the column(s) in theSummarizedExperiment'scolData()that should be used as study variable groups. If not defined (the default) a single study variable group"undefined"and single study variable"undefined"will be used.- group_description
For
MzTabM():characterwith an optional description of each study variable group. If provided its length has to match the length of parametergroups.- group_type
For
MzTabM():characterdefining the type for each study variable group. If provided its length has to match the length of parametergroups. Supported values are"[STATO, STATO:0000252, categorical variable, ]","[STATO, STATO:0000228, ordinal variable, ]"and"[STATO, STATO:0000251, continuous variable, ]"for categorical, ordinal or numerical values, respectively. If not provided (the default) the study variable group type will be inferred from the data type of the respective columns in theSummarizeExperiment'scolData().- group_datatype
For
MzTabM(): optionalcharacterdefining the data type of the values (i.e., study variables) for the study variable group. If provided, its length has to match the length of paramtergroups. Supported values are"xsd:string","xsd:integer","xsd:decimal","xsd:boolean","xsd:date","xsd:time","xsd:dateTime","xsd:anyURI", and"Parameter"(for CV Parameters). Date, time and dateTime values must be encoded in ISO 8601 format. If not provided the type is guessed by the data type of the respective column in theSummarizedExperiment'scolData().- group_unit
For
MzTabM(): optionalcharacterdefining the unit of the group variable (for numeric data types). If provided, its length has to match the length of parametergroups.NAor""has to be provided for groups for which no unit should be reported. By default (group_unit = character()) no unit is reported for any group.- smfCols.
named
characterdefining which SMF section columns should be use to characterize each feature.- assayName
character(1)defining the name of the assay to be used for theSummarizedExperimentassay. Defaults to1L.- ...
For
MzTabM(): additional arguments to be passed tomtdFromSampleData()such asaverage_function,variation_functionordescription- object
A
SummarizedExperiment.- mzt
A
MzTabMobject.- rowIdCol
character(1)defining the column name in the SMF section which should be used as row names for theSummarizedExperiment. Defaults to"SMF_ID".- exp_mass_to_charge
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()containing the information on the features' m/z. Mandatory.- retention_time_in_seconds
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()containing the retention times of the features. Optional- retention_time_in_seconds_start
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()with the retention time start of the features' chromatographic signal. Optional.- retention_time_in_seconds_end
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()with the retention time end of the features' chromatographic signal. Optional.- SME_ID_REFS
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()providing the references to rows in the small molecule evidence (SME) section.- SME_ID_REF_ambiguity_code
For
smfCols:character(1)defining the column name in theSummarizedExperiment'srowData()with ambiguity codes for SME ID references.- charge
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()containing the information on the features' charge state (integer). Optional.- adduct_ion
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()containing information on the adduct/ion for each feature. Optional.- isotopomer
For
smfCols():character(1)defining the column name in theSummarizedExperiment'srowData()containing the information on the features' isotopomer description. Optional.- opt_identifier
characterwith the identifier to be used for optional columns passed through.... This is used to create the column names for optional columns in the formatopt_{opt_identifier}_{column_name}. If not provided, the default is"global". Must be of length 1 or matching the number of opt columns provided in.... Optional.
Value
smfCols()returns a namedcharactervector.smf()returns adata.frame.
A SummarizedExperiment object.
Generate mzTab-M content from a SummarizedExperiment
MzTabM(): function to compile a MzTabM object from the information available in theSummarizedExperiment. The resultingMzTabMobject should be completed by adding additional metadata through e.g.setMtdInstrument(),setMtdDatabase(),setMtdCv(),setMtdContact(),setMtdProtocol()orsetMtdField(). SeemtdFromSampleData()for details and parameters to compile the MTD section. Seesmf()below for information and parameters to compile the SMF section. Note that the SMF section is only created ifsmfCols.is defined and not empty. See MTD-export for more details on the mzTab-M metadata and SMF-export for fields/columns to characterize features in an SMF section.smf(): generate a SMF matrix from aSummarizedExperiment. TheSummarizedExperiment's assay containing the feature abundances can be specified with parameterassayNamewhich defaults toassayName = 1Lhence using the first assay. UseassayNames()on theSummarizedExperimentto list all available assays. ParametersmfCols.defines the mapping between SMF field names and column names in theSummarizedExperiment'srowData(). ThesmfCols()function can be used to assist compiling this mapping. SMF field exp_mass_to_charge is mandatory. See also SMF-export for details and description of the individual fields.smfCols(): allows to define which columns of theSummarizedExperiment'srowData()should be used as columns characterizing the features in the mzTab-M SMF section. Each argument of the function (such asexp_mass_to_charge) represents one of the mzTab-M SMF fields (columns). Using e.g.exp_mass_to_charge = "mzmed"would use the"mzmed"column of therowData()asexp_mass_to_chargefield/column of the mzTab-M SMF section. See SMF-export for details on the individual SMF fields.
Examples
## Load an example `SummarizedExperiment`: contains the results from the
## xcms-based preprocessing and data analysis described in Metabonaut
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
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#> mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#> rank, rbind, rownames, sapply, saveRDS, scale, sequence, table,
#> tapply, transform, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
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#> findMatches
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#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
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#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
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data(se)
se
#> class: SummarizedExperiment
#> dim: 9068 10
#> metadata(0):
#> assays(2): raw raw_filled
#> rownames(9068): FT0001 FT0002 ... FT9067 FT9068
#> rowData names(11): mzmed mzmin ... QC ms_level
#> colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML
#> MS_QC_POOL_4_POS.mzML
#> colData names(15): sample_name derived_spectra_data_file ... polarity
#> instrument
## Information on the features:
rowData(se)
#> DataFrame with 9068 rows and 11 columns
#> mzmed mzmin mzmax rtmed rtmin rtmax npeaks
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> FT0001 50.9898 50.9893 50.9904 203.600 201.459 208.108 8
#> FT0002 51.0590 51.0581 51.0599 191.167 190.053 194.525 9
#> FT0003 51.9866 51.9863 51.9879 203.147 201.459 207.046 7
#> FT0004 53.0204 53.0161 53.0205 203.234 200.962 217.922 10
#> FT0005 53.5208 53.5184 53.5216 203.194 201.183 209.900 10
#> ... ... ... ... ... ... ... ...
#> FT9064 998.697 998.691 998.705 25.352 23.6341 26.4839 4
#> FT9065 998.779 998.758 998.784 162.691 161.5110 164.8667 8
#> FT9066 999.204 999.191 999.218 146.163 143.0103 147.9139 8
#> FT9067 999.330 999.318 999.339 157.048 154.3261 159.1735 7
#> FT9068 999.781 999.775 999.794 162.763 161.5110 164.3995 7
#> CTR CVD QC ms_level
#> <numeric> <numeric> <numeric> <integer>
#> FT0001 1 3 4 1
#> FT0002 2 3 4 1
#> FT0003 0 3 4 1
#> FT0004 3 3 4 1
#> FT0005 3 3 4 1
#> ... ... ... ... ...
#> FT9064 0 0 4 1
#> FT9065 2 2 4 1
#> FT9066 3 1 4 1
#> FT9067 3 1 3 1
#> FT9068 1 3 3 1
## Available information on samples/MS runs:
colData(se)
#> DataFrame with 10 rows and 15 columns
#> sample_name derived_spectra_data_file
#> <character> <character>
#> MS_QC_POOL_1_POS.mzML POOL FILES/MS_QC_POOL_1_P..
#> MS_A_POS.mzML A FILES/MS_A_POS.mzML
#> MS_B_POS.mzML B FILES/MS_B_POS.mzML
#> MS_QC_POOL_2_POS.mzML POOL FILES/MS_QC_POOL_2_P..
#> MS_C_POS.mzML C FILES/MS_C_POS.mzML
#> MS_D_POS.mzML D FILES/MS_D_POS.mzML
#> MS_QC_POOL_3_POS.mzML POOL FILES/MS_QC_POOL_3_P..
#> MS_E_POS.mzML E FILES/MS_E_POS.mzML
#> MS_F_POS.mzML F FILES/MS_F_POS.mzML
#> MS_QC_POOL_4_POS.mzML POOL FILES/MS_QC_POOL_4_P..
#> metabolite_asssignment_file source_name organism
#> <character> <character> <character>
#> MS_QC_POOL_1_POS.mzML m_MTBLS8735_LC-MS_po.. MS_QC_POOL_1_POS Homo sapiens
#> MS_A_POS.mzML m_MTBLS8735_LC-MS_po.. MS_A_POS Homo sapiens
#> MS_B_POS.mzML m_MTBLS8735_LC-MS_po.. MS_B_POS Homo sapiens
#> MS_QC_POOL_2_POS.mzML m_MTBLS8735_LC-MS_po.. MS_QC_POOL_1_POS Homo sapiens
#> MS_C_POS.mzML m_MTBLS8735_LC-MS_po.. MS_C_POS Homo sapiens
#> MS_D_POS.mzML m_MTBLS8735_LC-MS_po.. MS_D_POS Homo sapiens
#> MS_QC_POOL_3_POS.mzML m_MTBLS8735_LC-MS_po.. MS_QC_POOL_1_POS Homo sapiens
#> MS_E_POS.mzML m_MTBLS8735_LC-MS_po.. MS_E_POS Homo sapiens
#> MS_F_POS.mzML m_MTBLS8735_LC-MS_po.. MS_F_POS Homo sapiens
#> MS_QC_POOL_4_POS.mzML m_MTBLS8735_LC-MS_po.. MS_QC_POOL_1_POS Homo sapiens
#> blood_sample_type sample_type age
#> <character> <character> <integer>
#> MS_QC_POOL_1_POS.mzML blood serum pool NA
#> MS_A_POS.mzML blood plasma experimental sample 53
#> MS_B_POS.mzML blood plasma experimental sample 30
#> MS_QC_POOL_2_POS.mzML blood serum pool NA
#> MS_C_POS.mzML blood plasma experimental sample 66
#> MS_D_POS.mzML blood plasma experimental sample 36
#> MS_QC_POOL_3_POS.mzML blood serum pool NA
#> MS_E_POS.mzML blood plasma experimental sample 66
#> MS_F_POS.mzML blood plasma experimental sample 44
#> MS_QC_POOL_4_POS.mzML blood serum pool NA
#> unit phenotype injection_index
#> <character> <character> <integer>
#> MS_QC_POOL_1_POS.mzML year QC 1
#> MS_A_POS.mzML year CVD 2
#> MS_B_POS.mzML year CTR 3
#> MS_QC_POOL_2_POS.mzML year QC 4
#> MS_C_POS.mzML year CTR 5
#> MS_D_POS.mzML year CVD 6
#> MS_QC_POOL_3_POS.mzML year QC 7
#> MS_E_POS.mzML year CTR 8
#> MS_F_POS.mzML year CVD 9
#> MS_QC_POOL_4_POS.mzML year QC 10
#> species tissue polarity
#> <character> <character> <character>
#> MS_QC_POOL_1_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000133, b.. positive
#> MS_A_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_B_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_QC_POOL_2_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000133, b.. positive
#> MS_C_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_D_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_QC_POOL_3_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000133, b.. positive
#> MS_E_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_F_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000131, b.. positive
#> MS_QC_POOL_4_POS.mzML [NCBITaxon, NCBITaxo.. [BTO, BTO:0000133, b.. positive
#> instrument
#> <character>
#> MS_QC_POOL_1_POS.mzML 1
#> MS_A_POS.mzML 1
#> MS_B_POS.mzML 1
#> MS_QC_POOL_2_POS.mzML 1
#> MS_C_POS.mzML 1
#> MS_D_POS.mzML 1
#> MS_QC_POOL_3_POS.mzML 1
#> MS_E_POS.mzML 1
#> MS_F_POS.mzML 1
#> MS_QC_POOL_4_POS.mzML 1
## Available abundance assays:
assayNames(se)
#> [1] "raw" "raw_filled"
#############################################################################
## Create a `MzTabM` object with only metadata defined
## Metadata information is extracted from the `SummarizedExperiment`'s
## `colData()` which is expected to contain the relevant information to
## define and characterize the MS runs, the assays, the samples and the
## experimental/phenotypic conditions. The mapping between the MTD fields
## and the column names in `colData()` can be configured with parameters
## `sampleCols.`, `msRunCols.` and `assayCols.`. We use the respective
## helper functions to define these mappings:
## We use available `colData()` columns `"sample_name"`, `"species"` and
## `"tissue"` for the MTD sample fields *sample*, *species* and *tissue*:
s_cols <- sampleCols(sample = "sample_name", species = "species",
tissue = "tissue")
## We use `colData()` columns `"derived_spectra_data_file"` for *location*
## (i.e., the originating MS data file) and `"polarity"` for the
## *scan_polarity* field:
m_cols <- msRunCols(location = "derived_spectra_data_file",
scan_polarity = "polarity")
## For the present data set one MS run corresponds to one *assay*, hence we
## use also the MS data file name for the MTD field *assay*:
a_cols <- assayCols(assay = "derived_spectra_data_file")
## For the experimental design we use columns `"phenotype"` and `"age"`,
## which we provide with parameter `groups`. Having all these parameters
## defined we create now a `MzTabM` from the `SummarizedExperiment`:
m <- MzTabM(se, id = "MTBLS8735", sampleCols. = s_cols, msRunCols. = m_cols,
assayCols. = a_cols, groups = c("phenotype", "age"))
m
#> Object of class MzTabM
#> mzTab-M version 2.1.0-M
#> MTD section with 150 rows.
head(mtd(m))
#>
#> [1,] "mzTab-version"
#> [2,] "mzTab-ID"
#> [3,] "mzTab-profile"
#> [4,] "software[1]"
#> [5,] "quantification_method"
#> [6,] "sample[1]"
#> values
#> [1,] "2.1.0-M"
#> [2,] "MTBLS8735"
#> [3,] "M"
#> [4,] "[,,RmzTabM,RmzTabM version 0.99.3]"
#> [5,] "[MS, MS:1001834, LC-MS label-free quantitation analysis, ]"
#> [6,] "POOL"
## Importantly, depending on the provided information, the MTD section might
## be needed to be completed. See also the help for `MzTabM` for more
## information and helper functions to add/replace metadata content.
#############################################################################
## Create a `MzTabM` with MTD + SMF
## We need to specify which columns in the `SummarizedExperiment`'s
## `rowData()` contain information characterizing the individual features.
colnames(rowData(se))
#> [1] "mzmed" "mzmin" "mzmax" "rtmed" "rtmin" "rtmax"
#> [7] "npeaks" "CTR" "CVD" "QC" "ms_level"
## - *mzmed* defines the features *m/z* value and can be mapped to the SMF
## field (column) *exp_mass_to_charge*.
## - *rtmed* reports the median retention time of the feature and can be
## used for the SMF field *retention_time_in_seconds*.
## We will in addition add an optional field *feature_id* to report and add
## the IDs of the individual features from the `SummarizedExperiment`:
## add a column `"feature_id"` to the `rowData()`
rowData(se)$feature_id <- rownames(se)
## Define the mapping of SMF fields to `rowData()` columns:
smf_cols <- smfCols(exp_mass_to_charge = "mzmed",
retention_time_in_seconds = "rtmed", feature_id = "feature_id")
## We can use the `smf()` function to create the SMF section. For the
## SMF abundances we use the data from the `"raw_filled"` assay
assayNames(se)
#> [1] "raw" "raw_filled"
tmp <- smf(se, assayName = "raw_filled", smfCols. = smf_cols)
head(tmp)
#> SFH SMF_ID SME_ID_REFS SME_ID_REF_ambiguity_code adduct_ion isotopomer
#> FT0001 SMF 1 null null null null
#> FT0002 SMF 2 null null null null
#> FT0003 SMF 3 null null null null
#> FT0004 SMF 4 null null null null
#> FT0005 SMF 5 null null null null
#> FT0006 SMF 6 null null null null
#> exp_mass_to_charge charge retention_time_in_seconds
#> FT0001 50.9897946401403 null 203.600077134134
#> FT0002 51.059035992328 null 191.167453757996
#> FT0003 51.9865730172271 null 203.14665178874
#> FT0004 53.0203569195002 null 203.234292327779
#> FT0005 53.5208004472819 null 203.193618564868
#> FT0006 54.0100702952703 null 159.281630787851
#> retention_time_in_seconds_start retention_time_in_seconds_end
#> FT0001 null null
#> FT0002 null null
#> FT0003 null null
#> FT0004 null null
#> FT0005 null null
#> FT0006 null null
#> abundance_assay[1] abundance_assay[2] abundance_assay[3]
#> FT0001 421.6162 689.2422 411.3295
#> FT0002 710.8078 875.9192 457.5920
#> FT0003 445.5711 613.4410 277.5022
#> FT0004 16994.5260 24605.7340 19766.7069
#> FT0005 3284.2664 4526.0531 3521.8221
#> FT0006 10681.7476 10009.6602 9599.9701
#> abundance_assay[4] abundance_assay[5] abundance_assay[6]
#> FT0001 481.7436 314.7567 635.2732
#> FT0002 693.6997 781.2416 648.4344
#> FT0003 497.8866 425.3774 634.9370
#> FT0004 17808.0933 22780.6683 22873.1061
#> FT0005 3379.8909 4396.0762 4317.7734
#> FT0006 10800.5449 4792.2390 7296.4262
#> abundance_assay[7] abundance_assay[8] abundance_assay[9]
#> FT0001 439.6086 570.5849 579.9360
#> FT0002 700.9716 1054.0207 534.4577
#> FT0003 449.0933 556.2544 461.0465
#> FT0004 16965.7762 23432.1252 22198.4607
#> FT0005 3270.5290 4533.8667 4161.0132
#> FT0006 2382.1788 9236.9799 6817.8785
#> abundance_assay[10] opt_global_feature_id
#> FT0001 437.0340 FT0001
#> FT0002 711.0361 FT0002
#> FT0003 232.1075 FT0003
#> FT0004 16796.4497 FT0004
#> FT0005 3142.2268 FT0005
#> FT0006 6911.5439 FT0006
## We use the same parameters in the `MzTabM()` function to create a mzTab-M
## with MTD + SMF content. For the MTD we re-use all parameters defined above
m <- MzTabM(se, id = "MTBLS8735", sampleCols. = s_cols, msRunCols. = m_cols,
assayCols. = a_cols, groups = c("phenotype", "age"),
assayName = "raw_filled", smfCols. = smf_cols)
m
#> Object of class MzTabM
#> mzTab-M version 2.1.0-M
#> MTD section with 150 rows.
#> SMF section with 9068 rows and 22 columns.
head(smf(m))
#> SFH SMF_ID SME_ID_REFS SME_ID_REF_ambiguity_code adduct_ion isotopomer
#> FT0001 SMF 1 null null null null
#> FT0002 SMF 2 null null null null
#> FT0003 SMF 3 null null null null
#> FT0004 SMF 4 null null null null
#> FT0005 SMF 5 null null null null
#> FT0006 SMF 6 null null null null
#> exp_mass_to_charge charge retention_time_in_seconds
#> FT0001 50.9897946401403 null 203.600077134134
#> FT0002 51.059035992328 null 191.167453757996
#> FT0003 51.9865730172271 null 203.14665178874
#> FT0004 53.0203569195002 null 203.234292327779
#> FT0005 53.5208004472819 null 203.193618564868
#> FT0006 54.0100702952703 null 159.281630787851
#> retention_time_in_seconds_start retention_time_in_seconds_end
#> FT0001 null null
#> FT0002 null null
#> FT0003 null null
#> FT0004 null null
#> FT0005 null null
#> FT0006 null null
#> abundance_assay[1] abundance_assay[2] abundance_assay[3]
#> FT0001 421.6162 689.2422 411.3295
#> FT0002 710.8078 875.9192 457.5920
#> FT0003 445.5711 613.4410 277.5022
#> FT0004 16994.5260 24605.7340 19766.7069
#> FT0005 3284.2664 4526.0531 3521.8221
#> FT0006 10681.7476 10009.6602 9599.9701
#> abundance_assay[4] abundance_assay[5] abundance_assay[6]
#> FT0001 481.7436 314.7567 635.2732
#> FT0002 693.6997 781.2416 648.4344
#> FT0003 497.8866 425.3774 634.9370
#> FT0004 17808.0933 22780.6683 22873.1061
#> FT0005 3379.8909 4396.0762 4317.7734
#> FT0006 10800.5449 4792.2390 7296.4262
#> abundance_assay[7] abundance_assay[8] abundance_assay[9]
#> FT0001 439.6086 570.5849 579.9360
#> FT0002 700.9716 1054.0207 534.4577
#> FT0003 449.0933 556.2544 461.0465
#> FT0004 16965.7762 23432.1252 22198.4607
#> FT0005 3270.5290 4533.8667 4161.0132
#> FT0006 2382.1788 9236.9799 6817.8785
#> abundance_assay[10] opt_global_feature_id
#> FT0001 437.0340 FT0001
#> FT0002 711.0361 FT0002
#> FT0003 232.1075 FT0003
#> FT0004 16796.4497 FT0004
#> FT0005 3142.2268 FT0005
#> FT0006 6911.5439 FT0006