filterAdductTable: filter samples with low QC and features with large missing...
In JosieLHayes/adductomicsR: Processing of adductomic mass spectral datasets

Description Usage Arguments Value Examples

filter samples with low QC and features with large missing values Removes adducts that have not been integrated with many missing values and provides QC on samples

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filterAdductTable(adductTable = NULL, percMissing = 51, HKPmass = 
"575.3", quantPeptideMass = "811.7", remHKPzero = FALSE, remQuantPepzero 
=FALSE, remHKPlow = FALSE, outputDir = NULL)

`adductTable`	character a full path to the peaktable with number of rows equal to the number of adducts from outputPeakTable() which starts with adductQuantif_peakList_
`percMissing`	numeric percentage threshold to remove adducts with missing values. Default is 51. It is recommended to use just over the number of samples in the smallest group of your study. 51 is used as default for a 50:50 case control study
`HKPmass`	numeric mass for the housekeeping peptide. Must be the same asthat in the adduct table. max 2 decimal places. default= 575.3 for the LVNEVTEFAK peptide
`quantPeptideMass`	numeric mass for the peptide for which adducts are being quantified, Default is 811.7 for the ALVLIAFAQYLQQCPFEDHVK peptide
`remHKPzero`	logical if TRUE removes all samples where the housekeeping peptide is 0. default= FALSE
`remQuantPepzero`	logical if TRUE removes all samples where the peptide under quantification is 0. default= FALSE
`remHKPlow`	logical if TRUE removes all samples where the housekeeping peptide has an area less than 100000. default= TRUE.This is recommended because this peak should be large. If the HKP has been mis-identified quantification of all adducts will be affected.
`outputDir`	character path to results directory output is a csv file with only adducts and samples that passed filter. Remaining adducts can be quantified manually however it is recommended to rescale the quantification results and include the quantification method as a covariate in downstream analysis.

csv file

filterAdductTable(adductTable=paste0(system.file("extdata",
package="adductomicsR"),'/example_adductQuantif_peakList.csv'), percMissing
=51,HKPmass = "575.3", quantPeptideMass = "811.7",
remHKPzero=FALSE,remQuantPepzero = FALSE, remHKPlow = FALSE, outputDir =
NULL)