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Jasen Finch
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Merge pull request #37 from jasenfinch/devel
v0.5.9
2 parents 73f5cef + c1838f3 commit 06f2b9b

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‎DESCRIPTION

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Package: metaboMisc
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Title: Miscellaneous Functions for Metabolomics Analyses
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Version: 0.5.8
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Version: 0.5.9
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Authors@R: person("Jasen", "Finch", email = "jsf9@aber.ac.uk", role = c("aut", "cre"))
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Description: Miscellaneous helper functions for metabolomics analyses that do not yet have a permanent home.
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URL: https://jasenfinch.github.io/metaboMisc

‎NAMESPACE

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@@ -59,6 +59,7 @@ importFrom(ggplot2,theme)
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importFrom(lubridate,seconds_to_period)
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importFrom(magrittr,"%>%")
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importFrom(magrittr,set_names)
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importFrom(metabolyseR,"changeParameter<-")
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importFrom(metabolyseR,"dat<-")
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importFrom(metabolyseR,"parameters<-")
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importFrom(metabolyseR,"preTreated<-")

‎NEWS.md

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# metabMisc 0.5.8
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# metaboMisc 0.5.9
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* Corrected the exported file name for mode-less processed data for the `MetaboProfile` class.
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* Fixed the character sanitation in `sanitiseTable()`.
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* Fixed extraneous console messages in `detectMissInjections()` and `detectBatchDiff()` methods for the `MetaboProfile` class.
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* Enabled unit tests for methods for the `MetaboProfile` class.
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* `detectPretreatmentParameters()` now detects the presence of QC samples.
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# metaboMisc 0.5.8
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* Numbers of characters in strings are now limited by `sanitiseTable()`.
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‎R/detect.R

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@@ -60,20 +60,27 @@ setMethod('detectMissInjections',signature = 'MetaboProfile',
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mi <- x %>%
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processedData()
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63-
if (str_detect(technique(x),'GCMS')) {
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mi <- mi %>%
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rowSums() %>%
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tibble(value = .) %>%
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bind_cols(i)
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} else {
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mi <- mi %>%
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map(rowSums) %>%
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bind_cols() %>%
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rowSums() %>%
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as_tibble() %>%
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bind_cols(i)
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if (!is.list(mi)){
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mi <- list(mi)
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}
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mi <- mi %>%
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map(rowSums)
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if (mi %>%
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names() %>%
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is.na() %>%
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any()) {
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names(mi) <- replace(names(mi),
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is.na(names(mi)),
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'NA')
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}
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mi %>%
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mi %>%
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bind_cols() %>%
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rowSums() %>%
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as_tibble() %>%
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bind_cols(i) %>%
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missInject(idx = idx)
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})
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@@ -102,7 +109,7 @@ missInject <- function(TICdat,idx){
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#' @details Analysis of Variance (ANOVA) is used to detect differences in total ion count (TIC) averages between batches/blocks.
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#' @examples
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#' ## Retrieve file paths and sample information for example data
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#' files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
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#' files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes',ask = FALSE)[1:2]
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#'
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#' info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
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#'
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TICdat <- x %>%
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processedData()
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if (str_detect(technique(x),'GCMS')) {
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TICdat <- TICdat %>%
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rowSums() %>%
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{tibble(Sample = 1:length(.),
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TIC = .,
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batch = ri[,by] %>% deframe() %>% factor())}
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} else {
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TICdat <- TICdat %>%
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map(rowSums) %>%
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bind_cols() %>%
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rowid_to_column(var = 'Sample') %>%
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mutate(batch = ri[,by] %>% unlist() %>% factor()) %>%
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gather('Mode','TIC',-batch,-Sample)
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if (!is.list(TICdat)){
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TICdat <- list(TICdat)
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}
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TICdat <- TICdat %>%
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map(rowSums)
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if (TICdat %>%
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names() %>%
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is.na() %>%
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any()) {
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names(TICdat) <- replace(names(TICdat),
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is.na(names(TICdat)),
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'NA')
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}
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TICdat <- TICdat %>%
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bind_cols() %>%
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rowid_to_column(var = 'Sample') %>%
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mutate(batch = ri[,by] %>%
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unlist() %>%
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factor()) %>%
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gather('Mode','TIC',-batch,-Sample)
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diff <- batchDiff(TICdat,pthresh)
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return(diff)
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})
@@ -242,6 +258,8 @@ batchDiff <- function(TICdat,pthresh = 0.05){
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#' @rdname detectPretreatmentParameters
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#' @description Detect pre-treatment parameters for `Binalysis` or `MetaboProfile` class objects.
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#' @param x S4 object of class `Binalysis`, `MetaboProfile` or `AnalysisData`
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#' @param cls the name of the sample information table column containing the sample class information
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#' @param QCidx QC sample class label
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#' @return S4 object of class `AnalysisParameters`
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#' @examples
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#' ## Retreive example file paths and sample information
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#' pp
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#' @export
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266-
setGeneric('detectPretreatmentParameters',function(x){
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setGeneric('detectPretreatmentParameters',function(x,
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cls = 'class',
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QCidx = 'QC'){
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standardGeneric('detectPretreatmentParameters')
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})
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#' @rdname detectPretreatmentParameters
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#' @importFrom metabolyseR changeParameter<-
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setMethod('detectPretreatmentParameters',signature = 'Binalysis',
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function(x){
274-
detectPretreatment(x)
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function(x,cls = 'class',QCidx = 'QC'){
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pp <- detectPretreatment(x)
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sample_info <- binneR::sampleInfo(x)
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299+
if (!detectQC(sample_info,cls,QCidx)){
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parameters(pp,'pre-treatment')$QC <- NULL
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} else {
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changeParameter(pp,'QCidx','pre-treatment') <- QCidx
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}
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changeParameter(pp,'cls','pre-treatment') <- cls
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return(pp)
275308
})
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#' @rdname detectPretreatmentParameters
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setMethod('detectPretreatmentParameters',signature = 'MetaboProfile',
280-
function(x){
281-
detectPretreatment(x)
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function(x,cls = 'class',QCidx = 'QC'){
314+
pp <- detectPretreatment(x)
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316+
sample_info <- profilePro::sampleInfo(x)
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318+
if (!detectQC(sample_info,cls,QCidx)){
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parameters(pp,'pre-treatment')$QC <- NULL
320+
} else {
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changeParameter(pp,'QCidx','pre-treatment') <- QCidx
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}
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changeParameter(pp,'cls','pre-treatment') <- cls
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return(pp)
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})
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#' @importFrom metabolyseR parameters<- parameters
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return(pre_treat_params)
315360
}
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362+
detectQC <- function(sample_info,cls,QCidx){
363+
any(str_detect(sample_info[[cls]],'QC'))
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}
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#' Detect modelling parameters
318367
#' @rdname detectModellingParameters
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#' @description Detect modelling parameters for `Binalysis`, `MetaboProfile` or `Analysis` S4 classes.
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setMethod('detectModellingParameters',signature = 'MetaboProfile',
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function(x){
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idx <- x %>%
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processingParameters() %>%
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.$info %>%
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.$cls
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374-
sample_information <- x %>%
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profilePro::sampleInfo() %>%
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select(all_of(idx)) %>%
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deframe()
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379-
detectModelling(sample_information,idx)
418+
idx <- x %>%
419+
processingParameters() %>%
420+
.$info %>%
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.$cls
422+
423+
sample_information <- x %>%
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profilePro::sampleInfo() %>%
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select(all_of(idx)) %>%
426+
deframe()
427+
428+
detectModelling(sample_information,idx)
380429
})
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#' @rdname detectModellingParameters

‎R/export.R

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@@ -88,10 +88,20 @@ setMethod('exportData',signature = 'MetaboProfile',
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file_paths <- pd %>%
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names() %>%
9090
map_chr(~{
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prefix <- .x
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93+
if (prefix != 'n' | prefix != 'p'){
94+
prefix <- ''
95+
} else {
96+
prefix <- switch(prefix,
97+
n = 'negative_mode_',
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p = 'positive_mode_')
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}
100+
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bind_cols(pd[[.x]],i %>% select(name)) %>%
92102
gather('Feature','Intensity',-name) %>%
93103
spread(name,Intensity) %>%
94-
exportCSV(str_c(outPath,'/',.x,'_mode_processed_data.csv'))
104+
exportCSV(str_c(outPath,'/',prefix,'processed_data.csv'))
95105
})
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97107
return(file_paths)

‎R/sanitise.R

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@@ -19,7 +19,7 @@ sanitiseTable <- function(x,maxRows = 5000,sigFig = 3,maxCharacters = 100){
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x <- x[seq_len(maxRows),]
2020
}
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22-
a <- map_df(x,~{
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x <- map_df(x,~{
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if (typeof(.x) == 'character'){
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limit_characters <- .x %>%
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nchar() %>%

‎R/summary.R

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@@ -21,28 +21,48 @@ setGeneric('featureSummary',function(x)
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standardGeneric('featureSummary'))
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2323
#' @rdname featureSummary
24-
#' @importFrom dplyr n_distinct
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2625
setMethod('featureSummary',signature = 'Binalysis',
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function(x){
2827
x %>%
29-
binnedData() %>%
30-
map(~{{
31-
.x %>%
32-
rowid_to_column(var = 'Sample') %>%
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gather('Feature','Intensity',-Sample)
34-
}}) %>%
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bind_rows() %>%
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mutate(Mode = str_sub(Feature,1,1)) %>%
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group_by(Mode) %>%
38-
summarise(
39-
`Number of bins` = n_distinct(Feature),
40-
`Missing Data (%)` = round(length(which(Intensity == 0)) /
41-
length(Intensity) * 100,2),
42-
.groups = 'drop') %>%
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{{
44-
.$Mode[.$Mode == 'n'] <- 'Negative'
45-
.$Mode[.$Mode == 'p'] <- 'Positive'
46-
.
47-
}}
28+
binnedData() %>%
29+
featSummary()
4830
})
31+
32+
#' @rdname featureSummary
33+
34+
setMethod('featureSummary',signature = 'MetaboProfile',
35+
function(x){
36+
x %>%
37+
processedData() %>%
38+
featSummary()
39+
})
40+
41+
#' @importFrom dplyr n_distinct
42+
43+
featSummary <- function(x){
44+
45+
if (!is.list(x)){
46+
x <- list(x)
47+
}
48+
49+
x %>%
50+
map(~{{
51+
.x %>%
52+
rowid_to_column(var = 'Sample') %>%
53+
gather('Feature','Intensity',-Sample)
54+
}}) %>%
55+
bind_rows() %>%
56+
mutate(Mode = str_sub(Feature,1,1)) %>%
57+
mutate(Mode = replace(Mode,
58+
Mode != 'n' & Mode != 'p',
59+
NA)) %>%
60+
group_by(Mode) %>%
61+
summarise(
62+
`Number of bins` = n_distinct(Feature),
63+
`Missing Data (%)` = round(length(which(Intensity == 0)) /
64+
length(Intensity) * 100,2),
65+
.groups = 'drop') %>%
66+
mutate(Mode = replace(Mode,Mode == 'n','Negative') %>%
67+
replace(Mode == 'p','Positive'))
68+
}

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