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univariate.R
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univariate.R
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#' ANOVA
#' @rdname anova
#' @description One-way analysis of variance (ANOVA).
#' @param x S4 object of class `AnalysisData`
#' @param cls a vector of sample info column names to analyse
#' @param pAdjust p value adjustment method
#' @param comparisons list of comparisons to perform
#' @param returnModels should models be returned
#' @examples
#' library(metaboData)
#'
#' d <- analysisData(abr1$neg[,200:300],abr1$fact)
#'
#' ## Perform ANOVA
#' anova_analysis <- anova(d,cls = 'day')
#'
#' ## Extract significant features
#' explanatoryFeatures(anova_analysis)
#' @export
setGeneric("anova",
function(
x,
cls = 'class',
pAdjust = 'bonferroni',
comparisons = list(),
returnModels = FALSE)
standardGeneric("anova"))
#' @rdname anova
#' @importFrom dplyr bind_rows
#' @importFrom broom tidy
setMethod('anova',signature = 'AnalysisData',
function(x,
cls = 'class',
pAdjust = 'bonferroni',
comparisons = list(),
returnModels = FALSE){
d <- x %>%
dat()
i <- x %>%
sinfo() %>%
select(all_of(cls))
clsFreq <- i %>%
group_by_all() %>%
summarise(n = n(),.groups = 'drop')
if (TRUE %in% (clsFreq$n < 3)) {
clsRem <- clsFreq %>%
filter(n < 3)
x <- x %>%
removeClasses(cls = cls,classes = clsRem$class)
warning(
str_c('Classes with < 3 replicates removed: ',
str_c(str_c('"',
clsRem$class,'"'),
collapse = ', ')),
call. = FALSE)
i <- x %>%
sinfo() %>%
select(all_of(cls))
}
if (length(comparisons) > 0) {
comp <- comparisons
} else {
comp <- map(i,~{unique(.) %>% sort() %>%
str_c(collapse = '~')})
}
models <- comp %>%
names() %>%
map(~{
pred <- .
ps <- comp[[pred]]
r <- ps %>%
future_map(~{
pc <- str_split(.x,'~')[[1]]
pad <- removeClasses(x,pred,classes = sinfo(x) %>%
select(all_of(pred)) %>%
unlist() %>%
unique() %>%
.[!(. %in% pc)])
response <- pad %>%
sinfo() %>%
select(all_of(pred)) %>%
unlist() %>%
factor()
pad %>%
dat() %>%
map(~{
aov(. ~ response)
})
}) %>%
set_names(comp[[pred]])
return(r)
}) %>%
set_names(names(comp))
results <- models %>%
map(~{
map(.,~{
map(.,tidy) %>%
bind_rows(.id = 'feature') %>%
mutate(adjusted.p.value = p.adjust(p.value,
method = pAdjust))
}) %>%
bind_rows(.id = 'comparison')
}) %>%
bind_rows(.id = 'response') %>%
filter(term == 'response')
res <- new('Univariate')
res@type <- 'ANOVA'
dat(res) <- dat(x)
sinfo(res) <- sinfo(x)
res@results <- results
if (returnModels == TRUE) {
res@models <- models
}
return(res)
}
)
#' Welch's t-test
#' @rdname ttest
#' @description Welch's t-test
#' @param x S4 object of class AnalysisData
#' @param cls vector of sample information column names to analyse
#' @param pAdjust p value adjustment method
#' @param comparisons named list of binary comparisons to analyse
#' @param returnModels should models be returned
#' @return An S4 object of class `Univariate`.
#' @examples
#' library(metaboData)
#'
#' d <- analysisData(abr1$neg[,200:300],abr1$fact) %>%
#' keepClasses(cls = 'day',classes = c('H','5'))
#'
#' ## Perform t-test
#' ttest_analysis <- ttest(d,cls = 'day')
#'
#' ## Extract significant features
#' explanatoryFeatures(ttest_analysis)
#' @export
setGeneric("ttest",
function(
x,
cls = 'class',
pAdjust = 'bonferroni',
comparisons = list(),
returnModels = FALSE)
standardGeneric("ttest"))
#' @rdname ttest
#' @importFrom dplyr bind_rows
#' @importFrom broom glance
setMethod('ttest',signature = 'AnalysisData',
function(x,
cls = 'class',
pAdjust = 'bonferroni',
comparisons = list(),
returnModels = FALSE){
if (length(comparisons) > 0) {
pw <- comparisons
} else {
pw <- cls %>%
map(binaryComparisons,x = x) %>%
set_names(cls)
}
models <- pw %>%
names() %>%
map(~{
pred <- .
ps <- pw[[pred]]
r <- ps %>%
future_map(~{
pc <- str_split(.x,'~')[[1]]
pad <- removeClasses(x,pred,classes = sinfo(x) %>%
select(all_of(pred)) %>%
unlist() %>%
unique() %>%
.[!(. %in% pc)])
response <- pad %>%
sinfo() %>%
select(all_of(pred)) %>%
unlist() %>%
factor()
pad %>%
dat() %>%
map(~{
t.test(. ~ response)
})
}) %>%
set_names(pw[[pred]])
return(r)
}) %>%
set_names(names(pw))
results <- models %>%
map(~{
map(.,~{
map(.,glance) %>%
bind_rows(.id = 'feature') %>%
mutate(adjusted.p.value = p.adjust(p.value,
method = pAdjust))
}) %>%
bind_rows(.id = 'comparison')
}) %>%
bind_rows(.id = 'response')
res <- new('Univariate')
res@type <- 't-test'
dat(res) <- dat(x)
sinfo(res) <- sinfo(x)
res@results <- results
if (returnModels == TRUE) {
res@models <- models
}
return(res)
}
)
#' Linear regression
#' @rdname linearRegression
#' @description Linear regression
#' @param x S4 object of class `AnalysisData`
#' @param cls vector of sample information column names to regress
#' @param pAdjust p value adjustment method
#' @param returnModels should models be returned
#' @return An S4 object of class `Univariate`.
#' @examples
#' library(metaboData)
#'
#' d <- analysisData(abr1$neg[,200:300],abr1$fact)
#'
#' ## Perform linear regression
#' lr_analysis <- linearRegression(d,cls = 'injorder')
#'
#' ## Extract significant features
#' explanatoryFeatures(lr_analysis)
#' @export
setGeneric("linearRegression",
function(
x,
cls = 'class',
pAdjust = 'bonferroni',
returnModels = FALSE)
standardGeneric("linearRegression"))
#' @rdname linearRegression
setMethod('linearRegression',signature = 'AnalysisData',
function(x,
cls = 'class',
pAdjust = 'bonferroni',
returnModels = FALSE){
indep <- x %>%
sinfo() %>%
select(all_of(cls))
if (FALSE %in%
(map_chr(indep,class) %in% c('integer','numeric'))) {
stop('Independent variables need to be numeric',
call. = FALSE)
}
d <- x %>%
dat()
models <- indep %>%
colnames() %>%
map(~{
i <- .
pred <- indep %>%
select(all_of(i)) %>%
unlist()
d %>%
map(~{
lm(. ~ pred)
})
}) %>%
set_names(colnames(indep))
results <- models %>%
map(~{
map(.,~{
glance(.)
}) %>%
bind_rows(.id = 'feature') %>%
mutate(adjusted.p.value = p.adjust(p.value,
method = pAdjust))
}) %>%
bind_rows(.id = 'response')
res <- new('Univariate')
res@type <- 'linear regression'
dat(res) <- dat(x)
sinfo(res) <- sinfo(x)
res@results <- results
if (returnModels == TRUE) {
res@models <- models
}
return(res)
}
)