View source: R/tidyMS_R6_TransitionCorrelations.R
apply_to_response_matrix | R Documentation |
Apply function requiring a matrix to tidy table
apply_to_response_matrix(data, config, .func, .funcname = NULL)
data |
data.frame |
config |
AnalysisConfiguration |
.func |
function |
.funcname |
name of function (used for creating new column) |
Other preprocessing:
INTERNAL_FUNCTIONS_BY_FAMILY
,
filter_proteins_by_peptide_count()
,
get_robscales()
,
normalize_log2_robscale()
,
robust_scale()
,
scale_with_subset()
,
scale_with_subset_by_factors()
bb <- sim_lfq_data_peptide_config(Nprot = 100)
data <- bb$data
conf <- bb$config
res <- apply_to_response_matrix(data, conf, .func = base::scale)
stopifnot("abundance_base..scale" %in% colnames(res))
stopifnot("abundance_base..scale" == conf$table$get_response())
conf <- bb$config$clone(deep=TRUE)
conf$table$workIntensity <- "abundance"
res <- apply_to_response_matrix(data, conf$clone(deep=TRUE), .func = robust_scale)
# Normalize data using the vsn method from bioconductor
if( require("vsn")){
res <- apply_to_response_matrix(data, conf$clone(deep=TRUE), .func = vsn::justvsn)
}
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