## Copyright (C) 2020,2021,2023 by University of Luxembourg
## Licensed under the Apache License, Version 2.0 (the "License");
## you may not use this file except in compliance with the License.
## You may obtain a copy of the License at
## http://www.apache.org/licenses/LICENSE-2.0
## Unless required by applicable law or agreed to in writing, software
## distributed under the License is distributed on an "AS IS" BASIS,
## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
## See the License for the specific language governing permissions and
## limitations under the License.
#' @title Run Shinyscreen Pipeline
#'
#' @details TODO.
#'
#' @param envopts `envopts`, an `envopts` object.
#' @param project `character(1)`, a directory containing input data.
#' @param m `state`, a Shinyscreen state.
#' @param phases `character(n)`, a character vector of Shinyscreen
#' phases.
#' @param help `logical(1)`, print help?
#' @export
run <- function(envopts,
project="",
m=NULL,
phases=NULL,
help=F) {
all_phases=list(setup=setup_phase,
comptab=mk_comp_tab,
db=make_db,
extract=extr_data,
prescreen=prescreen,
sort=sort_spectra,
subset=subset_summary,
plot=create_plots)
if (help) {
message("(run): You can run some of the following, or all the phases:")
message(paste(paste0("(run): ",names(all_phases)),collapse = "\n"))
return(invisible(NULL))
}
the_phases <- if (is.null(phases)) all_phases else {
x <- setdiff(phases,names(all_phases))
if (length(x)>0) {
message("(run): Error. Unknown phases:")
message(paste(paste0("(run): ",x),collapse = "\n"))
stop("Aborting.")
}
all_phases[phases]
}
m = if (is.null(m)) {
## A project from scratch.
new_project(project,envopts=envopts)
} else {
## Regenerate the runtime environment.
project = if (nchar(project)==0L) m$run$project else project
m$run = new_runtime_state(project=project,
envopts = envopts,
conf=m$conf)
m
}
m = withr::with_dir(new=m$run$paths$project,code = Reduce(function (prev,f) f(prev),
x = the_phases,
init = m))
return(invisible(m))
}
##' @export
setup_phase <- function(m) {
message("Stage: setup")
m <- mk_tol_funcs(m)
m <- load_inputs(m)
m <- concurrency(m)
m
}
##' @export
run_in_dir <- function(m) {
m <- setup_phase(m)
m <- mk_comp_tab(m)
m <- extr_data(m)
m <- prescreen(m)
m <- sort_spectra(m)
m <- subset_summary(m)
m <- create_plots(m)
invisible(m)
}
##' @export
load_compound_input <- function(m) {
fns <- file.path(m$run$paths$project,m$conf$compounds$lists)
cmpds = join_compound_lists(fns)
## Process sets.
cmpds = process_cmpd_sets(cmpds)
dups <- duplicated(cmpds$ID)
dups <- dups | duplicated(cmpds$ID,fromLast = T)
dupIDs <- cmpds$ID[dups]
dupfns <- cmpds$ORIG[dups]
msg <- ""
for (fn in unique(dupfns)) {
inds <- which(dupfns %in% fn)
fndupID <- paste(dupIDs[inds], collapse = ',')
msg <- paste(paste('Duplicate IDs', fndupID,'found in',fn),msg,sep = '\n')
}
## TODO: Should we just kick out the duplicates, instead of
## erroring?
assert(all(!dups), msg = msg)
cmpds[,("known"):=.(the_ifelse(!is.na(SMILES),"structure",the_ifelse(!is.na(Formula),"formula","mz")))]
m$input$tab$cmpds <- cmpds
m
}
##' @export
load_data_input <- function(m) {
if (NROW(m$input$tab$mzml)==0L) {
if (!file.exists(m$run$paths$datatab)) {
stop("A CSV file with data file entries does not exist (`paths$datatab' in config).")
}
m$input$tab$mzml <- file2tab(m$run$paths$datatab)
} else {
message("Table `datatab' already loaded.")
}
m$input$tab$mzml <- as.data.table(m$input$tab$mzml)
assert(all(unique(m$input$tab$mzml[,.N,by=c("adduct","tag")]$N)<=1),msg="Some rows in the data table contain multiple entries with same tag and adduct fields.")
pref<-m$run$paths$data
message("load_data_input: ", pref)
for (fn in m$input$tab$mzml$file) {
if (!file.exists(file.path(pref,fn))) stop("File ",fn," does not exist.")
}
## m$input$tab$mzml[,file:=fifelse(file.exists(file),file,file.path(..pref,file))]
## m$input$tab$mzml[,file:=norm_path(file)]
m
}
##' @export
load_inputs <- function(m) {
m <- load_compound_input(m)
m <- load_data_input(m)
m
}
##' @export
mk_comp_tab <- function(m) {
message("Stage: comptab")
mzml<- m$input$tab$mzml
setkey(mzml,set)
cmpds<-m$input$tab$cmpds
setkey(cmpds,set,ID)
assert(nrow(cmpds)>0,msg="No compound lists have been provided.")
assert(all(mzml[,unique(set)] %in% cmpds[,unique(set)]),msg="Not all set names in the `datatab' data file table match those in the provided set list.")
assert(all(mzml[,!is.na(unique(adduct))]),msg="Some data file entries do not have selected adducts.")
message("Begin generation of the comprehensive table.")
comp <- cmpds[mzml,.(tag,adduct,ID,RT,set,Name,file,SMILES,Formula,mz,known),on="set",allow.cartesian=T]
tab2file(tab=comp,file=paste0("setidmerge",".csv"))
setkey(comp,known,set,ID)
message("Merged all sets.")
message("Calculate formulas from SMILES (if any). Please wait.")
## Get just the info needed for mz calculation.
comp_known <- comp[known=="structure" | known=="formula"]
## Remove mz==NA col from knowns.
comp_known[,mz:=NULL]
comp_unknown <- comp[known=="mz"]
smiforadd <- comp_known[known=="structure" | known=="formula",unique(.SD),.SDcols=c("adduct","ID","SMILES","Formula")]
## Turn SMILES into formulas.
smiles <- smiforadd[,unique(.SD),.SDcols=c("SMILES")]
smiles[,`:=`(Formula=smiles2form(SMILES))]
badsmiles <- as.character(smiles[Formula=="",SMILES])
if (length(badsmiles)>0) {
stop("Unable to create formula from SMILES:",paste(badsmiles,collapse="\n"))
}
smiforadd <- smiles[smiforadd,.(ID,SMILES,Formula,adduct),on=c("SMILES")]
data.table::setkey(smiforadd,"adduct","ID")
smiforadd[,Formula:=as.character(Formula)]
## Update the intermediate table with masses.
message("Formulas have been calculated. Start calculating masses from formulas.")
smiforadd[,mz:=calc_mz_from_formula(Formula,adduct,ID)]
message("Mass calculation has been completed.")
## Update the whole comprehensive table with masses from
## formulas. Doing it in a merge leaves a mess that has to be
## cleaned.
comp2 <- merge(comp_known,smiforadd,all.x = T, by= c("adduct","ID"))
## Take Formulas from smiforadd (y) and SMILES from comp (x).
comp2[,`:=`(Formula=Formula.y,SMILES=SMILES.x)]
## Now, populate mz from smiforadd (y) if SMILES/formula known,
## else take what was in the comp (x).
## comp2[,mz:=fifelse(known=="structure" | known=="formula",mz.y,mz.x)]
nms <- names(comp)
comp_known<-comp2[,..nms]
## In case you were wondering why is this all so complicated,
## well, for the moment I do not want to exclude mixed knowns and
## unknowns in the same run. The unknowns would have masses filled
## already at the stage of the compound list, so thay are taken
## from comp_unknown. Another twist is that mz can be calculated
## from either SMILES, or Formula.
## Combine knowns and unknowns finally.
comp <- rbind(comp_known,comp_unknown)
## Rename stuff to be renamed and reorder columns.
setnames(comp,names(COMP_NAME_MAP),
function(o) COMP_NAME_MAP[[o]])
setcolorder(comp,COMP_NAME_FIRST)
## Write it out.
fn_out <- get_fn_comp(m)
tab2file(tab=comp,file=fn_out)
message("Generation of comprehensive table finished.")
## Index for fast search and access.
setkeyv(comp,c("set","tag","mz"))
m$out$tab$comp <- comp
## TODO: Not tested on cases when there are both knowns and
## unknowns present in the compound lists. It *should* work
## though.
m
}
#' @export
concurrency <- function(m) {
## Reads the concurrency entry in the config. It is optional, if
## not given, then it is up to the user to define the plan of the
## futures package. If present, it contains at least the `plan'
## specification. It can also contain `workers` entry specifying
## the number of workers. If that entry is absent, the default
## number of workers is NO_WORKERS from the resources.R.
## TODO: Needs a rework to be useful. But, this is not a problem,
## because the user controls concurrency settings from the outside
## using future::plan.
## workers <- m$conf$concurrency$workers
## plan <- m$conf$concurrency$plan
## if (!is.null(plan) && plan!=user) {
## n <- if (!is.null(workers)) workers else NO_WORKERS
## if (!is.na(n)) future::plan(plan,workers=workers) else future::plan(plan)
## m$conf$concurrency$workers <- n
## } else {
## m$conf$concurrency$workers <- NA
## m$conf$concurrency$plan <- "user"
## }
## message("plan: ",m$conf$concurrency$plan)
## message("workers: ",m$conf$concurrency$workers)
## So we can actually debug.
m$future <- if (is.null(m$conf$debug) || !m$conf$debug)
future::future
else {
message("Debug: futures evaluate as identity")
function(x,...) identity(x)
}
m
}
#' @export
mk_tol_funcs <- function(m) {
## Depending on units given when the user specified the errors,
## generate functions that calculate errors given the concrete
## mass.
## Mass errors can be either in ppm, or Da.
## Time errors in min, or s.
## The mass error calculation functions and the retention time
## error in minutes are in m$extr$tol.
## TODO make these things compatible with futures.
m$extr$tol$coarse <- gen_mz_err_f(m$conf$tolerance[["ms1 coarse"]],
"ms1 coarse error: Only ppm, or Da units allowed."
)
m$extr$tol$fine <- gen_mz_err_f(m$conf$tolerance[["ms1 fine"]],
"ms1 fine error: Only ppm, or Da units allowed.")
m$extr$tol$eic <- gen_mz_err_f(m$conf$tolerance$eic,
"eic error: Only ppm, or Da units allowed.")
m$extr$tol$rt <- gen_rt_err(m$conf$tolerance$rt,
"rt error: Only s(econds), or min(utes) allowed.")
m
}
##' @export
extr_data <-function(m) {
fine = create_fine_table(m)
dpath = m$run$paths$data
## Open all files.
fns = fine[,unique(file)]
lms = lapply(fns,function(fn) read_data_file(file.path(dpath,fn)))
names(lms) = fns
## Load all feature data.
lfdata = lapply(lms,get_fdata)
names(lfdata) = fns
## Extract MS1 chromatograms using "fine" tolerance.
cgram_ms1 = fine[,extr_cgrams_ms1(lms[[file]],.SD,lfdata[[file]]),
by="file",
.SDcols=c("iso_fine_min",
"iso_fine_max",
"rt_min",
"rt_max",
"precid")]
setkey(cgram_ms1,file,precid)
## Extract MS2 chromatograms.
## Create the "coarse" table. Parent masses are known with
## "coarse". We will prefilter our ms2 results based on that...
coarse = create_coarse_table(m)
cgram_ms2 = data.table(precid=integer(0),
ce=numeric(0),
scan=character(0),
idx=integer(0),
rt=numeric(0),
intensity=numeric(0))
## Extract MS2 spectra.
spectra = empty_spectra_table()
for (fn in names(lfdata)) {
rtab = relate_ms2_to_precid(coarse=coarse[.(fn),on=.(file)],
ms2=lfdata[[fn]]$ms2,
cgram_ms1=cgram_ms1[.(fn),
on=.(file)])
sptab = extract_spectra(lms[[fn]],rtab)
cgram_ms2 = rbind(cgram_ms2,rtab)
spectra = rbind(spectra,sptab)
}
setkey(cgram_ms1,precid,rt)
setkey(cgram_ms2,precid,ce,rt)
setkey(spectra,precid,scan)
m$db$extr$cgm$ms1 = cgram_ms1
m$db$extr$cgm$ms2 = cgram_ms2
m$db$extr$spectra = spectra
m
}
##' @export
conf_trans <- function(conf) {
conf$prescreen <- conf_trans_pres(conf$prescreen)
conf
}
##' @export
prescreen <- function(m) {
## Top-level auto prescreening function.
message("(prescreen): Start.")
m$qa = NULL
m$out$tab$summ = NULL
m$qa = analyse_extracted_data(m$db,m$conf$prescreen)
m$out$tab$summ = gen_summ(m$db,m$qa,m$out$tab$comp)
message("(prescreen): End.")
m
}
##' Sets the key specified by DEF_KEY_SUMM and adds second indices,
##' either from DEF_INDEX_SUMM, or user-specified in
##' conf[["summary table"]]$order. The order entry is a list of
##' strings with names of columns in summ, optionally prefixed with a
##' minus(-) sign. Columns prefixed with the minus are going to be in
##' ascending order.
##'
##' @title Sort the Summary Table
##' @param m
##' @return m
##' @author Todor Kondić
##' @export
sort_spectra <- function(m) {
## Sorts the summary table (summ) in order specified either in
## `order spectra` sublist of m$conf, or if that is null, the
## DEF_INDEX_SUMM.
## Now, add secondary indexing.
cols <- if (!is.null(m$conf[["summary table"]]$order)) m$conf[["summary table"]]$order else DEF_INDEX_SUMM
idx <- gsub("^\\s*-\\s*","",cols) #We need only column names for
#now, so remove minuses where
#needed.
assertthat::assert_that(all(idx %in% colnames(m$out$tab$summ)),msg = "Some column(s) in order key in conf file does not exist in the summary table.")
data.table::setindexv(m$out$tab$summ,idx)
## Now we order based on either summary table order subkey, or
## DEF_ORDER_SUMM
tmp <- quote(data.table::setorder())
tmp[[2]] <- quote(m$out$tab$summ)
for (n in 1:length(cols)) tmp[[2+n]] <- parse(text=cols[[n]])[[1]]
message("Ordering expression: \n",deparse(tmp))
eval(tmp) #Execute the setorder call
m
}
##' Subsets the summary table by applying conditions set out in the
##' filter subkey of summary table key of the config. Each member of
##' filter is an expression that and all of them are chained together
##' using AND logical operation and applied to the summary table.
##'
##'
##' @title Subset the Summary Table
##' @param m
##' @return m
##' @author Todor Kondić
##' @export
subset_summary <- function(m) {
filt <- m$conf[["summary table"]]$filter
m$out$tab$flt_summ <- if (!is.null(filt)) {
tmp <- lapply(filt, function (x) parse(text = x)[[1]])
expr <- Reduce(function (x,y) {z<-call("&");z[[2]]<-x;z[[3]]<-y;z},x=tmp)
message("Filtering with: ",deparse(bquote(m$out$tab$summ[.(expr)])))
eval(bquote(m$out$tab$summ[.(expr)]))
} else m$out$tab$summ
m
}
#' @export
create_plots <- function(m) {
## Produce plots of EICs and spectra and group them acording to
## conf$figures$grouping.
## Select the data nedeed for plotting.
flt_summ <- m$out$tab$flt_summ
ms2 <- m$db$extr$cgm$ms2
ms1 <- m$db$extr$cgm$ms1
rt_min <- rt_in_min(m$conf$figures$rt_min)
rt_max <- rt_in_min(m$conf$figures$rt_max)
keytab <- flt_summ[,unique(.SD),.SDcol=c("adduct","ID")]
for (n in 1:NROW(keytab)) {
select <- dtable(adduct=keytab$adduct[[n]],
ID=keytab$ID[[n]])
pdata_ms1 <- data4plot_ms1_cgram(ms1,select)
pdata_ms2 <- data4plot_ms2_cgram(ms2,select)
pdata_spec <- data4plot_ms2_spec(ms2,flt_summ,select)
tabl_ms1 <- table_eic(pdata_ms1)
## tabl_ms2 <- table_eic(pdata_ms2) # For the moment, no real
# info to be had here.
tabl_spec <- table_spec(pdata_spec)
palette = plot_palette(pdata_ms1)
p_eic <- plot_eic_w_facet(pdata_ms1 = pdata_ms1,
pdata_ms2 = pdata_ms2,
rt_range = c(rt_min,rt_max),
palette = palette)
p_spec <- plot_spec_w_facet(pdata_ms2 = pdata_spec,
mz_range = c(NA_real_,NA_real_),
palette = palette)
## TODO
plot_save_single(p_eic,
decotab = select,
figtag = "eic",
proj_path = m$run$paths$project,
tabl = tabl_ms1,
extension = m$conf$figures$ext)
plot_save_single(p_spec,
decotab = select,
figtag = "spec",
proj_path = m$run$paths$project,
tabl = tabl_spec,
extension = m$conf$figures$ext)
message("Plotting of figure ",n," out of ",NROW(keytab)," has been completed.")
}
m
}
prepare_app <- function(dir_before,
envopts) {
## Information that needs to be availabe to the shiny server.
init <- list()
init$dir_before <- dir_before
init$envopts = envopts
## Create independent starting `home' for the server.
dir_start <- tempfile("shinyscreen")
dir.create(dir_start, recursive = T)
## Get list of app document contents.
dir_rmd = system.file("rmd",package = "shinyscreen")
fnms_rmd = list.files(dir_rmd,pattern = r"(^app.*\.Rmd$)",full.names=T)
## Copy startup files to that location.
dir.create(file.path(dir_start,'www'), showWarnings=F)
saveRDS(object = init,file=file.path(dir_start,"init.rds"))
file.copy(system.file(file.path("www","custom.css"),package = "shinyscreen"),file.path(dir_start,"www","custom.css"))
for (fn in fnms_rmd) file.copy(fn,file.path(dir_start,basename(fn)))
dir_start
}
#' @export
#' @title app
#' @param envopts `envopts`. Shinyscreen environment options.
#' @param shiny_args `list`, optional list of arguments conveyed to
#' `rmarkdown::run` `shiny_args` argument.
#' @param render_args `list`, optional list of arguments conveyed to
#' `rmarkdown::run` `render_args` argument.
#' @param metfrag_runtime `character(1)`, a location on the server side
#' of the MetFrag jar file.
#' @return Nada.
#' @author Todor Kondić
app <- function(envopts,
shiny_args=list(launch.browser=F),
render_args=NULL) {
dir_before = getwd()
message("dir_before: ", dir_before)
message("top_data_dir: ", envopts$top_data_dir)
message("projects: ", envopts$projects)
dir_start = prepare_app(dir_before=dir_before,
envopts=envopts)
on.exit(expr=setwd(dir_before))
setwd(dir_start)
rmarkdown::run(file = "app.Rmd", shiny_args = shiny_args, render_args = render_args)
}
#' @export
#' @title serve
#' @param envopts `envopts`, an `envopts` object.
#' @param user `character(1)`, subdir of usersdir.
#' @param host `character(1)`, optional, address where the page is
#' served.
#' @param port `integer(1)`, optional, port at which the page is
#' served.
#' @param top_data_dir `character(1)`, a location on the server side
#' containing data directories.
#' @param metfrag_db_dir `character(1)`, a location on the server side
#' containing MetFrag DBs.
#' @return Nada.
#' @author Todor Kondić
serve <- function(envopts,user,host='0.0.0.0',port=7777) {
shiny_args <- c(list(launch.browser=F),list(host=host,port=port))
projects <- file.path(envopts$users_dir,user)
if (!dir.exists(projects)) {
dir.create(projects)
message('Created projects: ',projects)
} else {
message('Using existing projects: ', projects)
}
app(shiny_args=shiny_args,
envopts=envopts)
}
#' @export
#' @title report
report <- function(m) {
report_author <- if (!is.null(m$conf$report$author)) m$conf$report$author else REPORT_AUTHOR
report_title <- if (!is.null(m$conf$report$title)) m$conf$report$title else REPORT_TITLE
fn_header <- system.file(file.path('rmd','report_header.rmd'),package = "shinyscreen")
fn_chunk <- system.file(file.path('rmd','report_chunk.rmd'), package = "shinyscreen")
dir.create(REP_TOPDIR,recursive = T,showWarnings = F)
header <- knitr::knit_expand(fn_header)
flt_summ <- m$out$tab$reptab
ms2 <- m$db$extr$cgm$ms2
ms1 <- m$db$extr$cgm$ms1
rt_min <- rt_in_min(m$conf$figures$rt_min)
rt_max <- rt_in_min(m$conf$figures$rt_max)
keytab <- flt_summ[,unique(.SD),.SDcol=c("adduct","ID")]
repdoc <- header
for (n in 1:NROW(keytab)) {
select <- dtable(adduct=keytab$adduct[[n]],
ID=keytab$ID[[n]])
pdata_ms1 <- data4plot_ms1_cgram(ms1,select)
pdata_ms2 <- data4plot_ms2_cgram(ms2,select)
pdata_spec <- data4plot_ms2_spec(ms2,flt_summ,select)
tabl_ms1 <- table_eic(pdata_ms1)
## tabl_ms2 <- table_eic(pdata_ms2) # For the moment, no real
# info to be had here.
tabl_spec <- table_spec(pdata_spec)
palette = plot_palette(pdata_ms1)
p_eic <- plot_eic_w_facet(pdata_ms1 = pdata_ms1,
pdata_ms2 = pdata_ms2,
rt_range = c(rt_min,rt_max),
palette = palette) + ggplot2::theme(legend.position = "bottom")
p_spec <- plot_spec_w_facet(pdata_ms2 = pdata_spec,
mz_range = c(NA_real_,NA_real_),
palette = palette) + ggplot2::theme(legend.position = "bottom")
eic_things <- plot_save_single(p_eic,
decotab = select,
figtag = "eic",
proj = m$conf$project,
tabl = tabl_ms1,
subdir = REP_TOPDIR,
extension = m$conf$figures$ext)
spec_things <- plot_save_single(p_spec,
decotab = select,
figtag = "spec",
proj = m$conf$project,
tabl = tabl_spec,
subdir = REP_TOPDIR,
extension = m$conf$figures$ext)
report_chunk_header <- paste0("Adduct: ",keytab$adduct[[n]],"; ",
"ID: ",keytab$ID[[n]])
report_tab_eic_cap <- sprintf("EIC for %s",keytab$ID[[n]])
report_fn_eic <- eic_things$fn_plot
report_tab_eic <- if (shiny::isTruthy(eic_things$tab)) knitr::kable(eic_things$tab, caption = report_tab_eic_cap) else ""
report_tab_spec_cap <- sprintf("Spectrum for %s",keytab$ID[[n]])
report_fn_spec <- spec_things$fn_plot
report_tab_spec <- if (shiny::isTruthy(eic_things$tab)) knitr::kable(spec_things$tab, caption = report_tab_spec_cap) else ""
repdoc <- c(repdoc,knitr::knit_expand(fn_chunk))
message("(report) Knitting of chunk ",n," out of ",NROW(keytab)," has been completed.")
}
fn_rep <- file.path(m$run$paths$project,"report.Rmd")
message("(report) Writing Rmd...")
cat(repdoc,file=fn_rep,sep = "\n")
message("(report) ...done.")
message("(report) Render start ...")
rmarkdown::render(fn_rep,output_dir = m$run$paths$project)
message("(report) ...done.")
m
}
#' @title Initialise Shinyscreen Configuration
#' @details This function is used to inform `shinyscreen` about the
#' working environment. If argument `save` is T, the configuration
#' will be memorised. Subsequent calls to `init` without arguments
#' will just load the configuration. If `merge` argument is T, the
#' resulting configuration object is going to be a merge between
#' the new parameters and the memorised ones. Those arguments not
#' mentioned in the argument list will be remembered from the save
#' config.
#' @inheritParams empty_envopts
#' @param merge `logical(1)`, optional. If T, merge with saved
#' configuration.
#' @param save `logical(1)`, optional. If T, save configuration,
#' otherwise just return the Shinyscreen environment options.
#' @param conf_dir `character(1)`, optional. Place where the
#' configuration resides. Changing this usually only makes sense
#' for testing.
#' @return An `envopts` object.
#' @author Todor Kondić
#' @export
init <- function(projects=NULL,
top_data_dir=NULL,
users_dir=NULL,
metfrag_db_dir=NULL,
metfrag_jar=NULL,
java_bin=NULL,
metfrag_max_proc=NULL,
no_structure_plots=NULL,
merge=T,
save=F,
conf_dir=tools::R_user_dir(package="shinyscreen",
which="config")) {
## The function will usually return a merge between the saved
## configuration and arguments provided by the user. This is why
## we need to know which arguments have been actually changed by
## the user. In order to do this, all `envopts'-like arguments to
## `init' have been created with an illegal default value, NULL.
## Therefore, any argument with an user-supplied _valid_ value
## will be non-NULL. We can browse through the function
## environment, pick out these non-NULLs and then override the
## saved config with them.
## So, get the function environment.
env = environment()
## Get the list of all possible arguments from the simpletst
## `envopts' constructor.
eargs = formalArgs(empty_envopts)
## Check which are NULLs and retain only those which are
## not.
evals = lapply(eargs,function(ca) env[[ca]])
ennull = sapply(evals,is.null,USE.NAMES=F)
eargs = eargs[!ennull]
evals = evals[!ennull]
names(evals) = eargs
## Now call the empty envopts constructor only with non-NULL
## arguments.
enew = do.call(empty_envopts,evals)
## If merging should occur.
if (merge) {
## Get saved values.
eold = load_envopts(dir=conf_dir)
if (length(eold)>0L) {
## First, merge on non-metfrag keys. Overwrite only NULLs.
simplekeys = setdiff(names(enew),"metfrag")
for (a in simplekeys) {
if (is.null(enew[[a]])) enew[[a]] = eold[[a]]
}
## Now, metfrag. Do the same.
mfkeys = names(enew$metfrag)
for (a in mfkeys) {
if (is.null(enew$metfrag[[a]])) enew$metfrag[[a]] = eold$metfrag[[a]]
}
}
}
## Replace the remaining NULL values with actual defaults.
e = seal_envopts(enew)
if (save) save_envopts(o=e,dir=conf_dir)
e
}
#' @title Run MetFrag
#' @details
#' @param m `state`, a state object.
#' @return m
#' @author Todor Kondić
metfrag <- function(m) {
stagtab = metfrag_get_stag_tab(m$out$tab$summ[ms2_sel==T])
ftab = metfrag_run(param = m$run$metfrag$param,
path = m$run$metfrag$path,
subpaths = m$run$metfrag$subpaths,
db_file = m$run$metfrag$db_file,
stag_tab = stagtab, ms2 = m$db$extr$spectra,
runtime=m$run$metfrag$runtime,
java_bin=m$run$metfrag$java_bin,
nproc = m$conf$metfrag$nproc)
tab = summarise_metfrag_results(param = m$conf$metfrag$param,
path = m$run$metfrag$path,
subpaths = m$run$metfrag$subpaths,
cand_parameters = m$conf$metfrag$cand_parameters,
db_scores = m$conf$metfrag$database_scores,
int_scores = m$conf$metfrag$intrinsic_scores,
collect_candidates= m$conf$metfrag$collect_candidates,
file_tab = ftab)
fn = file.path(m$run$metfrag$path,"metfrag_summary.csv")
data.table::fwrite(x=tab,file=fn,quote=T)
m
}
make_db <- function(m) {
m = make_db_catalogue(m)
m = make_db_precursors(m)
m
}