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## Copyright (C) 2020 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.
##' @export
new_state <- function() {
m <- new_conf()
init_state(m)
}
##' @export
new_rv_state <- function() react_v(m=list2rev(new_state()))
##' @export
new_state_fn_conf <- function(fn_conf) {
m <- new_state()
m$conf <- read_conf(fn_conf)
init_state(m)
}
run <- function(fn_conf) {
m <- new_state_fn_conf(fn_conf)
dir.create(m$conf$project,
showWarnings = F,
recursive = T)
m <- withr::with_dir(new=m$conf$project,code = run_in_dir(m))
setup_phase <- function(m) {
##' @export
run_in_dir <- function(m) {
m <- setup_phase(m)
m <- sort_spectra(m)
m <- subset_summary(m)
m <- create_plots(m)
m <- save_plots(m)
load_compound_input <- function(m) {
coll <- list()
fields <- colnames(EMPTY_CMPD_LIST)
fns <- m$conf$compounds$lists
for (l in 1:length(fns)) {
fn <- fns[[l]]
# fnfields <- somehow read the file columns in
dt <- file2tab(fn, colClasses=c(ID="character",
SMILES="character",
Formula="character",
Name="character",
RT="numeric",
mz="numeric"))
# nonexist <- setdiff(fnfields,fields)
coll[[l]] <- dt #if (length(nonexist)==0) dt else dt[,(nonexist) := NULL]
coll[[l]]$ORIG <- fn
}
cmpds <- if (length(fns)>0) rbindlist(l=c(list(EMPTY_CMPD_LIST), coll), use.names = T, fill = T) else EMPTY_CMPD_LIST
dups <- duplicated(cmpds$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
load_data_input <- function(m) {
m$input$tab$mzml <- file2tab(m$conf$data)
m
}
##' @export
load_inputs <- function(m) {
m <- load_data_input(m)
m
}
mk_comp_tab <- function(m) {
setid <- m$input$tab$setid
cmpds<-m$input$tab$cmpds
setkey(cmpds,ID)
mzml[,`:=`(wd=sapply(Files,add_wd_to_mzml,m$conf$project))]
assert(nrow(cmpds)>0,msg="No compound lists have been provided.")
message("Begin generation of the comprehensive table.")
comp <- cmpds[setid,on="ID"][mzml,.(tag,adduct,ID,RT,set,Name,Files,wd,SMILES,Formula,mz,known),on="set",allow.cartesian=T]
tab2file(tab=comp,file=paste0("setidmerge",".csv"))
setkey(comp,known,set,ID)
## Known structure.
## comp[,`:=`(mz=mapply(calc_mz_from_smiles,SMILES,adduct,ID,USE.NAMES = F))]
comp[known=="structure",`:=`(mz=calc_mz_from_smiles(SMILES,adduct,ID))]
## Known formula.
comp[known=="formula",`:=`(mz=calc_mz_from_formula(Formula,adduct,ID))]
setnames(comp,names(COMP_NAME_MAP),
setcolorder(comp,COMP_NAME_FIRST)
tab2file(tab=comp,file=fn_out)
setkeyv(comp,c("set","tag","mz"))
m$out$tab$comp <- comp
verify_compounds <- function(conf) {
## * Existence of input files
fns_cmpds <- conf$compounds$lists
fn_cmpd_sets <- conf$compounds$sets
msg=paste("Cannot find the compound sets file:",fn_cmpd_sets))
for (fn in fns_cmpds) {
assert(isThingFile(fn), msg=paste("Cannot find compound list:",fn))
}
df_sets <- file2tab(fn_cmpd_sets)
all_sets<-unique(df_sets$set)
return(list(conf=conf,all_sets=all_sets))
}
verify_data_df <- function(mzml,all_sets) {
no_files <- which(mzml[,!file.exists(Files)])
no_adducts <- which(mzml[,!(adduct %in% names(ADDUCTMAP))])
no_sets <- which(mzml[,!(set %in% all_sets)])
assert(length(no_files)==0,msg = paste("Non-existent data files at rows:",paste(no_files,collapse = ',')))
assert(length(no_adducts)==0,msg = paste("Unrecognised adducts at rows:",paste(no_adducts,collapse = ',')))
assert(length(no_sets)==0,msg = paste("Unknown sets at rows:",paste(no_sets,collapse = ',')))
}
verify_data <- function(conf,all_sets) {
## * Existence of input files
fn_data <- conf$data
assert(isThingFile(fn_data),msg=paste("Data table does not exist:",fn_data))
mzml <- file2tab(fn_data)
verify_data_df(mzml=mzml,all_sets)
## 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.
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 (!m$conf$debug)
future::future
else {
message("Debug: futures evaluate as identity")
function(x,...) identity(x)
}
## 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.")
## Reduce the comp table to only unique masses (this is because
## different sets can have same masses).
m$out$tab$data <- m$out$tab$comp[,head(.SD,1),by=c('adduct','tag','ID')]
m$out$tab$data[,set:=NULL] #This column is meaningless now.
files <- m$out$tab$data[,unique(Files)]
allCEs <- do.call(c,args=lapply(files,function(fn) {
z <- MSnbase::readMSData(files=fn,msLevel = c(1,2),mode="onDisk")
unique(MSnbase::collisionEnergy(z),fromLast=T)
}))
allCEs <- unique(allCEs)
allCEs <- allCEs[!is.na(allCEs)]
cols <-paste('CE',allCEs,sep = '')
vals <- rep(NA,length(cols))
m$out$tab$data[,(cols) := .(rep(NA,.N))]
files <- m$out$tab$data[,unique(Files)]
ftags <- m$out$tab$data[,.(tag=unique(tag)),by=Files]
futuref <- m$future
tmp <- lapply(1:nrow(ftags),function(ii) {
fn <- ftags[ii,Files]
tag <- ftags[ii,tag]
tab <- as.data.frame(data.table::copy(m$out$tab$data[,.(Files,adduct,mz,rt,ID)]))
## err_ms1_eic <- m$extr$tol$eic
## err_coarse_fun <- m$extr$tol$coarse
## err_fine_fun <- m$extr$tol$fine
## err_rt <- m$extr$tol$rt
err_coarse <- m$conf$tolerance[["ms1 coarse"]]
err_fine <- m$conf$tolerance[["ms1 fine"]]
err_ms1_eic <- m$conf$tolerance$eic
err_rt <- m$conf$tolerance$rt
x <- futuref(extract(fn=fn,
tab=tab,
err_ms1_eic=err_ms1_eic,
err_coarse = err_coarse,
err_fine= err_fine,
err_rt= err_rt),
lazy = T)
msk <- sapply(tmp,future::resolved)
curr_done <- which(msk)
for (x in curr_done) {
message("Done extraction for ", unique(future::value(tmp[[x]])$Files))
}
while (!all(msk)) {
msk <- sapply(tmp,future::resolved)
newly_done <- which(msk)
for (x in setdiff(newly_done,curr_done)) {
message("Done extraction for ", unique(future::value(tmp[[x]])$Files))
}
Sys.sleep(0.5)
curr_done <- newly_done
}
ztmp <- lapply(tmp,future::value)
## ## We need to add in Files (after futures are resolved).
## for (nn in 1:nrow(ftags)) {
## fn <- ftags[nn,Files]
## ztmp[[nn]]$Files <- fn
## }
m$extr$ms <- data.table::rbindlist(ztmp)
fn_ex <- get_fn_extr(m)
message('Saving extracted data to ', fn_ex)
saveRDS(object = m$extr, file = fn_ex)
message('Done saving extracted data.')
timetag <- format(Sys.time(), "%Y%m%d_%H%M%S")
saveRDS(object = m, file = file.path(m$conf$project,
paste0(timetag,"_",FN_EXTR_STATE)))
##' @export
conf_trans <- function(conf) {
conf$prescreen <- conf_trans_pres(conf$prescreen)
conf
}
##' @export
prescreen <- function(m) {
## Top-level auto prescreening function.
confpres <- conf_trans_pres(m$conf$prescreen)
## TODO need to fix max spec intensity
gen_ms2_spec_tab <- function(ms) {data.table::rbindlist(lapply(1:nrow(ms), function (nr) {
adduct <- ms$adduct[[nr]]
ID <- ms$ID[[nr]]
Files <- ms$Files[[nr]]
spec <- ms$spec[[nr]]
ms2_sel <- ms$ms2_sel[[nr]]
dt <- if (length(spec[[1]]) < 3)
dtable(CE=NA_real_,
rt=NA_real_,
spec=list(dtable(mz=NA_real_,intensity=NA_real_)),
ms2_sel=NA) else {
dtable(
CE=sapply(spec,
function (x) x$CE),
rt=sapply(spec,
function (x) x$rt),
spec=lapply(spec,
function (x) x$spec),
ms2_sel=F)
}
if (!is.na(ms2_sel)) dt$ms2_sel[[ms2_sel]] <- T
dt$Files <- Files
dt$ID <- ID
dt$adduct <- adduct
dt[,ms2_max_int := .(sapply(spec,function (sp) sp[,max(intensity)]))]
dt
}))}
gen_ms1_spec_tab <- function(ms) {
cols <- MS1_SPEC_COLS
ms[,..cols]
}
m$qa <- create_qa_table(m$extr$ms,confpres)
mms1 <- assess_ms1(m)
m <- assess_ms2(mms1)
fields <- c("Files","adduct","ID",QA_COLS)
m$out$tab$ms2_spec <- gen_ms2_spec_tab(m$qa$ms)
m$out$tab$ms1_spec <- gen_ms1_spec_tab(m$qa$ms)
m$out$tab$summ <- merge(m$out$tab$comp,m$qa$ms[,..fields],by=c("Files","adduct","ID"))
data.table::setkeyv(m$out$tab$ms2_spec,c("adduct","Files","ID"))
data.table::setkeyv(m$out$tab$ms1_spec,c("adduct","Files","ID"))
##' Sets the key specified by DEF_KEY_SUMM and adds second indices,
##' either from DEF_INDEX_SUMM, or user-specified in
##' conf[["summary table"]]$order. The order entry is a list of
##' strings with names of columns in summ, optionally prefixed with a
##' minus(-) sign. Columns prefixed with the minus are going to be in
##' ascending order.
##'
##' @title Sort the Summary Table
##' @param m
##' @return m
##' @author Todor Kondić
## Sorts the summary table (summ) in order specified either in
## `order spectra` sublist of m$conf, or if that is null, the
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## DEF_INDEX_SUMM.
## Here set default sorting keys.
data.table::setkeyv(m$out$tab$summ,DEF_KEY_SUMM)
## Now, add secondary indexing.
cols <- if (!is.null(m$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: ",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
#' @export
create_plots <- function(m) {
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## Empty ms1_plot table.
## Select the data nedeed for plotting.
x <- m$out$tab$flt_summ
ms1_plot_data <- gen_base_ms1_plot_tab(summ=x,
ms1_spec=m$out$tab$ms1_spec)
group_data <- m$conf$figures$grouping
plot_group <- group_data$group
plot_plot <- group_data$plot
plot_label <- group_data$label
plot_index <- c(plot_group,plot_plot,plot_label)
## All the possible curve labels.
all_labels <- x[,unique(.SD),.SDcols=plot_label][[plot_label]]
## Plot styling.
style_eic_ms1 <- plot_decor(m,m$conf$logaxes$ms1_eic_int,
all_labels=all_labels,
legend_name=plot_label)
style_eic_ms2 <- plot_decor(m,m$conf$logaxes$ms2_eic_int,
all_labels=all_labels,
legend_name=plot_label)
style_spec_ms2 <- plot_decor(m,m$conf$logaxes$ms2_spec_int,
all_labels=all_labels,
legend_name = plot_label)
## Generate MS1 EIC plots.
ms1_plot <- ms1_plot_data[,.(fig={
df <- .SD[,data.table::rbindlist(Map(function (a,b,c,d) {
s <- a[[1]]
s$plot_label <- b
s$rt_peak <- c
s$mz <- d
s},
eicMS1,
.SD[[..plot_label]],
rt_peak,
mz))]
list(plot_eic_ms1(df,style_fun = style_eic_ms1,
plot_label = ..plot_label))
}),by=c(plot_group,plot_plot)]
m$out$tab$ms1_plot <- ms1_plot
m
}
create_plots_old <- function(m) {
## Helpers
textf <- ggplot2::element_text
x <- m$out$tab$ms1_spec
y <- m$out$tab$flt_summ
## Logarithmic, or linear y axis?
scale_y_ms1_eic <- if (shiny::isTruthy(m$conf$logaxes$ms1_eic_int))
ggplot2::scale_y_log10 else ggplot2::scale_y_continuous
scale_y_ms2_eic <- if (shiny::isTruthy(m$conf$logaxes$ms2_eic_int))
ggplot2::scale_y_log10 else ggplot2::scale_y_continuous
scale_y_ms2_spec <- if (shiny::isTruthy(m$conf$logaxes$ms2_spec_int))
ggplot2::scale_y_log10 else ggplot2::scale_y_continuous
scale_x <- function(...) ggplot2::scale_x_continuous(...,limits=DEFAULT_RT_RANGE)
## Colour palette.
tags <- y[,unique(tag)]
getpal <- colorRampPalette(RColorBrewer::brewer.pal(8,"Dark2"))
col_all_vals <- getpal(length(tags))
names(col_all_vals) <- tags
scale_colour <- function(values=col_all_vals,...) ggplot2::scale_colour_manual(values = values,name=m$conf$figures[["legend title"]],...)
rt_new_lim <- c(rt_in_min(m$conf$figures$rt_min),
rt_in_min(m$conf$figures$rt_max))
rt_lim <- get_coord_lim(rt_new_lim,DEFAULT_RT_RANGE)
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my_coord <- ggplot2::coord_cartesian(xlim = rt_lim)
conf_psub <- m$conf$figures[["plot subset"]]
psub <- if (!is.null(conf_psub)) conf_psub else FIG_DEF_SUBSET
assertthat::assert_that(all(psub %in% colnames(y)), msg = "Some plot subset columns are not recognised.")
mk_title<-function(txt, mass) paste(txt," ",
"m/z = ",
formatC(mass,format='f',digits=M_DIGITS),sep='')
mk_leg_lab<-function(tag,rt) {if (length(tag) > 0) paste(tag,"; rt= ",formatC(rt,format='f',digits=RT_DIGITS)," min",sep='') else character(0)}
sci10<-function(x) {ifelse(x==0, "0", parse(text=gsub("[+]", "", gsub("e", " %*% 10^", scales::scientific_format()(x)))))}
my_theme <- function (...) ggplot2::theme()
plot_eic_ms1 <- function(df) {
mz <- df[,unique(mz)]
ID <- df[,unique(ID)]
tbl <- df[,.(labs=mk_leg_lab(tag,rt_peak),tag=tag),by=c("tag","rt_peak")]
labs <- tbl[,labs]
tags <- tbl[,tag]
df[,tag:=factor(tag)]
ggplot2::ggplot(df,ggplot2::aes(x=rt,y=intensity,colour=tag)) +
ggplot2::geom_line(key_glyph=KEY_GLYPH) +
ggplot2::labs(x=CHR_GRAM_X,
y=CHR_GRAM_Y
## title=mk_title("EIC", mz),
## tag=ID
) +
scale_y_ms1_eic(labels=sci10) +
scale_colour(values=col_all_vals[as.character(tags)]) +
my_coord +
my_theme()
}
plot_eic_ms2 <- function(df) {
mz <- df[,unique(mz)]
ID <- df[,unique(ID)]
ddf <- df[!is.na(rt)==T]
## df[,tag:=factor(tag)]
ggplot2::ggplot(ddf,ggplot2::aes(x=rt,ymin=0,ymax=ms2_max_int,color=tag)) +
ggplot2::geom_linerange(key_glyph=KEY_GLYPH) +
ggplot2::labs(x=CHR_GRAM_X,
y=CHR_GRAM_Y
## title=mk_title("MS2 EIC for precursor",mz),
## tag=ID
) +
scale_y_ms2_eic(labels=sci10) +
scale_colour(values=col_all_vals[as.character(ddf$tag)]) +
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my_coord +
my_theme()
}
plot_spec_ms2 <- function(df) {
ddf <- df[ms2_sel == T]
mz <- ddf[,unique(mz)]
ID <- ddf[,unique(ID)]
tags <- ddf[,tag]
specs <- ddf[,spec]
rts <- ddf[,rt]
lst <- Map(function(d,t) {d$tag<-t;d},specs,tags)
data <- dtable(mz=numeric(0),intensity=numeric(0),tag=factor(0))
data <- rbind(data,
data.table::rbindlist(lst),
fill=T)
data <- data[!(is.na(mz)),]
leglabs <- mk_leg_lab(tags,rts)
ggplot2::ggplot(data,ggplot2::aes(x=mz,ymin=0,ymax=intensity,color=tag)) +
ggplot2::geom_linerange(key_glyph=KEY_GLYPH) +
ggplot2::labs(x="mz",
y="intensity"
## tag=ID,
## title=mk_title("MS2 spectrum for precursor",mz)
) +
scale_y_ms2_spec(labels=sci10) +
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scale_colour(values=col_all_vals[as.character(tags)]) +
my_theme()
}
## MS1
tmp <- y[x,.(set,adduct,Files,ID,tag,mz,rt_peak=i.ms1_rt,eicMS1=lapply(i.eicMS1,list)), on=c("adduct","Files","ID"),nomatch=NULL]
message("Start generating MS1 EICs.")
z <- tmp[,.(fig= {
df <- .SD[,data.table::rbindlist(Map(function (a,b,c) {
s <- a[[1]]
s$tag <- b
s$rt_peak <- c
s},eicMS1,tag,rt_peak))]
df$mz <- .SD[,unique(mz)]
df$ID <- .SD[,unique(ID)]
list(plot_eic_ms1(df))
}),by=psub,.SDcols=c("eicMS1","tag","mz","ID")]
message("Done generating MS1 EICs.")
structab <- z[,.(ID=unique(ID))]
structab <- m$out$tab$comp[known=="structure",][structab,.(ID=i.ID,SMILES=SMILES),on="ID",nomatch=NULL,mult="first"]
message("Start generating structures.")
structab[,structimg:=.(lapply(SMILES,function (sm) smiles2img(sm,width = 500,height = 500, zoom = 4.5)))]
message("Done generating structures.")
q <- structab[z,on="ID"][,c("structfig") := .(Map(function (st,eic) {
df <- eic[[1]]$data
ddf <- dtable(x=df$rt,
y=df$intensity)
ggplot2::ggplot(ddf) +
ggplot2::geom_blank() +
ggplot2::annotation_custom(st) +
ggplot2::theme_void()
},
structimg,
fig))]
m$out$tab$ms1_plot_eic <- q[,structimg:=NULL]
data.table::setkeyv(m$out$tab$ms1_plot_eic,c("set","adduct","ID"))
## MS2
x <- m$out$tab$ms2_spec
tmp <- y[x,.(set,adduct,Files,
ID=ID,tag=factor(tag),mz,CE=i.CE,
rt=i.rt,ms2_max_int,
spec=i.spec,
ms2_sel=i.ms2_sel), on = c("adduct","Files","ID")]
message("Start generating MS2 EICs.")
m$out$tab$ms2_plot <- tmp[,.(fig_eic = list(plot_eic_ms2(.SD)),
fig_spec = list(plot_spec_ms2(.SD))),
.SDcols=c("rt","ms2_max_int",
"tag","spec","ms2_sel","mz","ID"),
by = psub]
message("Done generating MS2 EICs.")
data.table::setkeyv(m$out$tab$ms2_plot,c("set","adduct","ID"))
m
}
#' @export
save_plots <- function(m) {
topdir <- FIG_TOPDIR
dir.create(topdir,showWarnings = F)
rt_lim <- DEFAULT_RT_RANGE
if (isTruthy(m$conf$figures$rt_min)) rt_lim[[1]] <- rt_in_min(m$conf$figures$rt_min)
if (isTruthy(m$conf$figures$rt_max)) rt_lim[[2]] <- rt_in_min(m$conf$figures$rt_max)
my_theme <- function(...) ggplot2::theme(legend.position = "none",...)
clean_range<-function(def,rng) {
x1 <- rng[1]
x2 <- rng[2]
if (is.na(x1) || x1 == 0) x1 <- def[1]
if (is.na(x2) || x2 == 0) x2 <- def[2]
c(x1,x2)
}
sets <- m$out$tab$flt_summ[,unique(set)]
for (s in sets) {
sdf <- m$out$tab$flt_summ[set==s,]
group <- sdf[,unique(adduct)]
for (g in group) {
asdf <- sdf[adduct==g,]
ids <- asdf[,unique(ID)]
for (id in ids) {
message("Image ","set: ",s," group: ", g, " id: ",id)
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tab <- asdf[ID==id,.(tag,ms1_int,ms1_rt,adduct,mz)]
ms1_figs <- m$out$tab$ms1_plot_eic[set==s & adduct==g & ID==id,.(fig,structfig)]
ms2_figs <- m$out$tab$ms2_plot[set==s & adduct==g & ID==id,.(fig_eic,fig_spec)]
ms1_eic <- ms1_figs$fig[[1]]
rt_rng <- range(ms1_eic$data[,rt])
if (!is.na(rt_lim[[1]])) rt_rng[[1]] <- rt_lim[[1]]
if (!is.na(rt_lim[[2]])) rt_rng[[2]] <- rt_lim[[2]]
my_coord <- ggplot2::coord_cartesian(xlim = rt_rng)
ms2_eic <- ms2_figs$fig_eic[[1]]+my_coord #ggplot2::coord_cartesian(xlim = rt_rng)
ms2_spec <- ms2_figs$fig_spec[[1]]
xxdf <- ms1_figs$fig[[1]]$data[,.(rt=rt,intensity=intensity)]
empty_fig <- ggplot2::ggplot(xxdf,ggplot2::aes(x=rt,y=intensity)) +
ggplot2::geom_blank() +
ggplot2::theme_void()
## if (id == 1078) browser()
if (NROW(ms2_eic$data) == 0) ms2_eic <- empty_fig
if (NROW(ms2_spec$data) == 0) ms2_spec <- empty_fig
leg <- cowplot::get_legend(ms1_eic)
big_fig <- cowplot::plot_grid(ms1_eic+my_theme(),
ms1_figs$structfig[[1]],
ms2_eic+my_theme(),
empty_fig,
ms2_spec+my_theme(),leg,
align = "hv",
axis='l',
ncol = 2,
nrow = 3,
rel_widths = c(2,1))
ggplot2::ggsave(plot=big_fig,filename = fig_path(top=topdir,
set=s,
group=g,
id=id,
suff="all"))
}
}
}
m
}
#' @export
report <- function(m) {
figtopdir <- FIG_TOPDIR #file.path(m$conf$project,FIG_TOPDIR)
pander::evalsOptions("graph.output","pdf")
author <- if (!is.null(m$conf$report$author)) m$conf$report$author else REPORT_AUTHOR
title <- if (!is.null(m$conf$report$title)) m$conf$report$title else REPORT_TITLE
doc <- pander::Pandoc$new(author,title)
doc$add(pander::pandoc.header.return("Plots",level = 1))
sets <- m$out$tab$flt_summ[,unique(set)]
rep_theme <- ggplot2::labs(title = NULL)
for (s in sets) {
doc$add(pander::pandoc.header.return(paste('Set', s), level = 2))
sdf <- m$out$tab$flt_summ[set==s,]
group <- sdf[,unique(adduct)]
for (g in group) {
asdf <- sdf[adduct==g,]
ids <- asdf[,unique(ID)]
for (id in ids) {
message("Image ","set: ",s," group: ", g, " id: ",id)
doc$add(pander::pandoc.header.return(paste('ID',id),level = 3))
tab <- asdf[ID==id,.(tag,ms1_int,ms1_rt,adduct,mz,Files)]
ms2info <- m$out$tab$ms2_spec[adduct==g & ID==id,.(Files,ID,rt,ms2_max_int)]
tab2 <- tab[ms2info,on="Files"][,.(tag,mz,adduct,"$RT_{ms1}$[min]"=ms1_rt,"$RT_{ms2}$[min]"=rt,"$I{ms1}$"=formatC(ms1_int, format="e",digits = 2), "$I(ms2)$"= formatC(ms2_max_int, format="e",digits = 2))]
data.table::setorderv(tab2,c("$I{ms1}$","$I(ms2)$"),c(-1,-1))
doc$add.paragraph("")
figpath <- fig_path(top=figtopdir,set=s,group=g,id=id,suff="all",ext="pdf")
doc$add(pander::pandoc.image.return(img=paste0("file:",figpath)))
doc$add.paragraph("")
message("Adding table.")
doc$add.paragraph(pander::pandoc.table.return(tab2))
message("Done adding table.")
## doc$add(print(tab))
doc$add.paragraph("")
}
}
}
doc$add(pander::pandoc.header.return("Appendix", level = 1))
doc$add(pander::pandoc.header.return("Configuration",level = 2))
doc$add(m$conf)
doc$add(pander::pandoc.header.return("R Session Info",level = 2))
doc$add(sessionInfo())
m$out$report <- doc
m$out$report$export('report.pdf')
m
}
#' @export
app <- function() {
unlink(list.files(pattern = "app_run.*html$"))
unlink(list.files(pattern = "app_run.*Rmd$"))
file.copy(system.file(file.path("rmd","app.Rmd"),package = "shinyscreen"),"app_run.Rmd")
rmarkdown::run(file = "app_run.Rmd")
}