From 035dc03315b043fde24272723846556b4bcb6e58 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Todor=20Kondi=C4=87?= <todor.kondic@uni.lu>
Date: Wed, 31 Aug 2022 12:35:12 +0200
Subject: [PATCH] api,mix: prescreen,gen_summ: Update generation of summ table.
---
R/api.R | 8 ++---
R/mix.R | 99 +++++++++++++++++++++++++++++++++++++--------------------
2 files changed, 68 insertions(+), 39 deletions(-)
diff --git a/R/api.R b/R/api.R
index f9c1059..9c78852 100644
--- a/R/api.R
+++ b/R/api.R
@@ -536,12 +536,10 @@ conf_trans <- function(conf) {
prescreen <- function(m) {
## Top-level auto prescreening function.
message("(prescreen): Start.")
- confpres <- conf_trans_pres(m$conf$prescreen)
+ ## confpres <- conf_trans_pres(m$conf$prescreen)
- m$qa <- create_qa_table(m$extr,confpres)
- m1 <- assess_ms1(m)
- m <- assess_ms2(m1)
- m$out$tab$summ <- gen_summ(m$out$tab$comp,m$qa$ms1,m$qa$ms2)
+ m$qa <- analyse_extracted_data(m$extr,m$conf$prescreen)
+ m$out$tab$summ <- gen_summ(m$out$tab$comp,m$qa)
message("(prescreen): End.")
m
}
diff --git a/R/mix.R b/R/mix.R
index 72a3b07..52aa6ac 100644
--- a/R/mix.R
+++ b/R/mix.R
@@ -177,40 +177,40 @@ gen_empty_summ <- function() {
EMPTY_SUMM
}
-gen_summ <- function(comp,qa_ms1,qa_ms2) {
- comp_cols <- intersect(SUMM_COLS,colnames(comp))
- summ <- comp[,..comp_cols]
- data.table::setkeyv(summ,BASE_KEY)
- ms1_cols <- intersect(SUMM_COLS,colnames(qa_ms1))
- ms1_cols <- setdiff(ms1_cols,colnames(summ))
- summ <- qa_ms1[summ,c(..comp_cols,..ms1_cols),on=BASE_KEY]
- ms2_cols <- intersect(colnames(qa_ms2),SUMM_COLS)
- ms2_cols <- setdiff(ms2_cols,colnames(summ))
- summ <- qa_ms2[summ,c(..comp_cols,..ms1_cols,..ms2_cols),on=BASE_KEY]
- data.table::setkeyv(summ,c(BASE_KEY_MS2,"an"))
- summ[,qa_ms1_exists:=the_ifelse(!is.na(qa_ms1_good_int),T,F)]
- summ[,qa_ms2_exists:=the_ifelse(!is.na(CE),T,F)]
- summ[,qa_pass:=apply(.SD,1,all),.SDcols=QA_FLAGS[!(QA_FLAGS %in% "qa_pass")]]
- summ$Comments<-""
- data.table::setkeyv(summ,DEF_KEY_SUMM)
- data.table::setcolorder(summ,SUMM_COLS)
-
- ## Quality scores for ms1 and ms2.
- summ[,qlt_ms1 := as.integer(Map(function(m1,m2,m3) { m1*5L + m2*3L + m3*2L},
- as.integer(qa_ms1_exists),
- as.integer(qa_ms1_above_noise),
- as.integer(qa_ms1_good_int)))]
- summ[,qlt_ms2 := as.integer(Map(function(m1,m2,m3) { m1*5L + m2*3L + m3*2L},
- as.integer(qa_ms2_exists),
- as.integer(qa_ms2_near),
- as.integer(qa_ms2_good_int)))]
-
- summ[is.na(qlt_ms1),qlt_ms1:=0L]
- summ[is.na(qlt_ms2),qlt_ms2:=0L]
-
- summ
+## gen_summ <- function(comp,qa_ms1,qa_ms2) {
+## comp_cols <- intersect(SUMM_COLS,colnames(comp))
+## summ <- comp[,..comp_cols]
+## data.table::setkeyv(summ,BASE_KEY)
+## ms1_cols <- intersect(SUMM_COLS,colnames(qa_ms1))
+## ms1_cols <- setdiff(ms1_cols,colnames(summ))
+## summ <- qa_ms1[summ,c(..comp_cols,..ms1_cols),on=BASE_KEY]
+## ms2_cols <- intersect(colnames(qa_ms2),SUMM_COLS)
+## ms2_cols <- setdiff(ms2_cols,colnames(summ))
+## summ <- qa_ms2[summ,c(..comp_cols,..ms1_cols,..ms2_cols),on=BASE_KEY]
+## data.table::setkeyv(summ,c(BASE_KEY_MS2,"an"))
+## summ[,qa_ms1_exists:=the_ifelse(!is.na(qa_ms1_good_int),T,F)]
+## summ[,qa_ms2_exists:=the_ifelse(!is.na(CE),T,F)]
+## summ[,qa_pass:=apply(.SD,1,all),.SDcols=QA_FLAGS[!(QA_FLAGS %in% "qa_pass")]]
+## summ$Comments<-""
+## data.table::setkeyv(summ,DEF_KEY_SUMM)
+## data.table::setcolorder(summ,SUMM_COLS)
+
+## ## Quality scores for ms1 and ms2.
+## summ[,qlt_ms1 := as.integer(Map(function(m1,m2,m3) { m1*5L + m2*3L + m3*2L},
+## as.integer(qa_ms1_exists),
+## as.integer(qa_ms1_above_noise),
+## as.integer(qa_ms1_good_int)))]
+## summ[,qlt_ms2 := as.integer(Map(function(m1,m2,m3) { m1*5L + m2*3L + m3*2L},
+## as.integer(qa_ms2_exists),
+## as.integer(qa_ms2_near),
+## as.integer(qa_ms2_good_int)))]
+
+## summ[is.na(qlt_ms1),qlt_ms1:=0L]
+## summ[is.na(qlt_ms2),qlt_ms2:=0L]
+
+## summ
-}
+## }
pp_touch_q<-function(summ) {
## Returns indices that are ok to be auto processed.
which(summ$checked==SUMM_CHK_NONE | summ$checked==SUMM_CHK_AUTO)
@@ -768,6 +768,7 @@ analyse_extracted_data <- function(extr,prescreen_param) {
## times was that it was helpful, at least sometimes.
tab_ms2[qa_ms1_good_int==T,qa_ms1_above_noise:=fifelse(ms1_int>ms1_mean/3.,T,F)]
tab_ms2[qa_ms1_good_int==T & qa_ms1_above_noise==T & qa_ms2_good_int==T,qa_ms2_near:=T]
+ tab_ms2$qa_ms2_exists=T
## Find MS1 with no corresponding MS2.
@@ -784,13 +785,43 @@ analyse_extracted_data <- function(extr,prescreen_param) {
## QA for the above (lazy qa ... take only the max peak into account).
tab_noms2[,c("qa_ms1_good_int","qa_ms1_above_noise"):=.(ms1_int>ms1_int_thresh,ms1_int>ms1_mean/3.)]
+ ## MS2 QA criteria all fail.
+ tab_noms2[,c("qa_ms2_exists","qa_ms2_good_int","qa_ms2_near"):=.(F,F,F)]
+
## Bind MS1-only and MS1/MS2 entries together.
- res <- rbind(tab_ms2,tab_noms2,fill=T)
+ res <- rbind(tab_ms2,tab_noms2,fill=T,use.names=T)
+
+ ## Every single entry which was extracted has at least MS1.
+ res[,qa_ms1_exists:=T]
data.table::setkeyv(res,BASE_KEY)
+
+ qflg <- QA_FLAGS[!(QA_FLAGS %in% "qa_pass")]
+ res[,qa_pass:=apply(.SD,1,all),.SDcols=qflg]
+ res[.(T),del_rt:=abs(ms2_rt - ms1_rt),on="qa_pass",by='an']
+ res[.(T),ms2_sel:=ms2_rt[which.min(del_rt)]==ms2_rt,on="qa_pass",by=BASE_KEY_MS2]
+ res[,qlt_ms1:=apply(.SD,1,function(rw) sum(c(5L,3L,2L)*rw)),.SDcol=c("qa_ms1_exists",
+ "qa_ms1_above_noise",
+ "qa_ms1_good_int")]
+ res[,qlt_ms2:=apply(.SD,1,function(rw) sum(c(5L,3L,2L)*rw)),.SDcol=c("qa_ms2_exists",
+ "qa_ms2_near",
+ "qa_ms2_good_int")]
res
}
+## Based on the `comprehensive' and `qa' tabs, greate `summ'.
+gen_summ <- function(comp,qa) {
+ comp_cols <- intersect(SUMM_COLS,colnames(comp))
+ rdcomp <- comp[,..comp_cols]
+ data.table::setkeyv(rdcomp,BASE_KEY)
+ summ <- qa[rdcomp,nomatch=NA]
+ flgs <- c(QA_FLAGS,"ms2_sel")
+ summ[is.na(qa_ms1_exists),(flgs):=F]
+ data.table::setkeyv(summ,c(BASE_KEY_MS2,'an'))
+ summ[.(F),c("qlt_ms1","qlt_ms2"):=0.,on="qa_ms1_exists"]
+ summ
+}
+
--
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