diff --git a/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.cpp b/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.cpp
index 660b63d0d7208184e64b47d6f1d853441e51230e..eeb9e88bb67915257c224b944579b207207f1127 100644
--- a/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.cpp
+++ b/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.cpp
@@ -51,6 +51,7 @@ McqrSdsXicWidget::McqrSdsXicWidget(McqrRunView *mcqr_window,
disableFutureStepsInToolBox();
addFactorsToListViews();
initGrantleeEngine();
+ m_mcqrLoadDataMode = p_mcqr_experiment.get()->getMcqrLoadDataMode();
}
McqrSdsXicWidget::~McqrSdsXicWidget()
@@ -221,7 +222,7 @@ McqrSdsXicWidget::initGrantleeEngine()
else
{
throw pappso::PappsoException(
- QObject::tr("currently, only fraction experiment can be run inside "
+ QObject::tr("currently, only fraction experiment can be run inside "
"X!TandemPipeline"));
}
}
@@ -236,7 +237,14 @@ McqrSdsXicWidget::changeStepAfterEndTag(QString end_name)
}
else if(end_name == "MCQREnd: filtering_data")
{
- configureReconstitutingTab();
+ if(m_mcqrLoadDataMode == McqrLoadDataMode::basic)
+ {
+ configureNormaliseTab();
+ }
+ else
+ {
+ configureReconstitutingTab();
+ }
}
else if(end_name == "MCQREnd: reconstiting_fraction")
{
diff --git a/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.h b/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.h
index c71688df5dadbeab2508cdbe93db12846ea78a5b..336430426538920365a23548a01f974fde86a18b 100644
--- a/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.h
+++ b/src/gui/mcqr_run_view/mcqr_sds_xic_widget/mcqrsdsxicwidget.h
@@ -102,4 +102,5 @@ class McqrSdsXicWidget : public McqrParamWidget
QStandardItemModel *mp_factorsLabelModel = nullptr;
QStandardItemModel *mp_factorsListModel = nullptr;
QStandardItemModel *mp_factorLevelsToRemove = nullptr;
+ McqrLoadDataMode m_mcqrLoadDataMode;
};
diff --git a/src/resources/templates/mcqr_scripts/mcqr_basic_xic_analysis.R b/src/resources/templates/mcqr_scripts/mcqr_basic_xic_analysis.R
index 514f6f79887b2084ca0c937819d2d6e0d4955667..8205640da0dfcd11bd42dcac903d5f6602487855 100644
--- a/src/resources/templates/mcqr_scripts/mcqr_basic_xic_analysis.R
+++ b/src/resources/templates/mcqr_scripts/mcqr_basic_xic_analysis.R
@@ -120,56 +120,18 @@
############# If necessary, removing dubious chromatographic data
# Removing peptides-mz showing too much variations of their retention time. These peptides-mz may occur from mis-identifications. Use the plot produced by the mcq.plot.rt.variability function to decide on the cut-off of standard deviation to use (e.g. here, 20 seconds).
- capture.output(XIC <- mcq.drop.variable.rt(XIC_clean, cutoff={{ cutoff_rt }}, file=stderr())
+ capture.output(XIC <- mcq.drop.variable.rt(XIC_clean, cutoff={{ cutoff_rt }}), file=stderr())
# Removing peptides-mz associated to very large chromatographic peaks. Use the plot produced by the mcq.plot.peak.width function to decide on the cut-off to use (e.g. here, 200 seconds). Don't be too stringent with this filter because the peak width is proportional to the peak height. A large peak may therefore simply correspond to a peptide-mz that is particularly intense in a given injection.
- capture.output(XIC <- mcq.drop.wide.peaks(XIC, cutoff={{ cutoff_peaks }}, file=stderr())
+ capture.output(XIC <- mcq.drop.wide.peaks(XIC, cutoff={{ cutoff_peaks }}), file=stderr())
############# Display of a summary of the 'XIC' object after removal of dubious peptides
cat("<h3>XIC summary after filtering</h3>")
- summary(XIC.SDS)
+ summary(XIC)
message("MCQREnd: filtering_data")
{% elif mcqr_step == 2%}
message("MCQRBegin: reconstiting_fraction")
- #****************************************************************************************************************
- # 3.3.2. Reconstituting samples from fractions
- #****************************************************************************************************************
- cat("<h3>Reconstituting samples from fractions</h3>")
- cat("<h4>XIC summary after track reconstituted</h4>")
- ############# Computing peptide intensities by track as the sum of the peptide intensities of all fractions for each track
- XIC.BY.TRACK <- mcq.compute.quantity.by.track(XIC.SDS)
- summary(XIC.BY.TRACK)
-
- ############# Checking intensity profiles for the peptide_mz belonging to the same protein
- ### pas posible pour l'instant
- # Display of the graph on the screen for 10 proteins
-# mcq.plot.peptide.intensity(XIC.BY.TRACK, flist=c({{ factor_list }}), rCutoff = {{ r_cutoff }}, showImputed = {{ show_imputed }}, nprot= {{ n_prot }}, log= {{ log }}, scale = {{ scale }})
-
- ############# Checking the distribution of the peptide intensities in the reconstituted samples (tracks). Are there samples showing odd distribution?
- cat("<h4>Peptide intensities distribution in the reconstituted samples</h4>")
- # Display of the graph on the screen
- svglite("{{ tmp_path }}/intensity_violin.svg", width=14, height=12)
- mcq.plot.intensity.violin(XIC.BY.TRACK, factorToColor=c({{ factor_color }}))
- capture.output(dev.off(), file=stderr())
- cat("<p><img src=\"{{ tmp_path }}/intensity_violin.svg\" /></p>")
-
-
- ############# Checking the global correlation between the peptide-mz intensities of one sample and the peptide-mz intensities of a sample chosen as reference. Are the correlation globally correct?
- cat("<h4>Plot global correlation between all tracks vs {{ track_level }}</h4>")
- # Display of the graph on the screen
- svglite("{{ tmp_path }}/intensity_correlation_%01d.svg", width=14, height=12)
- capture.output(mcq.plot.intensity.correlation(XIC.BY.TRACK, ref="{{ track_level }}"), file=stderr())
- capture.output(dev.off(), file=stderr())
-
- nb_tracks = length(XIC.BY.TRACK@metadata@metadata[["track"]]) - 1
- nb_pages = nb_tracks%/%4
- if(nb_tracks%%4 != 0) {
- nb_pages = nb_pages + 1
- }
- for(i in 1:nb_pages){
- cat(paste0("<p><img src=\"{{ tmp_path }}/intensity_correlation_", i, ".svg\" /></p>"))
- }
message("MCQREnd: reconstiting_fraction")
{% elif mcqr_step == 3 %}
@@ -181,7 +143,7 @@
cat("<h3>Normalizing peptide-mz intensities</h3>")
############# Normalizing peptide-mz intensities
# Normalization by a median-based method ("median" or "median.RT", based on a reference sample) or by a "percent" method. See help for details
- capture.output(XIC <- mcq.compute.normalization(XIC.BY.TRACK, ref="{{ track_ref }}", method="{{ norm_method }}"), file=stderr())
+ capture.output(XIC <- mcq.compute.normalization(XIC, ref="{{ track_ref }}", method="{{ norm_method }}"), file=stderr())
cat("<h4>Control the normalization effets</h4>")
############# Controling the effect of normalization on intensities distribution