From dc9ae94a7d07f6f2de6e7a6e5b7485466254b2b5 Mon Sep 17 00:00:00 2001
From: Philippine Louail <127301965+philouail@users.noreply.github.com>
Date: Mon, 28 Oct 2024 14:36:04 +0100
Subject: [PATCH] Update xcms.Rmd

---
 vignettes/xcms.Rmd | 110 +++++++++++++++++++++------------------------
 1 file changed, 51 insertions(+), 59 deletions(-)

diff --git a/vignettes/xcms.Rmd b/vignettes/xcms.Rmd
index 3f59a2e9..e2f6c5b7 100644
--- a/vignettes/xcms.Rmd
+++ b/vignettes/xcms.Rmd
@@ -62,7 +62,10 @@ be applied to the older *MSnbase*-based workflows (xcms version 3). Additional
 documents and tutorials covering also other topics of untargeted metabolomics
 analysis are listed at the end of this document. There is also a [xcms
 tutorial](https://jorainer.github.io/xcmsTutorials) available with more examples
-and details.
+and details. 
+To get a complete overview of LCMS-MS analysis, an end-to-end workflow
+[Metabonaut website](https://rformassspectrometry.github.io/metabonaut/), which 
+integrate the *xcms* preprocessing steps with the downstream analysis, is available. 
 
 
 # Preprocessing of LC-MS data
@@ -1180,55 +1183,6 @@ defined above.  The `filter` argument can accommodate various types of input,
 each determining the specific type of quality assessment and filtering to be
 performed.
 
-The `RsdFilter` enable users to filter features based on their relative
-standard deviation (coefficient of variation) for a specified `threshold`. It
-is recommended to base the computation on quality control (QC) samples,
-as demonstrated below:
-
-```{r}
-# Set up parameters for RsdFilter
-rsd_filter <- RsdFilter(threshold = 0.3,
-                        qcIndex = sampleData(faahko)$sample_type == "QC")
-
-# Apply the filter to faakho object
-filtered_faahko <- filterFeatures(object = faahko, filter = rsd_filter)
-
-# Now apply the same strategy to the res object
-rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = res$sample_type == "QC")
-filtered_res <- filterFeatures(object = res, filter = rsd_filter, assay = "raw")
-```
-
-All features with an RSD (CV) strictly larger than 0.3 in QC samples were thus
-removed from the data set.
-
-The `DratioFilter`  can be used to filter features based on the D-ratio or
-*dispersion ratio*, which compares the standard deviation in QC samples to that
-in study samples.
-
-```{r}
-# Set up parameters for DratioFilter
-dratio_filter <- DratioFilter(
-    threshold = 0.5,
-    qcIndex = sampleData(filtered_faahko)$sample_type == "QC",
-    studyIndex = sampleData(filtered_faahko)$sample_type == "study")
-
-# Apply the filter to faahko object
-filtered_faakho <- filterFeatures(object = filtered_faahko,
-                                  filter = dratio_filter)
-
-# Now same but for the res object
-dratio_filter <- DratioFilter(
-    threshold = 0.5,
-    qcIndex = filtered_res$sample_type == "QC",
-    studyIndex = filtered_res$sample_type == "study")
-
-filtered_res <- filterFeatures(object = filtered_res,
-                               filter = dratio_filter)
-```
-
-All features with an D-ratio strictly larger than 0.5 were thus removed from
-the data set.
-
 The `PercentMissingFilter` allows to filter features based on the percentage of
 missing values for each feature. This function takes as an input the parameter
 `f` which is supposed to be a vector of length equal to the length of the object
@@ -1276,16 +1230,54 @@ samples. More information can be found in the documentation of the filter:
 ?BlankFlag
 ```
 
-## Normalization
+The `RsdFilter` enable users to filter features based on their relative
+standard deviation (coefficient of variation) for a specified `threshold`. It
+is recommended to base the computation on quality control (QC) samples,
+as demonstrated below:
+
+```{r}
+# Set up parameters for RsdFilter
+rsd_filter <- RsdFilter(threshold = 0.3,
+                        qcIndex = sampleData(faahko)$sample_type == "QC")
+
+# Apply the filter to faakho object
+filtered_faahko <- filterFeatures(object = faahko, filter = rsd_filter)
+
+# Now apply the same strategy to the res object
+rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = res$sample_type == "QC")
+filtered_res <- filterFeatures(object = res, filter = rsd_filter, assay = "raw")
+```
+
+All features with an RSD (CV) strictly larger than 0.3 in QC samples were thus
+removed from the data set.
+
+The `DratioFilter`  can be used to filter features based on the D-ratio or
+*dispersion ratio*, which compares the standard deviation in QC samples to that
+in study samples.
+
+```{r}
+# Set up parameters for DratioFilter
+dratio_filter <- DratioFilter(
+    threshold = 0.5,
+    qcIndex = sampleData(filtered_faahko)$sample_type == "QC",
+    studyIndex = sampleData(filtered_faahko)$sample_type == "study")
+
+# Apply the filter to faahko object
+filtered_faakho <- filterFeatures(object = filtered_faahko,
+                                  filter = dratio_filter)
+
+# Now same but for the res object
+dratio_filter <- DratioFilter(
+    threshold = 0.5,
+    qcIndex = filtered_res$sample_type == "QC",
+    studyIndex = filtered_res$sample_type == "study")
+
+filtered_res <- filterFeatures(object = filtered_res,
+                               filter = dratio_filter)
+```
 
-Normalizing features' signal intensities is required, but at present not (yet)
-supported in `xcms` (some methods might be added in near future). It is advised
-to use the `SummarizedExperiment` returned by the `quantify()` method for any
-further data processing, as this type of object stores feature definitions,
-sample annotations as well as feature abundances in the same object. For the
-identification of e.g. features with significant different
-intensities/abundances it is suggested to use functionality provided in other R
-packages, such as Bioconductor's excellent *limma* package.
+All features with an D-ratio strictly larger than 0.5 were thus removed from
+the data set.
 
 
 ## Alignment to an external reference dataset