diff --git a/vignettes/xcms.Rmd b/vignettes/xcms.Rmd
index ba16810b..cf405eca 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
@@ -1230,6 +1233,52 @@ 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 `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
+(i.e. number of samples) with the sample type for each. The function then
+computes the percentage of missing values per sample groups and filters
+features based on this. Features with a percent of missing values larger than
+the threshold in all sample groups will be removed. Another option is to base
+this quality assessment and filtering only on QC samples.
+
+Both examples are shown below:
+
+```{r}
+# To set up parameter `f` to filter only based on QC samples
+f <- sampleData(faahko)$sample_type
+f[f != "QC"] <- NA
+
+# To set up parameter `f` to filter per sample type excluding QC samples
+f <- sampleData(faahko)$sample_type
+f[f == "QC"] <- NA
+
+missing_filter <- PercentMissingFilter(threshold = 30,
+                                       f = f)
+# Apply the filter to faakho object
+filtered_faahko <- filterFeatures(object = faahko,
+                                  filter = missing_filter)
+
+# Apply the filter to res object
+missing_filter <- PercentMissingFilter(threshold = 30,
+                                       f = f)
+filtered_res <- filterFeatures(object = res,
+                               filter = missing_filter)
+```
+
+Here, no feature was removed, meaning that all the features had less than 30%
+of `NA` values in at least one of the sample type.
+
+Although not directly relevant to this experiment, the `BlankFlag` filter can be
+used to flag features based on the intensity relationship between blank and QC
+samples. More information can be found in the documentation of the filter:
+
+```{r}
+# Retrieve documentation for the main function and the specific filter.
+?filterFeatures
+?BlankFlag
+```
+
 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,
@@ -1238,14 +1287,14 @@ as demonstrated below:
 ```{r}
 # Set up parameters for RsdFilter
 rsd_filter <- RsdFilter(threshold = 0.3,
-                        qcIndex = sampleData(faahko)$sample_type == "QC")
+                        qcIndex = sampleData(filtered_faahko)$sample_type == "QC")
 
 # Apply the filter to faakho object
-filtered_faahko <- filterFeatures(object = faahko, filter = rsd_filter)
+filtered_faahko <- filterFeatures(object = filtered_faahko, filter = rsd_filter)
 
 # Now apply the same strategy to the res object
-rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = res$sample_type == "QC")
-filtered_res <- filterFeatures(object = res, filter = rsd_filter, assay = "raw")
+rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = filtered_res$sample_type == "QC")
+filtered_res <- filterFeatures(object = filtered_res, filter = rsd_filter, assay = "raw")
 ```
 
 All features with an RSD (CV) strictly larger than 0.3 in QC samples were thus
@@ -1279,64 +1328,6 @@ filtered_res <- filterFeatures(object = filtered_res,
 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
-(i.e. number of samples) with the sample type for each. The function then
-computes the percentage of missing values per sample groups and filters
-features based on this. Features with a percent of missing values larger than
-the threshold in all sample groups will be removed. Another option is to base
-this quality assessment and filtering only on QC samples.
-
-Both examples are shown below:
-
-```{r}
-# To set up parameter `f` to filter only based on QC samples
-f <- sampleData(filtered_faakho)$sample_type
-f[f != "QC"] <- NA
-
-# To set up parameter `f` to filter per sample type excluding QC samples
-f <- sampleData(filtered_faakho)$sample_type
-f[f == "QC"] <- NA
-
-missing_filter <- PercentMissingFilter(threshold = 30,
-                                       f = f)
-
-# Apply the filter to faakho object
-filtered_faakho <- filterFeatures(object = filtered_faakho,
-                                  filter = missing_filter)
-
-# Apply the filter to res object
-missing_filter <- PercentMissingFilter(threshold = 30,
-                                       f = f)
-filtered_res <- filterFeatures(object = filtered_res,
-                               filter = missing_filter)
-```
-
-Here, no feature was removed, meaning that all the features had less than 30%
-of `NA` values in at least one of the sample type.
-
-Although not directly relevant to this experiment, the `BlankFlag` filter can be
-used to flag features based on the intensity relationship between blank and QC
-samples. More information can be found in the documentation of the filter:
-
-```{r}
-# Retrieve documentation for the main function and the specific filter.
-?filterFeatures
-?BlankFlag
-```
-
-## Normalization
-
-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.
-
 
 ## Alignment to an external reference dataset