match-standards-water-mix03.Rmd

---
title: "Identifying mix03 standards in water"
author: "Andrea Vicini, Vinicius Verri Hernandes, Johannes Rainer"
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    toc_float: true
    toc_depth: 3
    fig_width: 5
---

```{r, include = FALSE, cached = FALSE}
knitr::read_chunk("R/match-standards-chunks.R")
MIX <- 3
MATRIX <- "Water"
POLARITY <- "POS"
## settings to find MS2 spectra for features. Be rather inclusive here.
FEATURE_MS2_PPM <- 20
FEATURE_MS2_TOLERANCE <- 0.05
```

```{r, libraries, echo = FALSE, message = FALSE}
```
```{r, general-settings, echo = FALSE, message = FALSE}
```

**Current version: 0.10.0, 2022-05-31.**

# Introduction

Mixes of standards have been solved in water or added to human serum sample
pools in two different concentration and these samples were measured with the
LC-MS setup from Eurac (used also to generate the CHRIS untargeted metabolomics
data). Assignment of retention time and observed ion can be performed for each
standard based on MS1 information such as expected m/z but also based on the
expected difference in signal between samples with low and high concentration of
the standards. Finally, experimental fragment spectra provide the third level of
evidence. Thus, the present data set allows to annotate features to standards
based on 3 levels of evidence: expected m/z, difference in measured intensity
and MS2-based annotation.

A detailed description of the approach is given in
[match-standards-introduction.Rmd](match-standards-introduction.Rmd).


The list of standards that constitute the present sample mix are listed below
along with the expected retention time and the most abundant adduct for positive
and negative polarity as defined manually in a previous analysis by Mar
Garcia-Aloy.

```{r, standards-table, echo = FALSE, results = "asis"}
```

# Data import

We next load the data and split it according to matrix and polarity.

```{r, data-import}
```

We next also load the reference databases we will use to compare the
experimental MS2 spectra against. Below we thus load HMDB and MassBank data. We
create in addition *neutral loss* versions of the databases. Since HMDB does not
provide precursor m/z values we assume the fragment spectra to represent `[M+H]+`
ions (respectively `[M-H]-` ions for negative polarity) and add the m/z for
these adducts as *precursor m/z*.

```{r, load-reference-databases, message = FALSE}
```

```{r, setup-ion-db, message = FALSE, echo = FALSE}
```

# Initial evaluation of available MS2 data

Before performing the actual analysis that involves chromatographic peak
detection and evaluation of feature abundances, we compare all experimental MS2
spectra against the reference libraries. This provides already a first hint for
which standards a valid MS2 spectrum was recorded (and hence would allow
annotation based on evidence 3) and what related retention time might be.

We first extract all MS2 spectra from the respective data files and process them
by first removing all peaks with an intensity below 5% of the highest peak
signal, then scaling all intensities to a value range between 0 and 100 and
finally remove all MS2 spectra with less than 2 peaks.

```{r prepare-all-ms2}
fls <- fileNames(data_all)[data_all$polarity == "POS"]
```
```{r, all-ms2}
```

We next match these spectra against all reference spectra from HMDB for the
standards in `r MIX_NAME`. 

The settings for the matching are defined below. These will be used for all MS2
spectra similarity calculations. All matching spectra with a similarity larger
0.7 are identified.

```{r, compare-spectra-param}
```
 
Standards for which no MS2 spectrum in HMDB is present are listed in
the table below.

```{r, echo = FALSE, results = "asis"}
tmp <- std_dilution[!(std_dilution$HMDB %in% hmdb_std$compound_id), ]
pandoc.table(tmp[, c("name", "HMDB", "formula")], style = "rmarkdown",
             caption = "Standards for which no reference spectrum is available")
```

The table below lists all standards and the number of matching reference spectra
(along with their retention times).

```{r, table-all-ms2-hmdb, echo = FALSE, results = "asis"}
```

We repeat the same analysis for the negative polarity data (code not shown
again).

```{r, echo = FALSE}
fls <- fileNames(data_all)[data_all$polarity == "NEG"]
```
```{r, all-ms2, echo = FALSE}
```
```{r, table-all-ms2-hmdb, echo = FALSE, results = "asis"}
```

We next perform the full analysis of the data set involving MS1 and MS2 data.


# Water, positive polarity

We first perform the analysis on the samples with the standards solved in pure
water acquired in positive polarity mode.

```{r}
POLARITY <- "POS"
data <- filterFile(data_all, which(data_all$polarity == POLARITY))
hmdb <- hmdb_pos
hmdb_nl <- hmdb_pos_nl
```

## Data pre-processing

We perform the pre-processing of our data set which consists of the
chromatographic peak detection followed by a peak refinement step to reduce peak
detection artifacts, the correspondence analysis to group peaks across samples
and finally the gap-filling to fill in missing peak data from samples in which
no chromatographic peak was detected. For details on settings please refer to
the analysis of *mix 01* samples.

```{r, preprocessing, eval = !file.exists(paste0(RDATA_PATH, "processed_data_", POLARITY, ".RData")), message = FALSE}
```

```{r, echo = FALSE}
load(paste0(RDATA_PATH, paste0("processed_data_", POLARITY, ".RData")))
data_FS <- filterFile(data, which(data$mode == "FS"),
                      keepFeatures = TRUE)
```

## Signal intensity difference

We compute the difference in (log2) signals between samples with high and low
concentration of the standards and calculate the p-value for this difference
using the Student's t-test.

```{r, abundance-difference}
```

## Identification of features matching standards

We now identify all features matching the any of the pre-defined set of adducts
for the standards of mix `r MIX`. Matching features are further subsetted to
those that show an at least twice as high signal in samples with high
concentration compared to those with low concentrations. Also, features with a
signal in low concentration but no detectable signal in high concentration are
removed.

```{r, define-adducts-pos}
```
```{r, match-features}
```

For most of the standards (`r length(unique(mD$target_name))` out of 
`r nrow(std_dilution)`) in mix `r MIX` at least one feature was found
matching the standards adducts' m/z. 

```{r, table-standard-no-feature, results = "asis", echo = FALSE}
```

In the next sections we investigate for each standard which assignment would be
the correct one or, for those for which no signal was detected, why that was the
case.

## Standards with matching features

While for some standards a matching feature was found we still need to evaluate
whether this matching is correct. For each standard we thus first evaluate the
EICs for all matching features, then we match their MS2 spectra (if available)
against reference libraries. To ensure correct assignment of a feature, its
retention time and eventually related MS2 spectra, we consider the following
criteria to determine the annotation confidence:

- feature(s) was/were found with m/z matching those of adduct(s) of the 
  standard.
- signal is higher for samples with higher concentration.
- MS2 spectra matches reference spectra for the standard (if available).
- MS2 spectra don't match MS2 spectra of other compounds.

### Acetyl-CoA

```{r, echo = FALSE}
std <- "Acetyl-CoA"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on
m/z matching. Based on their retention times, these seem however to represent
ions from different compounds.

We next plot the EICs for the assigned features and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-acetyl-coa-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Unclear.
- No MS2 spectra available.


### Adenylosuccinic Acid

```{r, echo = FALSE}
std <- "Adenylosuccinic Acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching.
Based on their retention times, these seem however to represent ions
from different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-adenylosuccinic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-adenylosuccinic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-pos-adenylosuccinic-acid-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In both cases we don't find any match. In addition, since some of the extracted
spectra are associated to ions different from `[M+H]+` we perform a neutral
loss spectra comparison.

```{r mix03-water-pos-adenylosuccinic-acid-ms2-hmdb_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_hmdb_nl <- hmdb_nl[hmdb_nl$compound_id == hmdb_id]
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_hmdb_nl, csp_nl)
```

Surprisingly, with neutral loss comparison we find matches for features
(FT4779, FT4780, FT4781) that were matched to `[M+H]+` ion.

```{r mix03-water-pos-adenylosuccinic-acid-hmdb-nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.7, ppm = csp_nl@ppm,
                           tolerance = csp_nl@tolerance)
```

```{r mix03-water-pos-adenylosuccinic-acid-ms2-mbank_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank_nl <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey), nl = TRUE)
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_mbank_nl, csp_nl)
```

We don't get any match with the MassBank comparison.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
std_ms2_sel <- Spectra()
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

For completeness, we also perform a neutral loss spectra comparison with HMDB
and MassBank.

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, hmdb_nl, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, mbank_nl,
              sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```

#### Summary

- What is a bit strange is that only the neutral loss spectra match - even if
  the experimental spectrum is a `[M+H]+` ion. But similarity is very high thus
  we consider the feature matching the standard.
- FT4779 (RT = 225.9, `[M+H]+` ion) with confidence **B**. 
- FT5185 (RT = 225.9, `[M+K]+` ion) with confidence **B-**.
- FT5013 (RT = 224.3, `[M+Na]+` ion) with confidence **B-**.
- Reference MS2: FT4779 `[M+H]+`: FT4779_F07.S0694, FT4779_F07.S0736,
 FT4779_F07.S0770, FT4779_F07.S0794, FT4779_F08.S0686, FT4779_F08.S0728,
 FT4779_F08.S0793 (low confidence). FT5185 `[M+K]+`: FT5185_F08.S0738 (low
 confidence). FT5013 `[M+Na]+`: FT5013_F08.S0736, FT5013_F07.S0720 (low
 confidence).

The associated ion and MS2 spectra are added to the database.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT4779", "FT5185", "FT5013"),
                  confidence_level = c("B", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT4779_F07.S0694", "FT4779_F07.S0736", "FT4779_F07.S0770",
                 "FT4779_F07.S0794", "FT4779_F08.S0686", "FT4779_F08.S0728",
                 "FT4779_F08.S0793", "FT5185_F08.S0738", "FT5013_F08.S0736",
                 "FT5013_F07.S0720")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Alanine

```{r, echo = FALSE}
std <- "Alanine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Three features have been assigned to this standard based on m/z matching.
Based on their retention times, these seem however to represent ions from two different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-alanine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectrum against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-alanine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-pos-alanine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.06,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix03-water-pos-alanine-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In both cases we don't find any good match (maximum similarity score around
0.07). The experimental MS2 spectrum has however also many peaks.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
std_ms2_sel <- Spectra()
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

#### Summary

- FT0172 (RT = 164.4, `[M+H]+` ion) with confidence **C**.
- FT0727 (RT = 161.9, `[M+2Na-H]+` ion) with confidence **C-**.
- Reference MS2: FT0172 `[M+H]+`: FT0172_F08.S0494 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0172", "FT0727"),
                  confidence_level = c("C", "C-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0172_F08.S0494")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Citrulline

```{r, echo = FALSE}
std <- "Citrulline"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The MS2 spectra available for the features matched to `r std` are shown
below (peaks with an intensity below 5% of the maximum peak where removed).

```{r mix03-water-pos-citrulline-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectrum against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-citrulline-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

The best matches are those involving spectra from FT1271 (F07.S0554, F07.S0598,
F08.S0564, F08.S0616) and FT1066 (F07.S0550, F08.S0575).

```{r mix03-water-pos-citrulline-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.9,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix03-water-pos-citrulline-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

We obtain similar results with the MassBank comparison.

```{r mix03-water-pos-citrulline-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.9,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

#### Summary

- FT1271 (RT = 182.7, `[M+H]+` ion) with confidence **A**. 
- FT1066 (RT = 182.6, `[M+H-NH3]+` ion) with confidence **A**.
- FT1550 (RT = 182.6, `[M+Na]+` ion) with confidence **A-**.
- FT1046 (RT = 182.6, `[M+H-H2O]+` ion) with confidence **A-**.
- Reference MS2: FT1271 `[M+H]+`: FT1271_F07.S0554, FT1271_F07.S0598,
  FT1271_F08.S0564, FT1271_F08.S0616 (high confidence). FT1066 `[M+H-NH3]+`:
  FT1066_F07.S0550, FT1066_F08.S0575 (high confidence). FT1550 `[M+Na]+`:
  FT1550_F07.S0556, FT1550_F08.S0578 (low confidence). FT1046 `[M+H-H2O]+`:
  FT1046_F08.S0589 (low confidence).
  
```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT1271_F07.S0554", "FT1271_F07.S0598", "FT1271_F08.S0564",
                 "FT1271_F08.S0616", "FT1066_F07.S0550", "FT1066_F08.S0575",
                 "FT1550_F07.S0556", "FT1550_F08.S0578", "FT1046_F08.S0589")]
ms2$confidence <- c(rep("high", 6), rep("low", 3))
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Cytosine

```{r, echo = FALSE}
std <- "Cytosine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching. 
Based on their retention times, these seem however to represent ions from
different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-cytosine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-cytosine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We have matches for spectra from FT0363 and FT0212. We observe very low
similarity scores for matches involving spectra from FT0203, FT0998, FT0999.

```{r mix03-water-pos-cytosine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We also perform a comparison against MassBank.

```{r mix03-water-pos-cytosine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In the MassBank case only spectra from FT0363 match.

```{r mix03-water-pos-cytosine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT0363 (RT = 147.2, ion `[M+H]+`): confidence **B**.
- FT0203 (RT = 148.6, ion `[M+H-H2O]+`): confidence **B-**.
- FT0212 (RT = 148.6, ion `[M+H-NH3]+`): confidence **B-**.
- FT0998 (RT = 152.9, ion `[M+2Na-H]+`): confidence **B-**.
- FT0730 (RT = 152.6, ion `[M+Na]+`): confidence **B-**.
- Reference MS2: FT0363 `[M+H]+`: FT0363_F07.S0355, FT0363_F07.S0415,
  FT0363_F08.S0373, FT0363_F08.S0429 (high confidence). FT0203 `[M+H-H2O]+`:
  FT0203_F07.S0385, FT0203_F08.S0443 (low confidence). FT0212 `[M+H-NH3]+`:
  FT0212_F07.S0377, FT0212_F07.S0448, FT0212_F08.S0382, FT0212_F08.S0444 (low
  confidence). FT0988 `[M+2Na-H]+`: FT0998_F07.S0348, FT0998_F08.S0375,
  FT0998_F08.S0432 (low confidence). FT0730 `[M+Na]+`: FT0730_F07.S0356,
  FT0730_F07.S0416, FT0730_F08.S0374, FT0730_F08.S0431 (low confidence).

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0363_F07.S0355", "FT0363_F07.S0415", "FT0363_F08.S0373",
                 "FT0363_F08.S0429", "FT0203_F07.S0385", "FT0203_F08.S0443",
                 "FT0212_F07.S0377", "FT0212_F07.S0448", "FT0212_F08.S0382",
                 "FT0212_F08.S0444", "FT0998_F07.S0348", "FT0998_F08.S0375",
                 "FT0998_F08.S0432", "FT0730_F07.S0356", "FT0730_F07.S0416",
                 "FT0730_F08.S0374", "FT0730_F08.S0431")]
ms2$confidence <- c(rep("high", 4), rep("low", 13))
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Glucose

```{r, echo = FALSE}
std <- "Glucose"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching.
Based on their retention times, these seem however to represent ions from two
different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-glucose-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-glucose-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-pos-glucose-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

No matches are found. In addition, since the extracted spectra from FT1112
are associated to an ion different from `[M+H]+` (`[M+H-H2O]+`) we perform a
neutral loss spectra comparison.

```{r mix03-water-pos-glucose-ms2-hmdb_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_hmdb_nl <- hmdb_nl[hmdb_nl$compound_id == hmdb_id]
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_hmdb_nl, csp_nl)
```

We find low similarity matches for spectra from FT1112 but only a single peak
m/z seem to match as shown below.

```{r mix03-water-pos-glucose-mirror-hmdb_nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.3,
                           ppm = csp_nl@ppm, tolerance = csp_nl@tolerance)
```

```{r mix03-water-pos-glucose-ms2-mbank_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank_nl <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey), nl = TRUE)
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_mbank_nl, csp_nl)
```

We don't get new good matches from the neutral loss spectra comparison.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

For completeness, we also perform a neutral loss spectra comparison with HMDB
and MassBank.

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, hmdb_nl, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, mbank_nl,
              sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```

#### Summary

- FT1596 (RT = 170.3, `[M+Na]+` ion) with confidence **D**.
- Should be verified in serum or negative polarity!
- Other features exist (FT1112 (RT = 137.8 , `[M+H-H2O]+` ion)), but it's MS2
  spectra match sucrose and galactitol with higher similarity. Based on previous
  analyses we assign the feature with RT = 170.3 to glucose.
  
```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1596"),
                  confidence_level = c("D"))

```

```{r, add-ions, echo = FALSE}
```


### Glutathione Oxidized

```{r, echo = FALSE}
std <- "Glutathione Oxidized"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from different
compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-glutathione-oxidized-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-glutathione-oxidized-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We have good matches for F07.S0661, F08.S0690 from FT5928 and F07.S0698,
F07.S0750, F08.S0730 that were assigned to both FT5928 and FT5929.
 
```{r mix03-water-pos-glutathione-oxidized-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix03-water-pos-glutathione-oxidized-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

We obtain similar results with some of the spectra mentioned above matching
with low similarity score.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank,
              sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

There are matches with similarity higher than 0.7 involving more than one
compound (maybe they are similar?).


#### Summary

- The reason of **A** grading over **B** is that none of the **selected** MS2
  spectra matches other compounds.
- FT5928 (RT = 210.8, `[M+H]+`): confidence level **A**.
- FT2562 (RT = 210.8, `[M+2H]2+`): confidence level **A-**.
- FT6075 (RT = 211.9, `[M+Na]+`) with confidence **A-**.
- FT6179 (RT = 211.4, `[M+K]+`) with confidence **A-**.
- FT5818 (RT = 210.2, `[M+H-H2O]+`) with confidence **A-**.
- FT5826 (RT = 210.2, `[M+H-NH3]+`) with confidence **A-**.
- Reference MS2: FT5928 `[M+H]+`: FT5928_F07.S0698, FT5928_F07.S0750,
  FT5928_F08.S0690, FT5928_F08.S0730 (high confidence). FT6075 `[M+Na]+`:
  FT6075_F08.S0691, FT6075_F07.S0672 (low confidence). FT6179 `[M+K]+`:
  FT6179_F08.S0693 (low confidence). FT5818 `[M+H-H2O]+`: FT5818_F07.S0671,
  FT5818_F08.S0689 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT5928", "FT2562", "FT6075", "FT6179",
                                 "FT5818", "FT5826"),
                  confidence_level = c("A", "A-", "A-", "A-", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT5928_F07.S0698", "FT5928_F07.S0750", "FT5928_F08.S0690",
                 "FT5928_F08.S0730", "FT6075_F08.S0691", "FT6075_F07.S0672",
                 "FT6179_F08.S0693", "FT5818_F07.S0671", "FT5818_F08.S0689")]
ms2$confidence <- c(rep("high", 4), rep("low", 5))
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```



### Glycolic acid

```{r, echo = FALSE}
std <- "Glycolic acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

A single feature has been assigned to this standard based on m/z matching.
We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-glycolic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```


#### Summary

- Inconclusive.


### Guanine

```{r, echo = FALSE}
std <- "Guanine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from different
compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-guanine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-guanine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-pos-guanine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

Since all available MS2 spectra are associated to ions different from `[M+H]+`
we also perform a neutral loss comparison against reference spectra from HMDB
and MassBank.

```{r mix03-water-pos-guanine-ms2-hmdb_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_hmdb_nl <- hmdb_nl[hmdb_nl$compound_id == hmdb_id]
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_hmdb_nl, csp_nl)
```

```{r mix03-water-pos-guanine-hmdb_nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.1, ppm = csp_nl@ppm,
                               tolerance = csp_nl@tolerance)
```

```{r mix03-water-pos-guanine-ms2-mbank_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank_nl <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey), nl = TRUE)
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_mbank_nl, csp_nl)
```

```{r mix03-water-pos-guanine-mbank_nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_mbank_nl, 0.1, ppm = csp_nl@ppm,
                               tolerance = csp_nl@tolerance)
```

In both cases the matches don't look very good and involve only one to max 2
peaks.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

For completeness, we also perform a neutral loss spectra comparison with HMDB
and MassBank.

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, hmdb_nl, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```

```{r, echo = FALSE, message = FALSE, results = "asis", eval = ALL_NL_MATCH}
perform_match(std_ms2_nl, mbank_nl,
              sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp_nl)
```


#### Summary

- Inconclusive.
- FT1529 (RT = 155.9, `[M+2Na-H]+` ion) would match the previous RT, but it's
  MS2 spectrum matches the one of 2-Amino-6-nitrobnzothiazole.
- FT1242 (RT = 156, `[M+Na]+` ion).


### Isoleucine

```{r, echo = FALSE}
std <- "Isoleucine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-isoleucine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-isoleucine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

The spectra F07.S0397 and F08.S0430 from FT0681 match with good similarity
scores against some reference spectra of `r std`. Also F08.S0536 from  FT0683
matches but with lower similarity score.

```{r mix03-water-pos-isoleucine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We obtain similar results considering MassBank reference spectra.

```{r mix03-water-pos-isoleucine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-pos-isoleucine-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT0681 (RT = 150.9, `[M+H]+` ion) with confidence **B**.
- FT1268 (RT = 149, `[M+2Na-H]+` ion) with confidence **B-**.
- FT0419 (RT = 151, `[M+H-NH3]+` ion) with confidence **B-**.
- Reference MS2: FT0681 `[M+H]+`: FT0681_F07.S0397, FT0681_F08.S0430 (high
  confidence), FT1268 `[M+2Na-H]+` ion: FT1268_F08.S0445 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0681", "FT1268", "FT0419"),
                  confidence_level = c("B", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0681_F07.S0397", "FT0681_F08.S0430", "FT1268_F08.S0445")]
ms2$confidence <- c("high", "high", "low")
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Malonic acid

```{r, echo = FALSE}
std <- "Malonic acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-malonic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- FT0900 (RT = 59.51, `[M+2Na-H]+` ion) with confidence **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0900"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE}
```


### Niacinamide

```{r, echo = FALSE}
std <- "Niacinamide"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from different
compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-niacinamide-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-niacinamide-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We have good matches for spectra F07.S0128, F07.S0167, F08.S0135, F08.S0178
from FT0504 and for F07.S0197, F08.S0207 (and less good for
F08.S0248) from FT0506. Below we show the mirror plots of the best matches.

```{r mix03-water-pos-niacinamide-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.8, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We obtain similar results with the MassBank comparison as we show below.

```{r mix03-water-pos-Niacinamide-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- Giving confidence level **A** because Nicotinamide and Vitamin PP are synonyms
  for Niacinamide and all other matching compounds differ by their precursor
  m/z.
- FT0504 (RT = 50, `[M+H]+` ion) with confidence level **A**.
- FT0298 (RT = 48.86, `[M+H-NH3]+` ion) with confidence level **A-**.
- Reference MS2: FT0504 `[M+H]+`: FT0504_F07.S0128, FT0504_F07.S0167,
  FT0504_F08.S0135, FT0504_F08.S0178 (high confidence). FT0298 `[M+H-NH3]+`:
  FT0298_F07.S0165 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0504", "FT0298"),
                  confidence_level = c("A", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0504_F07.S0128", "FT0504_F07.S0167", "FT0504_F08.S0135",
                 "FT0504_F08.S0178", "FT0298_F07.S0165")]
ms2$confidence <- c(rep("high", 4), "low")
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### 5-Oxoproline

```{r, echo = FALSE}
std <- "5-Oxoproline"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from different
compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-5-oxoproline-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-5-oxoproline-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We find high similarity matches invoving spectra F07.S0198, F07.S0232,
F08.S0212, F08.S0249, F08.S0283 from FT0633 and lower similarity ones for
spectrum F07.S0664 from FT0635.

```{r mix03-water-pos-5-oxoproline-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We obtain similar results with the MassBank comparison as shown below.

```{r mix03-water-pos-5-oxoproline-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-pos-5-oxoproline-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.9, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

Note: Pyroglutamic Acid is a synonym for 5-Oxoproline.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT0633 (RT = 68.5, `[M+H]+` ion) with confidence level **A**.
- FT0940 (RT = 68.7, `[M+Na]+` ion) with confidence level **A-**.
- FT1612 (RT = 71.38, `[M+2K-H]+` ion) with confidence level **A-**.
- Reference MS2: FT0633 `[M+H]+`:  FT0633_F07.S0198, FT0633_F07.S0232,
  FT0633_F08.S0212, FT0633_F08.S0249, FT0633_F08.S0283 (high confidence).
  FT0940 `[M+Na]+`: FT0940_F08.S0242 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0633", "FT0940", "FT1612"),
                  confidence_level = c("A", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0633_F07.S0198", "FT0633_F07.S0232", "FT0633_F08.S0212",
                 "FT0633_F08.S0249", "FT0633_F08.S0283", "FT0940_F08.S0242")]
ms2$confidence <- c(rep("high", 5), "low")
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```



### Palmitoylcarnitine

```{r, echo = FALSE}
std <- "Palmitoylcarnitine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on
m/z matching. Based on their retention times, these seem however to represent
ions from different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The only available MS2 spectrum (cleaned) for the features matched to `r std` is
shown below (peaks with an intensity below 5% of the maximum peak and spectra
with less than 2 peaks where removed).

```{r mix03-water-pos-palmitoylcarnitine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-pos-palmitoylcarnitine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We don't get any match with the HMDB comparison.

```{r mix03-water-pos-palmitoylcarnitine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

With the MassBank comparison we find a good matches for spectra F07.S0178 and
F08.S0189 assigned to both FT3760 and FT3761 and for spectrum F08.S0524 from
FT3755.

```{r mix03-water-pos-5-palmitoylcarnitine-mbank, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- Here the EIC for the `[M+H]+` ion (feature FT3760) does not seem to be
  *complete*, i.e. it looks like the left part is missing. Thus, the retention
  time of 50 seconds is too high but the RT of the `[M+NH4]+` ion (RT = 36.41)
  seems to be more appropriate.
- FT3760 (RT= 50.8, `[M+H]+` ion) with confidence level **A**.
- FT4263 (RT = 36.4 `[M+NH4]+` ion) with  confidence **A-**.
- Reference MS2: FT3760 `[M+H]+`: FT3760_F07.S0178, FT3760_F08.S0189 (high
  confidence). FT4263 `[M+NH4]+`: FT4263_F08.S0128 (low confidence).

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT3760_F07.S0178", "FT3760_F08.S0189", "FT4263_F08.S0128")]
ms2$confidence <- c("high", "high", "low")
```

```{r, add-ms2-spectra, echo = FALSE}
```


### UDP

```{r, echo = FALSE}
std <- "UDP"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WP", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-pos-udp-ms2, echo = FALSE}
plot_spectra(std_ms2)
```


#### Summary

- FT4643 (RT = 232, `[M+2Na-H]+` ion) with confidence level **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT4643"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE}
```


# Water, negative polarity

We now perform the analysis on the samples with the standards still solved in
pure water but acquired in negative polarity mode.

```{r}
POLARITY <- "NEG"
data <- filterFile(data_all, which(data_all$polarity == POLARITY))
hmdb <- hmdb_neg
hmdb_nl <- hmdb_neg_nl
```

```{r, preprocessing, eval = !file.exists(paste0(RDATA_PATH, "processed_data_", POLARITY, ".RData")), message = FALSE}
```

```{r, echo = FALSE}
load(paste0(RDATA_PATH, paste0("processed_data_", POLARITY, ".RData")))
data_FS <- filterFile(data, which(data$mode == "FS"),
                      keepFeatures = TRUE)
```


## Signal intensity difference

We compute the difference in (log2) signals between samples with high and low
concentration of the standards and calculate the p-value for this difference
using the Student's t-test.

```{r, abundance-difference}
```

## Identification of features matching standards

We now identify all features matching the any of the pre-defined set of adducts
for the standards of mix `r MIX`. Matching features are further subsetted to
those that show an at least twice as high signal in samples with high
concentration compared to those with low concentrations. Also, features with a
signal in low concentration but no detectable signal in high concentration are
removed.

```{r, define-adducts-neg}
```
```{r, match-features}
```

For most of the standards (`r length(unique(mD$target_name))` out of 
`r nrow(std_dilution)`) in mix `r MIX` at least one feature was found
matching the standards adducts' m/z. 

```{r, table-standard-no-feature, results = "asis", echo = FALSE}
```

In the next sections we investigate for each standard which assignment would be
the correct one or, for those for which no signal was detected, why that was the
case.

## Standards with matching features

### Acetyl-CoA

```{r, echo = FALSE}
std <- "Acetyl-CoA"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EICs for the assigned features and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-acetyl-coa-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-acetyl-coa-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

All the extracted spectra match but with similarity lower than 0.7. 

```{r mix03-water-neg-acetyl-coa-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

We obtain similar results with the MassBank comparison but with lower similarity
scores as shown below.

```{r mix03-water-neg-acetyl-coa-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
std_ms2_sel <- Spectra()
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

We don't find any compound matching extracted spectra with similarity higher
than 0.7.

#### Summary

- Tricky to say which retention time might be the *correct* one for the two
  features below. Make that dependent on positive polarity and serum.
- FT3882 (RT = 210.4, `[M-H]-` ion) with confidence **C**.
- FT3884 (RT = 194, `[M-H]-` ion) with confidence **C**.
- Reference MS2: FT3882 `[M+H]+`: FT3881_F16.S0584 (high confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT3882"),
                  confidence_level = c("C"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT3881_F16.S0584")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```



### Adenylosuccinic Acid

```{r, echo = FALSE}
std <- "Adenylosuccinic Acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-adenylosuccinic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-adenylosuccinic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-neg-adenylosuccinic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
std_ms2_sel <- Spectra()
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

We don't find any compound matching extracted spectra with similarity higher
than 0.7.

#### Summary

- Inconclusive: check other polarity and serum for validation.
- FT2342 (RT = 224.1, `[M-H]-` ion) with confidence **D**.
- Reference MS2: FT2342 `[M-H]-`: FT2342_F15.S0675, FT2342_F15.S0722,
  FT2342_F16.S0689 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2342"),
                  confidence_level = c("D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2342_F15.S0675", "FT2342_F15.S0722", "FT2342_F16.S0689")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Alanine

```{r, echo = FALSE}
std <- "Alanine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-alanine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-alanine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Only spectrum F16.S0431 from FT0026 matches (the matches however involve a
single peak as shown in the mirror plots below).

```{r mix03-water-neg-alanine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix03-water-neg-alanine-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-neg-alanine-mbank, echo = TRUE, fig.cap = "Mirror plots"}
plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```



#### Summary

- FT0026 (RT = 161.6, `[M-H]-` ion) with confidence level **B**.
- Reference MS2: FT0026 `[M-H]-`: FT0026_F16.S0431 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0026"),
                  confidence_level = c("B"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0026_F16.S0431")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Citrulline

```{r, echo = FALSE}
std <- "Citrulline"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-citrulline-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-citrulline-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Both spectra F15.S0515 and F16.S0569 from FT0427 match HMDB reference spectra
for `r std`.

```{r mix03-water-neg-citrulline-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

We find similar results for MassBank as shown below.

```{r mix03-water-neg-citrulline-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-neg-citrulline-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

Note: delta-Ureidonorvaline is a synonym for Citrulline.

#### Summary

- FT0427 (RT=182.6, `[M-H]-` ion) with confidence level **A**.
- FT0748 (RT=182.3, `[M+HCOO]-` ion) with confidence level **A-**.
- FT0671 (RT=182.7, `[M+Cl]-` ion) with confidence level **A-**.
- Reference MS2: FT0427 `[M-H]-`: FT0427_F15.S0515, FT0427_F16.S0569 (high
  confidence).
  
```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0427", "FT0748", "FT0671"),
                  confidence_level = c("A", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0427_F15.S0515", "FT0427_F16.S0569")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Cytosine

```{r, echo = FALSE}
std <- "Cytosine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

A single feature been assigned to this standard based on m/z matching.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-cytosine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-cytosine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

The extracted MS2 spectra (all from FT0054) match with low similarity scores
HMDB reference spectra for `r std` (maximum similarity score around 0.6).

```{r mix03-water-neg-cytosine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

With the MassBank comparison no match is found as shown below.

```{r mix03-water-neg-cytosine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

#### Summary

- FT0054 (RT = 147.3, `[M-H]-` ion) with confidence level **C**.
- Reference MS2: FT0054 `[M-H]-`: FT0054_F15.S0311, FT0054_F15.S0365,
  FT0054_F16.S0312, FT0054_F16.S0367 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0054"),
                  confidence_level = c("C"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0054_F15.S0311", "FT0054_F15.S0365", "FT0054_F16.S0312",
                 "FT0054_F16.S0367")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Glucose

```{r, echo = FALSE}
std <- "Glucose"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-glucose-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectrum against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-glucose-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-neg-glucose-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In both cases no match is found.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

#### Summary

- Inconclusive. Need to validate with different polarity and serum data. EICs
  look however very nice.
- FT0462 (RT = 168.2, `[M-H]-` ion) with confidence level **D**.
- FT0764 (RT = 168.2, `[M-HC00]-` ion) with confidence level **D**.
- Reference MS2: FT0462 `[M-H]-`: FT0462_F16.S0457 (low confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0462", "FT0764"),
                  confidence_level = c("D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0462_F16.S0457")]
ms2$confidence <- "low"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Glutathione Oxidized

```{r, echo = FALSE}
std <- "Glutathione Oxidized"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on
m/z matching.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-glutathione-oxidized-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-glutathione-oxidized-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```
 
```{r mix03-water-neg-glutathione-oxidized-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

We repeat the same with MassBank and we obtain similar results as shown below.

```{r mix03-water-neg-glutathione-oxidized-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-neg-glutathione-oxidized-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank,
              sv = c("rtime", "target_name", "target_inchikey", "score"),
              name = "target_name", param = csp)
```

#### Summary

- FT3245 (RT = 210.2, `[M-H]-` ion) with confidence **A**.
- Reference MS2: FT3245 `[M-H]-`: FT3245_F15.S0631, FT3245_F15.S0676,
  FT3245_F16.S0645 (high confidence).
  
```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT3245"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT3245_F15.S0631", "FT3245_F15.S0676", "FT3245_F16.S0645")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Glycolic acid

```{r, echo = FALSE}
std <- "Glycolic acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on m/z matching.
Based on their retention times, these seem however to represent
ions from different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-glycolic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-glycolic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Spectra F15.S0121 and F16.S0119 (associated to both FT0015 and FT0016),
F15.S0140 (associated to both FT0016 and FT0017) all match the same reference
spectrum for `r std`.

```{r mix03-water-neg-glycolic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix03-water-neg-glycolic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

With the MassBank comparison we find no matches.

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

#### Summary

- FT0016 (RT=47.6, `[M-H]-` ion) with confidence **A**.
- FT0096 (RT=46.3, `[M+HCOO]-` ion) with confidence **A-**.
- Reference MS2: FT0016 `[M-H]-`: FT0016_F15.S0121, FT0016_F15.S0140,
  FT0016_F16.S0119 (high confidence).
  
```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0016", "FT0096"),
                  confidence_level = c("A", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0016_F15.S0121", "FT0016_F15.S0140", "FT0016_F16.S0119")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Guanine

```{r, echo = FALSE}
std <- "Guanine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-guanine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive. Need to check different polarity and serum data.
- FT0281 (RT = 171.3, `[M-H]-` ion) with confidence level **D**.
- FT0568 (RT = 171.5, `[M+HCOO]-` ion) with confidence level **D**.


### Isoleucine

```{r, echo = FALSE}
std <- "Isoleucine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-isoleucine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-isoleucine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

In terms of similarity score we observe good matches for spectra F16.S0453 from
feature FT0138. Below we show the mirror plots of the matches with score higher
than 0.7.

```{r mix03-water-neg-isoleucine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We obtain similar results with the MassBank comparison as shown below.

```{r mix03-water-neg-isoleucine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT0138 (RT = 167.4, `[M-H]-` ion) with confidence **B**.
- Reference MS2: FT0138 `[M-H]-`: F16.S0453.
- Note: could also be another feature with higher intensity.


### Malonic acid


```{r, echo = FALSE}
std <- "Malonic acid"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

A single feature has been assigned to this standard based on m/z matching.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-malonic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectrum against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-malonic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-neg-malonic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.4, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix03-water-neg-malonic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT0048 (RT = 73.6, `[M-H]-` ion) with confidence level **C**.
- Reference MS2: FT0048 `[M-H]-`: FT0048_F15.S0196 (high confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0048"),
                  confidence_level = c("C"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0048_F15.S0196")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### 5-Oxoproline

```{r, echo = FALSE}
std <- "5-Oxoproline"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

Many features have been assigned to this standard based on
m/z matching. Based on their retention times, these seem however to represent
ions from different compounds.

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-5-oxoproline-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-5-oxoproline-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix03-water-neg-5-oxoproline-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix03-water-neg-5-oxoproline-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix03-water-neg-5-oxoproline-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                           tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

Note: Pyroglutamic acid and L-a-Aminoglutaric Acid Lactam are synonyms for
5-Oxoproline.


#### Summary

- FT0115 (RT= 68.4, `[M-H]-` ion) with confidence level **A**.
- FT0424 (RT = 68.3, `[M+HCOO]-` ion) with confidence level **A-**.
- Reference MS2: FT0115 `[M-H]-`: FT0115_F16.S0170 (high confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0115", "FT0424"),
                  confidence_level = c("A", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0115_F16.S0170")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


### Palmitoylcarnitine

```{r, echo = FALSE}
std <- "Palmitoylcarnitine"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

```{r mix03-water-neg-palmitoylcarnitine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- FT1918 (RT = 34.4, `[M-H]-` ion) with confidence level **D**.
- FT2217 (RT = 35.6, `[M+HCOO]-` ion) with confidence level **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1918", "FT2217"),
                  confidence_level = c("D", "D"))
```

```{r, add-ions, echo = FALSE}
```


### UDP

```{r, echo = FALSE}
std <- "UDP"
```

```{r, table-feature-matches, results = "asis", echo = FALSE, message = FALSE}
```

We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "WN", std_ms2)
```

The cleaned MS2 spectra for the features matched to `r std` are shown below
(peaks with an intensity below 5% of the maximum peak and spectra with less
than 2 peaks where removed).

```{r mix03-water-neg-udp-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

We next match the extracted MS2 spectra against the reference spectra for 
`r std` from HMDB and MassBank.

```{r mix03-water-neg-udp-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

All extracted spectra (from FT1950) match reference standards for `r std`.

```{r mix03-water-neg-udp-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix03-water-neg-udp-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

We obtain similar results also for MassBank.

```{r mix03-water-neg-udp-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

In addition we match (**all**) the MS2 spectra for the matched features against
all spectra from HMDB or MassBank identifying reference spectra with a
similarity larger than 0.7. The results (if any spectra matched) are shown in
the two following tables.

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, hmdb, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```

```{r, echo = FALSE, message = FALSE, results = "asis"}
perform_match(std_ms2, mbank, sv = c("rtime", "target_name", "score"),
              name = "target_name", param = csp)
```


#### Summary

- FT1950 (RT= 227.6, `[M-H]-` ion) with confidence level **A**.
- Reference MS2: FT1950 `[M-H]-`: FT1950_F15.S0691, FT1950_F16.S0721,
  FT1950_F16.S0722 (high confidence).

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1950"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT1950_F15.S0691", "FT1950_F16.S0721", "FT1950_F16.S0722")]
ms2$confidence <- "high"
```

```{r, add-ions, echo = FALSE}
```

```{r, add-ms2-spectra, echo = FALSE}
```


## Standards without any matching feature

# Summary on the ion database

Summarizing the content that was added to the `IonDb`.

```{r, iondb-summary, echo = FALSE, results = "asis"}
```

# Session information

The R version and packages used in this analysis are listed below.

```{r sessioninfo}
sessionInfo()
```