match-standards-serum-mix02.Rmd

---
title: "Identifying mix02 standards in serum"
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 <- 2
MATRIX <- "Serum"
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.


# Serum, positive polarity

We first perform the analysis on the samples with the standards solved in serum 
and 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.

Some notes on the settings for the pre-processing:

- The `bw` parameter for the correspondence is larger than usual because adding
  the standards in higher concentrations caused considerable retention time
  shifts for some. A higher `bw` will allow to group also mis-aligned
  chromatographic peaks - but will not allow to discriminate between closely
  eluting ions.
- The `binSize` was also slightly increased to avoid splitting of features with
  similar m/z values.

```{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.

### Ascorbic Acid

```{r, echo = FALSE}
std <- "Ascorbic 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 EICs for the assigned features and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SP", 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 were removed).

```{r mix02-serum-pos-ascorbic-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 mix02-serum-pos-ascorbic-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 F07.S0389 from FT01572 and F07.S0398 and F08.S0394 from FT01902 match
reference spectra from `r std`, the latter two with a lower score.

```{r mix02-serum-pos-ascorbic-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 mix02-serum-pos-ascorbic-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)
```

No match was found for reference spectra from MassBank.

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

- FT01902 (RT = 128.8, `[M+H]+` ion) confidence level **C**.
- FT01572 (RT = 127.5, `[M+H-H2O]+` ion) with confidence level **C**.
- FT03383 (RT = 127.5, `[M+2K-H]+` ion) confidence level **C**.
- Reference MS2: `[M+H]+`: FT01572_F07.S0389; low confidence. `[M+H-H2O]+`:
  FT01572_F08.S0393, FT01572_F07.S0389; low confidence. `[M+2K-H]+`:
  FT03383_F07.S0390, FT03383_F07.S0428, FT03383_F08.S0387; 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("FT01902", "FT01572", "FT03383"),
                  confidence_level = c("C", "C", "C"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT01572_F07.S0389", "FT01572_F08.S0393", "FT01572_F07.S0389",
                 "FT03383_F07.S0390", "FT03383_F07.S0428", "FT03383_F08.S0387")]
ms2$confidence <- "low"
```

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

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


### C4 Carnitine (Isobutyryl-L-carnitine)


```{r, echo = FALSE}
std <- "C4 Carnitine"
```

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-c4-carnitine-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 mix02-serum-pos-c4-carnitine-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 mix02-serum-pos-c4-carnitine-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 mix02-serum-pos-c4-carnitine-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 mix02-serum-pos-c4-carnitine-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)
```

For the HMDB comparison we don't find matches while for the MassBank one
the neutral loss spectra cannot be computed. 

Since the chemical formula used for this standard is essentially the one of
Butyrylcarnitine (C4 Carnitine) and not the one of Isobotyryl-L-carnitine (the
standard that was bought from Sigma Aldrich) we also match the MS2 spectra
against the reference spectra from Butyrylcarnitine (HMDB0002013).

```{r mix02-serum-pos-butyrylcarnitine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- "HMDB0002013"
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

We get matches with high similarity against the reference spectra from
Butyrylcarnitine.

```{r mix02-serum-pos-ascorbic-butyrylcarnitine-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)
```

Similarity is quite high arguing that the standard measured here is
Butyrylcarnitine, not Isobutyryl-L-carnitine.

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)
```

There are high similarity matches involving compounds different from `r std`. In
particular for Butyrylcarnitine also the precursor m/z difference is close to
zero.

```{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

- FT02888 (RT = 37, `[M+H+]` ion) confidence level **A** (for
  butyrylcarnitine).
- FT03406 (RT = 36.46, `[M+Na]+` ion).
- FT03708 (RT = 36.31, `[M+K]+` ion).
- Reference MS2 (assign to butyrylcarnitine).

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("FT02888", "FT03406", "FT03708"),
                  confidence_level = c("A", "A-", "A-"))
fts$note <- paste0("Ambiguous: standard is isobutyryl-L-carnitine (Sigma ",
                   "Aldrich 51085), but matches butyrylcarnitine.")
## Get MS2 spectra:
ms2 <- std_ms2[c("FT02888_F07.S0128", "FT02888_F07.S0185", "FT02893_F07.S0241",
                 "FT02891_F07.S0287", "FT02890_F07.S0400")]
ms2$confidence <- "high"
```

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

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



### Fructose

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-fructose-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 mix02-serum-pos-fructose-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 mix02-serum-pos-fructose-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 all the extracted
spectra are associated to ions different from `[M+H]+` we perform a neutral
loss spectra comparison.

```{r mix02-serum-pos-fructose-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 mix02-serum-pos-fructose-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.05,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix02-serum-pos-fructose-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)
```

For the HMDB comparison we don't find good matches while for the MassBank one
the neutral loss spectra cannot be computed. 

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

- FT02330 (RT = 150.7, `[M+Na]+` ion) confidence level **D**.
- FT02615 (RT = 147.2, `[M+K]+` ion) confidence level **D**.
- Reference MS2: `[M+Na]+`: FT02330_F08.S0476; low confidence. `[M+K]+`:
  FT02615_F07.S0456; 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("FT02330", "FT02615"),
                  confidence_level = c("D", "D"))
```

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


### Glycine

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

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

One single feature has been assigned to this standard based on m/z matching.
We next plot its EIC and visually inspect it.

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

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

```{r mix02-serum-pos-glycine-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 mix02-serum-pos-glycine-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 mix02-serum-pos-glycine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
st2_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

Only the precursor peak matches, but the reference spectra have only a single
peak (i.e. the precursor peak).

```{r mix02-serum-pos-glycine-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 mix02-serum-pos-glycine-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)
```

For both HMDB and MassBank we find high similarity matches (involving only one
peak) for the only available extracted spectrum (i.e. F07.S0587 from feature
FT00174).

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

- FT00174 (RT = 173.1, `[M+H]+` ion) with confidence **A**. 
- Reference MS2: `[M+H]+`: FT00174_F07.S0587; high 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("FT00174"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT00174_F07.S0587")]
ms2$confidence <- "high"
```

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

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

  
### cGMP

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-cgmp-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 mix02-serum-pos-cgmp-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 MS2 spectra from FT05702 match with high similarity score reference spectra
from `r std`. We also have matches with very low similarity involving spectra
from FT06569.

```{r mix02-serum-pos-cgmp-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots", fig.width = 10, fig.height = 10}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix02-serum-pos-cgmp-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 observe similar results also from the MassBank comparison.

```{r mix02-serum-pos-cgmp-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)
```

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

- FT05702 (RT = 216, `[M+H]+` ion): confidence **A**.
- FT06194 (RT = 214.1, ion `[M+Na]+`): confidence **A-**.
- FT06570 (RT = 218.1, ion `[M+K]+`): confidence **A-**.
- Reference MS2: `[M+H]+`: FT05702_F07.S0757, FT05702_F07.S0775,
  FT05702_F07.S0796, FT05702_F08.S0766, FT05702_F08.S0788, FT05702_F08.S0808;
  high confidence. `[M+Na]+`: FT06194_F08.S0782; 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("FT05702", "FT06194", "FT06570"),
                  confidence_level = c("A", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT05702_F07.S0757", "FT05702_F07.S0775", "FT05702_F07.S0796",
                 "FT05702_F08.S0766", "FT05702_F08.S0788", "FT05702_F08.S0808",
                 "FT06194_F08.S0782")]
ms2$confidence <- c(rep("high", 6), "low")
```

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

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



### Histidine

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-histidine-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 mix02-serum-pos-histidine-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 FT01452 (F07.S0670, F07.S0729, F08.S0695),
FT01455 (F07.S0729), FT01453 (F07.S0729, F07.S0763, F08.S0773),
FT01456 (F08.S0773, F08.S0800).

```{r mix02-serum-pos-histidine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.9, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix02-serum-pos-histidine-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)
```

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

- FT01452 (RT = 189.4, `[M+H]+` ion) confidence level **A**. Other features for
  that ion represent signal from the tail of this feature and are thus discarded
  (along with their MS2 spectra).
- FT01920 (RT = 189.1, `[M+Na]+` ion) confidence level **A-**.
- FT02188 (RT = 185.3, `[M+K]+` ion) confidence level **A-**.
- Reference MS2: `[M+H]+`: FT01452_F07.S0670, FT01452_F07.S0729,
  FT01452_F08.S0695; high confidence.
  
The associated ion and MS2 spectra are added to the database.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
## Get MS2 spectra:
ms2 <- std_ms2[c("FT01452_F07.S0670", "FT01452_F07.S0729", "FT01452_F08.S0695")]
ms2$confidence <- "high"
```

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

  
  
### Hydroxyproline

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-hydroxyproline-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 mix02-serum-pos-hydroxyproline-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 matchings spectra are F07.S0648 and F08.S0686 from FT00922.
Also F07.S0623 and F08.S0648 from FT00598 match (with similarity around 0.4).
 
```{r mix02-serum-pos-hydroxyproline-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 mix02-serum-pos-hydroxyproline-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", "target_inchikey", "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)
```

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)
```

Interestingly, some MS2 spectra associated with FT00922 match reference spectra
from creatine.

```{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

- FT00922 (RT = 178, `[M+H]+`): confidence level **A**.
- FT00598 (RT = 176, `[M+H-H2O]+`): confidence level **A-**.
- FT01418 (RT = 175.8, `[M+Na]+`): confidence level **A-**.
- Reference MS2: `[M+H]+`: FT00922_F07.S0648, FT00922_F08.S0686; high
  confidence. `[M+H-H2O]+`: FT00598_F07.S0623, FT00598_F08.S0648; 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("FT00922", "FT00598", "FT01418"),
                  confidence_level = c("A", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT00922_F07.S0648", "FT00922_F08.S0686", "FT00598_F07.S0623",
                 "FT00598_F08.S0648")]
ms2$confidence <- c("high", "high", "low", "low")
```

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

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


### Hypoxanthine

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-hypoxanthine-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 mix02-serum-pos-hypoxanthine-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 involve F07.S0377, F07.S0412, F08.S0381, F08.S0412 from
FT01048. We have low similarity matches for F07.S0386 (around 0.25) and
F08.S0390 (around 0.4) from FT00681.
 
```{r mix02-serum-pos-hypoxanthine-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 mix02-serum-pos-hypoxanthine-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 good match is found with MassBank comparison (only low similarity ones
for spectra from FT01048).

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

- FT01048 (RT = 127, `[M+H]+` ion): confidence level **A** (although the MS2
  match also another compound with high confidence, but the precursor m/z
  differs by 0.02).
- FT01850 (RT = 125.9, `[M+K]+` ion): confidence level **A**.
- FT00681 (RT = 126.8, `[M+H-H2O]+` ion): confidence level **A-**.
- FT01572 (RT = 127.5, `[M+Na]+` ion): confidence level **A-**.
- Reference MS2: `[M+H]+`: FT01048_F07.S0377, FT01048_F07.S0412,
  FT01048_F08.S0381, FT01048_F08.S0412; high confidence. `[M+K]+`:
  FT01850_F07.S0378, FT01850_F08.S0382, FT01850_F08.S0414; low
  confidence. `[M+H-H2O]+`: FT00681_F07.S0386, FT00681_F08.S0390; low
  confidence. `[M+Na]+`: FT01572_F08.S0393, FT01572_F07.S0389; 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("FT01048", "FT01850", "FT00681", "FT01572"),
                  confidence_level = c("A", "A-", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT01048_F07.S0377", "FT01048_F07.S0412", "FT01048_F08.S0381",
                 "FT01048_F08.S0412", "FT01850_F07.S0378", "FT01850_F08.S0382",
                 "FT01850_F08.S0414", "FT00681_F07.S0386", "FT00681_F08.S0390",
                 "FT01572_F08.S0393", "FT01572_F07.S0389")]
ms2$confidence <- c(rep("high", 4), rep("low", 7))
```

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

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



### L-Kynurenine

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-l-kynurenine-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 mix02-serum-pos-l-kynurenine-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 matches with high similarity for extracted MS2 spectra from FT02454
(F07.S0465 and F08.S0478).

```{r mix02-serum-pos-l-kynurenine-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 obtaining similar results.

```{r mix02-serum-pos-l-kynurenine-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)
```

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

- FT02454 (RT = 156.6, `[M+H]+` ion) with confidence level **A**.
- FT03386 (RT = 157, `[M+2Na-H]+` ion) with confidence **A-**.
- FT02859 (RT = 156.6, `[M+Na]+` ion) with confidence **A-**.
- FT02158 (RT = 156.8, `[M+H-NH3]+` ion): with confidence **A-**.
- Reference MS2: `[M+H]+`: FT02454_F07.S0465, FT02454_F08.S0478; high
  confidence. `[M+Na]+`: FT02859_F08.S0480; low confidence. `[M+2Na-H]+`:
  FT03386_F07.S0469, FT03386_F08.S0482; low confidence.

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

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT02454_F07.S0465", "FT02454_F08.S0478", "FT02859_F08.S0480",
                 "FT03386_F07.S0469", "FT03386_F08.S0482")]
ms2$confidence <- c(rep("high", 2), rep("low", 3))
```

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


### Lactic acid

```{r, echo = FALSE}
std <- "Lactic 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, "SP", 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 were removed).

```{r mix02-serum-pos-lactic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive. Low/no signal.


### 1-Methylxanthine

```{r, echo = FALSE}
std <- "1-Methylxanthine"
```

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-1-methylxanthine-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 mix02-serum-pos-1-methylxanthine-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 MS2 spectra from feature FT01720 (F07.S0197, F07.S0247, F08.S0196,
F08.S0240, F08.S0266) and feature FT01719 (F07.S0273 and F08.S0266).

```{r mix02-serum-pos-1-methylxanthine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.9, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix02-serum-pos-1-methylxanthine-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)
```

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

- FT01719 (RT = 81.6, `[M+H]+` ion) with confidence level **A**.
- FT02107 (RT = 81, `[M+Na]+` ion) with confidence level **A-**.
- FT02478 (RT = 80.8, `[M+2Na-H]+` ion) with confidence level **A-**.
- Tricky situation since also FT01720 (`[M+H]+` ion has MS2 matching the
  standard, but the retention time is 60 seconds). Maybe add both?
- Reference MS2: `[M+H]+`: FT01719_F07.S0273, FT01719_F08.S0266; high
  confidence. `[M+Na]+`: FT02107_F08.S0273; low confidence. `[M+2Na-H]+`:
  FT02478_F08.S0285; 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("FT02478"),
                  confidence_level = c("A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT01719_F07.S0273", "FT01719_F08.S0266", "FT02107_F08.S0273",
                 "FT02478_F08.S0285")]
ms2$confidence <- c("high", "high", "low", "low")
```

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

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


### 3-Nitrotyrosine

```{r, echo = FALSE}
std <- "3-Nitrotyrosine"
```

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-3-nitrotyrosine-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 mix02-serum-pos-3-nitrotyrosine-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 very good matches for F07.S0483 and F08.S0493 from feature FT02778.

```{r mix02-serum-pos-3-nitrotyrosine-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 mix02-serum-pos-3-nitrotyrosine-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)
```

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

- FT02778 (RT=156.8, `[M+H]+` ion) with confidence level **A**.
- FT03724 (RT=156.9, `[M+2Na-H]+` ion) with confidence level **A-**.
- Reference MS2: `[M+H]+`: FT02778_F07.S0483, FT02778_F08.S0493; high
  confidence. `[M+2Na-H]+`: FT03724_F08.S0510; low confidence.
  
The associated ion and MS2 spectra are added to the database.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT02778_F07.S0483", "FT02778_F08.S0493", "FT03724_F08.S0510")]
ms2$confidence <- c("high", "high", "low")
```

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


### Phosphocreatine

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-phosphocreatine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive.


### UDP-glucuronate

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-udp-glucuronate-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 mix02-serum-pos-udp-glucuronate-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 mix02-serum-pos-udp-glucuronate-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, some extracted
spectra are associated to an ion different from `[M+H]+` we perform a neutral
loss spectra comparison.

```{r mix02-serum-pos-udp-glucuronate-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 mix02-serum-pos-udp-glucuronate-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)
```

Still we don't find any good match.

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. Available MS2 spectra don't match and no features found for the
  previously defined retention time.


### Valine

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

```{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, "SP", 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 were removed).

```{r mix02-serum-pos-valine-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 mix02-serum-pos-valine-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 mix02-serum-pos-valine-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)
```

The spectra F07.S0550 and F08.S0573 from FT00670 matches reference spectra for
`r std`.

```{r mix02-serum-pos-valine-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 matches (with similarity scores a bit lower) also for
MassBank.

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

- FT00670 (RT= 166.9, `[M+H]+` ion) with confidence level **B**.
- FT01634 (RT = 167.1, `[M+2Na-H]+` ion) with confidence level **B-**.
- Reference MS2: `[M+H+]`: FT00670_F07.S0550, FT00670_F08.S0573; high
  confidence. `[M+2Na-H]+`: FT01634_F08.S0568; low confidence.
  
The associated ion and MS2 spectra are added to the database.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT00670_F07.S0550", "FT00670_F08.S0573", "FT01634_F08.S0568")]
ms2$confidence <- c("high", "high", "low")
```

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


# Serum, negative polarity

We now perform the analysis on the samples with the standards still solved in
serum 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

### Ascorbic Acid

```{r, echo = FALSE}
std <- "Ascorbic 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 EICs for the assigned features and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-ascorbic-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 mix02-serum-neg-ascorbic-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)
```

Some MS2 spectra match the reference, but with a low similarity score.

```{r mix02-serum-neg-ascorbic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.6, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix02-serum-neg-ascorbic-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)
```

No match was found for reference spectra from MassBank.

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

- FT0705 (RT = 126.5, `[M-H]-` ion) with confidence **C**.
- Reference MS2: `[M-H]-`: FT0705_F15.S0305, FT0705_F15.S0342, FT0705_F16.S0347;
  low confidence.

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

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0705_F15.S0305", "FT0705_F15.S0342", "FT0705_F16.S0347")]
ms2$confidence <- "low"
```

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


### C4 Carnitine

```{r, echo = FALSE}
std <- "C4 Carnitine"
```

```{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 its EIC and visually inspect it (has very
low intensity).

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-c4-carnitine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive.
- FT1390 (RT = 34.54, `[M-H]-` ion) confidence level **D**. But only if we have
  some other evidence (from pos, or water) too.

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("FT1390"),
                  confidence_level = c("D"))
```

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


### Fructose

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

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

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-fructose-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

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

```{r mix02-serum-neg-fructose-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 available spectrum F16.S0457 from FT0761 matches reference spectra from
`r std` but the similarity score of the matches are not very high.  Below we
show the matches with similarity score higher than 0.3.

```{r mix02-serum-neg-fructose-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.3, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix02-serum-neg-fructose-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 in the MassBank case.

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

- FT0761 (RT = 151.5, `[M-H]-` ion) with confidence level **C**.
- Other features of other ions exist, but their EIC looks different.
- Reference MS2: `[M-H]-`: FT0761_F16.S0457; high 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("FT0761"),
                  confidence_level = c("C"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0761_F16.S0457")]
ms2$confidence <- "high"
```

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

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


### Glycine

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

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

One 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, "SN", 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 were removed).

```{r mix02-serum-neg-glycine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- FT0017 (RT=174, `[M-H]-` ion) with confidence level **D**. No MS2 spectra
  available.


### cGMP

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

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-cgmp-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 mix02-serum-neg-cgmp-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 spectra from FT3159 (F15.S0685, F15.S0721, F16.S0711, F16.S0742,
F16.S0767) seem to match.

```{r mix02-serum-neg-cgmp-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 mix02-serum-neg-cgmp-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 mix02-serum-neg-cgmp-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

- FT3159 (RT = 219, `[M-H]-` ion) with confidence level **A**.
- FT4018 (RT = 219, `[M+HCOO]-` ion) confidence level **A-**.
- Reference MS2: `[M-H]-`: FT3159_F15.S0685, FT3159_F15.S0721, FT3159_F16.S0711,
  FT3159_F16.S0742, FT3159_F16.S0767; high confidence.
  
The associated ion and MS2 spectra are added to the database.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT3159_F15.S0685", "FT3159_F15.S0721", "FT3159_F16.S0711",
                 "FT3159_F16.S0742", "FT3159_F16.S0767")]
ms2$confidence <- "low"
```

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


### Histidine

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

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-histidine-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 mix02-serum-neg-histidine-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 reference spectra from HMDB.

```{r mix02-serum-neg-histidine-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 mix02-serum-neg-histidine-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 mix02-serum-neg-histidine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.8, 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

- FT0466 (RT = 188.3, `[M-H]-` ion) with confidence level **A**.
- FT0888 (RT = , `[M+Cl]-` ion) with confidence level **A-**.
- FT0468 (RT = 224, `[M-H]-` ion) with confidence level **A**? Maybe that's just
  some leftover from the column?
- Reference MS2: `[M-H]-`: FT0466_F15.S0603, FT0466_F15.S0684; high 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("FT0466", "FT0888"),
                  confidence_level = c("A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0466_F15.S0603", "FT0466_F15.S0684")]
ms2$confidence <- "high"
```

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

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


### Hydroxyproline

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

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-hydroxyproline-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. The MS2 spectrum does however not match the
reference spectra.

```{r mix02-serum-neg-hydroxyproline-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 mix02-serum-neg-hydroxyproline-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 mix02-serum-neg-hydroxyproline-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.8, 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

- Inconclusive.
- FT0244 (RT = 176.2, `[M-H]-` ion) looks good, but the MS2 spectrum (which is
  also measured **before** the actual compound) matches creatine. Need thus
  evidence from other matrices/polarity to decide.
- FT0591 (RT = 176.9, `[M+Cl]-` ion).
- FT0720 (RT = 174.9, `[M+HCOO]-` ion).



### Hypoxanthine

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

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-hypoxanthine-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 mix02-serum-neg-hypoxanthine-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 matches with high similarity involving F15.S0316 and F16.S0337 from
FT0318.

```{r mix02-serum-neg-hypoxanthine-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 mix02-serum-neg-hypoxanthine-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 the similar matches but with very 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)
```

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

- FT0318 (RT = 123.6, `[M-H]-` ion) with confidence level **B**.
- FT0786 (RT = 124.6, `[M+HCOO]-` ion) with confidence level **B-**.
- Reference MS2: `[M-H]-`: FT0318_F15.S0316, FT0318_F16.S0337; high confidence.

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

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0318_F15.S0316", "FT0318_F16.S0337")]
ms2$confidence <- "high"
```

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


### L-Kynurenine

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

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-l-kynurenine-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 mix02-serum-neg-l-kynurenine-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 mix02-serum-neg-l-kynurenine-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 the available extracted MS2 spectrum is associated to an ion different
from `[M-H]-` we also perform a neutral loss comparison against reference
spectra from HMDB and MassBank.

```{r mix02-serum-neg-l-kynurenine-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 mix02-serum-neg-l-kynurenine-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)
```

Again we find matches(?) with very low score 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)
```

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. Depends on results from water and positive polarity.
- FT1099 (RT = 156, `[M-H]-` ion) with confidence level **D**.
- FT1642 (RT = 150.5, `[M+HCOO]-` ion) with confidence level **D-**.
- FT1525 (RT = 156.1, `[M+Cl]-` ion) with confidence level **D-**.


### Lactic acid

```{r, echo = FALSE}
std <- "Lactic 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, "SN", 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 were removed).

```{r mix02-serum-neg-lactic-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 mix02-serum-neg-lactic-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 mix02-serum-neg-lactic-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 all the
extracted spectra are associated to ions different from `[M-H]-` we perform
a neutral loss spectra comparison but we don't find additional matches as shown
below.

```{r mix02-serum-neg-lactic-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)
```

```{r mix02-serum-neg-lactic-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)
```

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. No signal at the expected RT.
- FT0318 is in fact **Hypoxanthine**.


### 1-Methylxanthine

```{r, echo = FALSE}
std <- "1-Methylxanthine"
```

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-1-methylxanthine-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 mix02-serum-neg-1-methylxanthine-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 MS2 spectra from FT0585 (F15.S0210, F16.S0219, F16.S0251) seem to match
reference spectra from `r std`.

```{r mix02-serum-neg-1-methylxanthine-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 mix02-serum-neg-1-methylxanthine-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

- FT0585 (RT = 71.6, `[M-H]-` ion) with confidence level **A**.
- FT1146 (RT = 72.3, `[M+HCOO]-` ion) with confidence level **A-**.
- Reference MS2: `[M-H]-`: FT0585_F15.S0210, FT0585_F16.S0219, FT0585_F16.S0251;
  high confidence.

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

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0585_F15.S0210", "FT0585_F16.S0219", "FT0585_F16.S0251")]
ms2$confidence <- "high"
```

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


### 3-Nitrotyrosine

```{r, echo = FALSE}
std <- "3-Nitrotyrosine"
```

```{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, "SN", 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 were removed).

```{r mix02-serum-neg-3-nitrotyrosine-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 mix02-serum-neg-3-nitrotyrosine-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 good matches for spectra from FT1305 (especially F15.S0425 but also
F16.S0459).

```{r mix02-serum-neg-3-nitrotyrosine-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 mix02-serum-neg-3-nitrotyrosine-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

- FT1305 (RT = 156.1, `[M-H]-` ion) with confidence **A**.
- FT1761 (RT = 157.4, `[M+Cl]-` ion) with confidence **A-**.
- Reference MS2: `[M-H]-`: FT1305_F15.S0425, FT1305_F16.S0459; high 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("FT1305", "FT1761"),
                  confidence_level = c("A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[c("FT1305_F15.S0425", "FT1305_F16.S0459")]
ms2$confidence <- "high"
```

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

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


### Phosphocreatine

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

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

Only a single feature was found.

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

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-phosphocreatine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive? Low signal.
- FT1136 (RT = 206, `[M-H]-` ion) with confidence **D**.


### UDP-glucuronate

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

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

A single feature was found, with low intensity and not at the expected RT.

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

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-udp-glucuronate-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive. Signal of the only feature is low, and also no large difference
  between low and high concentration.


### Valine

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

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

Two features with about the same retention time were found.
We next plot the EIC for the assigned feature and visually inspect these.

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SN", 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 were removed).

```{r mix02-serum-neg-valine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- FT0152 (RT = 166.7, `[M-H]-` ion) with confidence **D**.
- FT0542 (RT = 166.8, `[M+HCOO]-` ion) with confidence **D-**.


## 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()
```