match-standards-water-mix05.Rmd

---
title: "Identifying mix05 standards in water"
author: "Andrea Vicini, Vinicius Verri Hernandes, Johannes Rainer"
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    toc_float: true
    toc_depth: 3
    fig_width: 5
---

```{r, include = FALSE, cached = FALSE}
knitr::read_chunk("R/match-standards-chunks.R")
MIX <- 5
MATRIX <- "Water"
POLARITY <- "POS"
## settings to find MS2 spectra for features. Be rather inclusive here.
FEATURE_MS2_PPM <- 20
FEATURE_MS2_TOLERANCE <- 0.05
```

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

**Current version: 0.10.0, 2022-05-30.**

# Introduction

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

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


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

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

# Data import

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

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

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

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

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

# Initial evaluation of available MS2 data

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

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

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

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

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

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

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

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

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

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

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

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


# Water, positive polarity

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

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

## Data pre-processing

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

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

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

## Signal intensity difference

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

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

## Identification of features matching standards

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

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

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

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

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

## Standards with matching features

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

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

### dADP

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

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

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

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

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

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

```{r mix05-water-pos-dadp-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 mix05-water-pos-dadp-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 mix05-water-pos-dadp-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 mix05-water-pos-dadp-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- FT4724 (RT= 260.4, `[M+H]+` ion) with confidence level **A**.
- Reference MS2: `[M+H+]`: FT4724_F07.S0978, FT4724_F08.S0873, FT4724_F08.S0917;
  high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT4724"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT4724_F07.S0978", "FT4724_F08.S0873", "FT4724_F08.S0917")]
ms2$confidence <- c("high")
```

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

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

### dCDP

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

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

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

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

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

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

```{r mix05-water-pos-dcdp-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive: no MS2 spectra available; needs to be reconfirmed with other
  polarity and serum data set.
- EICs all have very low intensity.
- Not picking any feature.


### CoA

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

```{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.

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

The only available (cleaned) MS2 spectra 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 mix05-water-pos-coa-ms2, echo = FALSE}
plot_spectra(std_ms2)
```


#### Summary

- Inconclusive: no MS2 spectra available for the only assigned feature. EIC has
  very low intensity. Needs to be reconfirmed by other polarity or serum data
  set.
- Not picking any feature.


### Corticosterone

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

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

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

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

```{r mix05-water-pos-corticosterone-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 mix05-water-pos-corticosterone-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 from features FT3772 and FT3773 match HMDB reference spectra for
`r std`. Spectra from FT3418 match with low similarity.

```{r mix05-water-pos-corticosterone-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)
```

These are amazingly similar MS2 spectra. We obtain similar results with the
MassBank comparison as we show below.

```{r mix05-water-pos-corticosterone-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 mix05-water-pos-corticosterone-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

- FT3772 (RT = 34.5, `[M+H]+` ion) with confidence **B** (or A?).
- FT3418 (RT = 34.8, `[M+H-H2O]+` ion) with confidence **B-**.
- FT4138 (RT = 34.5, `[M+Na]+` ion) with confidence **B-**.
- Reference MS2: `[M+H]+`: FT3772_F07.S0117, FT3772_F08.S0112; high confidence.
  `[M+H-H2O]+`: FT3418_F07.S0115, FT3418_F08.S0089; low confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT3772_F07.S0117", "FT3772_F08.S0112", "FT3418_F07.S0115",
                 "FT3418_F08.S0089")]
ms2$confidence <- c(rep("high", 2), rep("low", 2))
```

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


### 1,3-Dimethyluric acid

```{r, echo = FALSE}
std <- "1,3-Dimethyluric acid"
```

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

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

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

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

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

```{r mix05-water-pos-13-dimethyluric-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 mix05-water-pos-13-dimethyluric-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 mix05-water-pos-13-dimethyluric-acid-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 mix05-water-pos-13-dimethyluric-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix05-water-pos-13-dimethyluric-acid-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

- FT1518 (RT = 113.4, ion `[M+H]+`) with confidence **B**.
- FT1949 (RT = 112.2, ion `[M+K]+`) with confidence **B-**.
- FT1759 (RT = 112.5, ion `[M+Na]+`) with confidence **B-**.
- FT2011 (RT = 101, ion `[M+2Na-H]+`) with confidence **B-**.
- Reference MS2: `[M+H]+`: FT1518_F07.S0333, FT1518_F07.S0386, FT1518_F07.S0412,
  FT1518_F08.S0391; high confidence. `[M+Na]+`: FT1759_F08.S0340,
  FT1759_F08.S0380; low confidence. `[M+2Na-H]`: FT2011_F07.S0390,
  FT2011_F08.S0318, FT2011_F08.S0367; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1518", "FT1949", "FT1759", "FT2011"),
                  confidence_level = c("B", "B-", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT1518_F07.S0333", "FT1518_F07.S0386", "FT1518_F07.S0412",
                 "FT1518_F08.S0391", "FT1759_F08.S0340", "FT1759_F08.S0380",
                 "FT2011_F07.S0390", "FT2011_F08.S0318", "FT2011_F08.S0367")]
ms2$confidence <- c(rep("high", 4), rep("low", 5))
```

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

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


### Glycerophospho-inositol

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

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

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

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

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

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

```{r mix05-water-pos-glycerophospho-inositol-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 mix05-water-pos-glycerophospho-inositol-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 mix05-water-pos-glycerophospho-inositol-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)
```

#### Summary

- Inconclusive. Need to evaluate negative polarity or serum data.
- Some EICs show high signal, but it is not clear which retention time would be
  correct.
- Not picking any feature.

  
### GTP

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

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

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

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

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

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

```{r mix05-water-pos-gtp-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive. No MS2 spectra available. The two EIC show low intensity signal.
- Not picking any feature.


### 2-Ketobutyric acid

```{r, echo = FALSE}
std <- "2-Ketobutyric acid"
```

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

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

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

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

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

```{r mix05-water-pos-2-ketobutyric-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 mix05-water-pos-2-ketobutyric-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 mix05-water-pos-2-ketobutyric-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 good match is found with HMDB or MassBank comparison. In addition, since all
of the extracted spectra are associated to ions different from `[M+H]+` we
perform a neutral loss spectra comparison.

```{r mix05-water-pos-2-ketobutyric-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 mix05-water-pos-2-ketobutyric-acid-hmdb_nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.2, ppm = csp_nl@ppm,
                               tolerance = csp_nl@tolerance)
```

```{r mix05-water-pos-2-ketobutyric-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)
```

With HMDB neutral loss comparison we find new low similarity matches for spectra
from FT0512 and FT0513.

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

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

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


#### Summary

- Inconclusive. Spectra from FT0512 and FT0513 (that match with low similarity
  reference spectra from `r std`) match with high similarity (and m/z precursor
  difference close to 0) reference spectra for other compounds such as
  D-Threonine. Need to evaluate negative polarity and serum data.
- Features with RT 170 are most likely Threonine (since that's also measured in
  this mix).
- Not picking any feature.


### Mannose

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

```{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 features and visually inspect these.

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

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

```{r mix05-water-pos-mannose-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 mix05-water-pos-mannose-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 only find a match with very low similarity score and we show below its mirror
plot.

```{r mix05-water-pos-mannose-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.05, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-pos-mannose-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 two available MS2 spectra are associated to an ion different from
`[M+H]+` we also perform a neutral loss comparison against reference spectra
from HMDB and MassBank but we don't find good matches as we show below.

```{r mix05-water-pos-mannose-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 mix05-water-pos-mannose-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)
```


#### Summary

- FT1537 (RT = 167.4, `[M+NH4]+` ion) confidence level **C**.
- FT1607 (RT = 163.4, `[M+Na]+` ion) confidence level **C-**.
- FT1758 (RT = 165.8, `[M+K]+` ion) confidence level **C-**.
- Reference MS2: `[M+NH4]+`: FT1537_F07.S0617, FT1537_F08.S0576; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1537", "FT1607", "FT1758"),
                  confidence_level = c("C", "C-", "C-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT1537_F07.S0617", "FT1537_F08.S0576")]
ms2$confidence <- c("low")
```

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

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


### 5-MTA

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

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

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

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

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

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

```{r mix05-water-pos-5-mta-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 mix05-water-pos-5-mta-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 FT2817, FT2818, FT2819, FT2821, FT2822 seem to match
reference spectra from `r std` with high similarity scores.

```{r mix05-water-pos-5-mta-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.85, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-pos-5-mta-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

- EIC signal is bimodal with the first peak at 51 (FT2817) and second at 83
  (FT2818).
- FT2817 (RT = 51.1, `[M+H]+` ion) with confidence level **B**.
- FT3251 (RT = 51, `[M+Na]+` ion) with confidence level **B-**.
- Reference MS2: `[M+H]+`: FT2817_F07.S0160, FT2817_F07.S0210, FT2817_F08.S0153,
  FT2817_F08.S0199; FT2818_F08.S0240; high confidence.
  `[M+Na]+`: FT3251_F07.S0172, FT3251_F08.S0161; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2817", "FT3251"),
                  confidence_level = c("B", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2817_F07.S0160", "FT2817_F07.S0210", "FT2817_F08.S0153",
                 "FT2817_F08.S0199", "FT2818_F08.S0240", "FT3251_F07.S0172",
                 "FT3251_F08.S0161")]
ms2$confidence <- c(rep("high", 5), rep("low", 2))
```

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

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


### NADP

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

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

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

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

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

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

```{r mix05-water-pos-nadp-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 mix05-water-pos-nadp-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 FT6971 seem to match reference spectra from `r std` with
low similarity.

```{r mix05-water-pos-nadp-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.3, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-pos-nadp-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, since all the extracted spectra are associated to ions different
from `[M+H]+` we perform a neutral loss spectra comparison.

```{r mix05-water-pos-nadp-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)
```

With neutral loss comparison we get a low similarity match also for
FT6922_F08.S0927.

```{r mix05-water-pos-nadp-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)
```

#### Summary

- FT6971 (RT = 259.4, `[M+H]+` ion) with confidence level **C**.
- FT4181 (RT = 259.5, `[M+2H]2+` ion) with confidence level **C-**.
- FT6922 (RT = 261.4, `[M+H-H2O]+` ion) with confidence level **C**.
- Reference MS2: `[M+H]+`: FT6971_F07.S0957, FT6971_F08.S0891; low confidence;
  `[M+H-H2O]+`: FT6922_F08.S0927; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT6971", "FT4181", "FT6922"),
                  confidence_level = c("C", "C-", "C"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT6971_F07.S0957", "FT6971_F08.S0891", "FT6922_F08.S0927")]
ms2$confidence <- c("low")
```

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

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


### Nicotinic acid

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

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

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

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

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

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

```{r mix05-water-pos-nicotinic-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 mix05-water-pos-nicotinic-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 mix05-water-pos-nicotinic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.8, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We have very good matches for F07.S0574 from FT2571.

```{r mix05-water-pos-nicotinic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- EIC show bimodal signal with the first peak at around 37, the second at around
  45. We pick features from the first peak.
- FT0568 (RT=37.37, `[M+H]+` ion) with confidence level **A**.
- Reference MS2: `[M+H]+`: FT0568_F07.S0122, FT0568_F08.S0118, FT0569_F08.S0437,
  FT0567_F08.S0118, FT0567_F07.S0184; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0568"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0568_F07.S0122", "FT0568_F08.S0118", "FT0569_F08.S0437",
                 "FT0567_F08.S0118", "FT0567_F07.S0184")]
ms2$confidence <- c("high")
```

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

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


### Orotic acid

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

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

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

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

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

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

```{r mix05-water-pos-orotic-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 mix05-water-pos-orotic-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)
```

We find high similarity matches for spectra from FT1034 and low similarity ones
for spectra from FT0779. We report below the mirror plots of the matches with
similarity higher than 0.7.

```{r mix05-water-pos-orotic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

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

```{r mix05-water-pos-orotic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- FT1034 (RT = 178.4, `[M+H]+` ion) with confidence level **A**.
- FT0779 (RT = 179.8, `[M+H-H2O]+` ion) with confidence level **A-**.
- Reference MS2: `[M+H]+`: FT1034_F07.S0641, FT1034_F08.S0595; high confidence.
  `[M+H-H2O]+`: FT0779_F07.S0640, FT0779_F08.S0594; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1034", "FT0779"),
                  confidence_level = c("A", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT1034_F07.S0641", "FT1034_F08.S0595", "FT0779_F07.S0640",
                 "FT0779_F08.S0594")]
ms2$confidence <- c(rep("high", 2), rep("low", 2))
```

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

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


### PABA

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

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

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

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

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

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

```{r mix05-water-pos-paba-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 mix05-water-pos-paba-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 mix05-water-pos-paba-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 match is pretty OK.

```{r mix05-water-pos-paba-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

- Inconclusive: needs verification with other polarity and serum data. EIC for
  feature FT0769 is not that clear. Also, while the match with the reference MS2
  is pretty good, there are also matches with other compounds.
- FT0769 (RT= 119.6, `[M+H]+` ion) with confidence level **B**.
- Reference MS2: `[M+H]+`: FT0769_F08.S0425; high confidence. 
- Not selecting any feature.


### Threonine

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

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

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

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

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

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

```{r mix05-water-pos-threonine-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 mix05-water-pos-threonine-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 mix05-water-pos-threonine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-pos-threonine-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 spectrum FT0292_F07.S0627 is associated to an ion different from
`[M+H]+` we perform a neutral loss spectra comparison.

```{r mix05-water-pos-threonine-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 mix05-water-pos-threonine-hmdb_nl, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2_nl, std_hmdb_nl, 0.8, ppm = csp_nl@ppm,
                               tolerance = csp_nl@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

- FT0512 (RT=173.7, `[M+H]+` ion) with confidence level **B**.
- FT0292 (RT=173.7, `[M+H-H2O]+` ion) with confidence level **B-**.
- FT0849 (RT=172.2, `[M+Na]+` ion) with confidence level **B-**.
- FT1134 (RT=172.2, `[M+2Na-H]+` ion) with confidence level **B-**.
- Reference MS2: `[M+H]+`: FT0512_F07.S0628, FT0512_F07.S0701, FT0512_F08.S0586;
  high confidence. `[M+H-H2O]+`: FT0292_F07.S0627.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0512", "FT0292", "FT0849", "FT1134"),
                  confidence_level = c("B", "B-", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0512_F07.S0628", "FT0512_F07.S0701", "FT0512_F08.S0586",
                 "FT0292_F07.S0627")]
ms2$confidence <- c(rep("high", 3), "low")
```

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

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


# Water, negative polarity

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

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

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

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


## Signal intensity difference

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

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

## Identification of features matching standards

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

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

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

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

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

## Standards with matching features

### dADP

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

```{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, "WN", std_ms2)
```

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

```{r mix05-water-neg-dadp-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 mix05-water-neg-dadp-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 obtain low similarity scores for spectra from features FT2897, FT2899,
FT2900, FT2898.

```{r mix05-water-neg-dadp-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.3,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

While similarity scores are not that high, the spectra (fragments) seem to match
relatively well.

```{r mix05-water-neg-dadp-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 some of the extracted spectra are associated to ions different from
`[M-H]-` we perform a neutral loss spectra comparison.

```{r mix05-water-neg-dadp-ms2-hmdb_nl, echo = FALSE, fig.width = 5, fig.height = 5}
std_hmdb_nl <- hmdb_nl[hmdb_nl$compound_id == hmdb_id]
std_ms2_nl <- neutralLoss(std_ms2, nl_param)
sim <- plot_ms2_similarity_heatmap(std_ms2_nl, std_hmdb_nl, csp_nl)
```

We obtain higher scores for the matches found before but the still no match is
found for spectra from features FT3295 and FT3296.

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

- FT2900 (RT = 260, `[M-H]-` ion) with confidence level **C**.
- Reference MS2: `[M-H]-`: FT2900_F15.S0784, FT2900_F15.S0830, FT2900_F16.S0735,
  FT2900_F16.S0806, FT2898_F16.S1023; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2900"),
                  confidence_level = c("C"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2900_F15.S0784", "FT2900_F15.S0830", "FT2900_F16.S0735",
                 "FT2900_F16.S0806", "FT2898_F16.S1023")]
ms2$confidence <- c("low")
```

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

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


### dCDP

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

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

Many features has been assigned to this standard based on m/z matching. We
next plot their EIC and visually inspect them.

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

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

```{r mix05-water-neg-dcdp-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 mix05-water-neg-dcdp-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)
```

Each extracted spectrum seem to match HMDB reference spectra from `r std`.

```{r mix05-water-neg-dcdp-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

We obtain similar results for MassBank as we show below.

```{r mix05-water-neg-dcdp-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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


#### Summary

- Tricky situation. Not a nice single peak but several peaks across a longer RT
  stretch. Maybe pick two features and report them? MS2 peaks match for all very
  well.
- FT2598 (RT = 233.7, `[M-H]-` ion): with confidence level **A**.
- FT2597 (RT = 259.7, `[M-H]-` ion): with confidence level **A**.
- Reference MS2: `[M-H]-`: FT2597_F16.S0794, FT2602_F15.S0708, FT2602_F15.S0783,
  FT2602_F16.S0734, FT2602_F16.S0794; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2598", "FT2597"),
                  confidence_level = c("A", "A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2597_F16.S0794", "FT2602_F15.S0708", "FT2602_F15.S0783",
                 "FT2602_F16.S0734", "FT2602_F16.S0794")]
ms2$confidence <- c("high")
```

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

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


### CoA

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

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

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

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

```{r mix05-water-neg-coa-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

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

```{r mix05-water-neg-coa-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix05-water-neg-coa-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.05, ppm = csp@ppm,
                           tolerance = csp@tolerance)
```

Matches are not that good.

```{r mix05-water-neg-coa-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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


#### Summary

- Inconclusive: from the signal (EIC) it looks like the feature at 203 seconds
  is the most likely candidate, but the other two match Acetaoacetyl-CoA with
  high similarity - the difference of precursor m/z exactly matching the
  difference of C4H4O2 between CoA and that compound.
- FT5135 (RT = 203, `[M-H]-` ion) with confidence level **D**.
- FT5136 (RT = 259.1, `[M-H]-` ion) with confidence level **C**.
- FT5137 (RT = 305.6, `[M-H]-` ion) with confidence level **C**.
- Eventually Reference MS2: FT5136_F16.S0878, FT5137_F16.S0972,
  FT5137_F16.S0998; low confidence.
- Not selecting any feature.


### Corticosterone

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

```{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 the EIC for the assigned feature and visually inspect these.

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

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

```{r mix05-water-neg-corticosterone-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 and we perform a neutral loss comparison as well.

```{r mix05-water-neg-corticosterone-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 mix05-water-neg-corticosterone-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 mix05-water-neg-corticosterone-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 mix05-water-neg-corticosterone-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)
```

No good match was found.

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

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

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

#### Summary

- FT2657 (RT = 33.7, `[M+HCOO]-` ion): with confidence level **D**.
- FT2535 (RT = 33.7, `[M+Cl]-` ion): with confidence level **D**.
- Reference MS2: `[M+HCOO]-`: FT2657_F15.S0111; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2657", "FT2535"),
                  confidence_level = c("D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2657_F15.S0111")]
ms2$confidence <- c("low")
```

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

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


### 1,3-Dimethyluric acid

```{r, echo = FALSE}
std <- "1,3-Dimethyluric acid"
```

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

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

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

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

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

```{r mix05-water-neg-13-dimethyluric-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 mix05-water-neg-13-dimethyluric-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)
```

All the extract spectra seem to match some of the HMDB reference spectra for
`r std`.

```{r mix05-water-neg-13-dimethyluric-acid-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 obtain similar results with the MassBank comparison as we show below.

```{r mix05-water-neg-13-dimethyluric-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix05-water-neg-13-dimethyluric-acid-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

- Signal is very high and results in a broad peak which is split into several
  smaller ones. Picking several features with different RT.
- FT0608 (RT = 84.08, `[M-H]-` ion) confidence level **B**.
- FT0605 (RT = 112.9, `[M-H]-` ion) confidence level **B**.
- Reference MS2: `[M-H]-`: FT0608_F15.S0280, FT0608_F16.S0284, FT0609_F16.S0503,
  FT0609_F15.S0487; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0608", "FT0605"),
                  confidence_level = c("B", "B"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0608_F15.S0280", "FT0608_F16.S0284", "FT0609_F16.S0503",
                 "FT0609_F15.S0487")]
ms2$confidence <- c("high")
```

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

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


### Glycerophospho-inositol

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

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

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

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

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

```{r mix05-water-neg-glycerophospho-inositol-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive. EIC data is rather low intensity.
- FT1975 (RT = 33, `[M-H]-` ion): with confidence level **D**.
- Not picking any feature.


### GTP

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

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

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

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

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

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

```{r mix05-water-neg-gtp-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 mix05-water-neg-gtp-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 available spectra (from feature FT4029) seem to match HMDB reference
spectra from `r std`.

```{r mix05-water-neg-gtp-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 mix05-water-neg-gtp-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- FT4029 (RT = 260.3, `[M-H]-` ion) with confidence **A**.
- Reference MS2: `[M-H]-`: FT4029_F15.S0835, FT4029_F16.S0867, FT4029_F16.S0868;
  high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT4029"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT4029_F15.S0835", "FT4029_F16.S0867", "FT4029_F16.S0868")]
ms2$confidence <- c("high")
```

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

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


### 2-Ketobutyric acid

```{r, echo = FALSE}
std <- "2-Ketobutyric acid"
```

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

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

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

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

The only available (cleaned) MS2 spectrum 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 mix05-water-neg-2-ketobutyric-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 mix05-water-neg-2-ketobutyric-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)
```

The available spectrum matches two of the HMDB reference spectra for `r std`
with similarity around 0.6. The matches however involve only one peak as we
show in the mirror plots below.

```{r mix05-water-neg-2-ketobutyric-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

Only the precursor m/z is matching.In addition, the reference spectrum consists
also of only the precursor peak without any additional fragments.  We obtain
similar results with the MassBank comparison as we show below.

```{r mix05-water-neg-2-ketobutyric-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- FT0038 (RT = 38, `[M-H]-` ion) with confidence level **C**.
- FT0297 (RT = 38.1, `[M+HCOO]-` ion) with confidence level **C-**.
- Reference MS2: `[M-H]-`: FT0038_F15.S0096; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0038", "FT0297"),
                  confidence_level = c("C", "C-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0038_F15.S0096")]
ms2$confidence <- c("high")
```

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

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


### Mannose

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

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

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

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

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

```{r mix05-water-neg-mannose-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 mix05-water-neg-mannose-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 mix05-water-neg-mannose-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 FT0768_F16.S0479 is associated to an ion different from `[M-H]-` we also
perform a neutral loss comparison against reference spectra from HMDB and
MassBank.

```{r mix05-water-neg-mannose-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 mix05-water-neg-mannose-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 don't find any matches. In addition we match (**all**) the
MS2 spectra for the matched features against all spectra from HMDB or MassBank
identifying reference spectra with a similarity larger than 0.7. The results
(if any spectra matched) are shown in the two following tables.

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

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

#### Summary

- FT0487 (RT = 167.6, `[M-H]-` ion): with confidence level **D**.
- FT0839 (RT = 165.3, `[M+HCOO]-` ion): with confidence level **D**.
- FT0768 (RT = 166.7, `[M+Cl]-` ion): with confidence level **D**.
- Reference MS2: `[M+HCOO]-`: FT0839_F15.S0471, FT0768_F16.S0479; low
  confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0487", "FT0839", "FT0768"),
                  confidence_level = c("D", "D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0839_F15.S0471", "FT0768_F16.S0479")]
ms2$confidence <- c("low")
```

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

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


### 5-MTA

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

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

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

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

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

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

```{r mix05-water-neg-5-mta-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 mix05-water-neg-5-mta-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 mix05-water-neg-5-mta-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 mix05-water-neg-5-mta-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

- Signal is again bimodal and distributes over a longer RT stretch. We report
  only the first feature.
- FT2104 (RT = 45.1, `[M+HCOO]-`) with confidence **B**.
- FT1441 (RT = 44.3, `[M-H]-`) with confidence **B**.
- FT1965 (RT = 45.1, `[M-Cl]-`) with confidence **B-**.
- Reference MS2: `[M+HCOO]-`: FT2104_F15.S0164; high confidence. `[M-H]-`:
  FT1441_F15.S0163, FT1441_F16.S0163; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2104", "FT1441", "FT1965"),
                  confidence_level = c("B", "B", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2104_F15.S0164", "FT1441_F15.S0163", "FT1441_F16.S0163")]
ms2$confidence <- c(rep("high", 1), rep("low", 2))
```

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

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


### NADP

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

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

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

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

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

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

```{r mix05-water-neg-nadp-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 mix05-water-neg-nadp-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 FT1646 (F07.S0185, F07.S0223, F08.S0190) and FT1642
(F07.S0223, F07.S0271, F08.S0270) seem to match reference spectra from `r std`.

```{r mix05-water-neg-nadp-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)
```

There is only a single peak matching.

```{r mix05-water-neg-nadp-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

- FT5078 (RT = 259.2, `[M-H]-` ion) with confidence level **A**.
- Reference MS2: FT5078_F15.S0823, FT5078_F16.S0877; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT5078"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT5078_F15.S0823", "FT5078_F16.S0877")]
ms2$confidence <- c("high")
```

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

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


### Nicotinic acid

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

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

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

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

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

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

```{r mix05-water-neg-nicotinic-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 mix05-water-neg-nicotinic-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)
```

We find good matches for all the available spectra.

```{r mix05-water-neg-nicotinic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-neg-nicotinic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

- FT0118 (RT = 44.69, `[M-H]-` ion) with confidence **B**.
- FT0415 (RT = 44.47, `[M+HCOO]-` ion) with confidence **B-**.
- Reference MS2: `[M-H-]`: FT0118_F15.S0118, FT0118_F15.S0176; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0118", "FT0415"),
                  confidence_level = c("B", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0118_F15.S0118", "FT0118_F15.S0176")]
ms2$confidence <- c("high")
```

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

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


### Orotic acid

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

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

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

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

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

```{r mix05-water-neg-orotic-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 mix05-water-neg-orotic-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 mix05-water-neg-orotic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.8, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

Matches are mostly due to a single peak.

```{r mix05-water-neg-orotic-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)
```

All available spectra (from feature FT0331) seem to match some reference spectra
from `r std`.

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

- FT0331 (RT = 175.5, `[M-H]-` ion) confidence level **A**.
- Reference MS2: FT0331_F15.S0518, FT0331_F16.S0521, FT0331_F16.S0556; high
  confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0331"),
                  confidence_level = c("A"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0331_F15.S0518", "FT0331_F16.S0521", "FT0331_F16.S0556")]
ms2$confidence <- c(rep("high", 3))
```

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

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


### PABA

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

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

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

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

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

```{r mix05-water-neg-paba-ms2, echo = FALSE}
plot_spectra(std_ms2)
```


#### Summary

- Inconclusive. There would be two features, both with low signal intensity and
  different RT. Requires validation by positive polarity or serum data.
- ? FT0225 (RT = 44.7, `[M-H]-` ion): with confidence level **D**?
- ? FT0224 (RT = 142, `[M-H]-` ion): with confidence level **D**?
- No feature selected.


### Threonine

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

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

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

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

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

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

```{r mix05-water-neg-threonine-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 mix05-water-neg-threonine-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 mix05-water-neg-threonine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix05-water-neg-threonine-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

- Inconclusive: while the MS2 spectrum with a very high retention time (over 190
  seconds) matches the reference quite well, the EIC for that feature (FT0101)
  does not look convincing while the two others at RT 172 look much
  clearer. Needs validation by positive polarity or serum data.
- FT0100 (RT = 172, `[M-H]-` ion) with confidence level **D**.
- FT0392 (RT = 173, `[M+HCOO]-` ion) with confidence level **D**.
- Eventual reference MS2 (if no other, better one found for serum data):
  `[M-H]-`: FT0101_F16.S0573; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0100", "FT0392"),
                  confidence_level = c("D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT0101_F16.S0573")]
ms2$confidence <- c("low")
```

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

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


## Standards without any matching feature

# Summary on the ion database

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

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

# Session information

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

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