match-standards-serum-mix07.Rmd

---
title: "Identifying mix07 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 <- 7
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, 2022-05-30.**

# Introduction

Mixes of standards have been solved in serum 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 pure
serum 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.

### ADMA

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

```{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 mix07-serum-pos-adma-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 mix07-serum-pos-adma-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 FT02314, FT00410, FT02316, FT02323, FT02318 match some of
the HMDB reference spectra for `r std`. Below we show the mirror plots of the
best matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-adma-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 mix07-serum-pos-adma-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

- Using confidence **B** because we can't distinguish between ADMA and SDMA.
- FT02314 (RT = 178.05, ion `[M+H]+`) with confidence **B**.
- FT02783 (RT = 178.67, ion `[M+Na]+`) with confidence **B**.
- FT03047 (RT = 178.7, ion `[M+K]+`) with confidence **B-**.
- FT03149 (RT = 178.39, ion `[M+2Na-H]+`) with confidence **B-**.
- FT00410 (RT = 178.67, ion `[M+2H]2+`) with confidence **B-**.
- FT02023 (RT = 178.67, ion `[M+H-NH3]+`) with confidence **B-**.
- Reference MS2: `[M+H]+`: FT02314_F07.S0614, FT02314_F07.S0657,
  FT02314_F08.S0612, FT02314_F08.S0659, FT02323_F08.S0839; high
  confidence. `[M+Na]+`: FT02783_F07.S0623, FT02783_F08.S0622; low
  confidence. `[M+2H]2+`: FT00410_F07.S0611; low confidence. 

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT02314", "FT02783", "FT03047", "FT03149",
                                 "FT00410", "FT02023"),
                  confidence_level = c("B", "B", "B-", "B-", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT02314_F07.S0614", "FT02314_F07.S0657", "FT02314_F08.S0612",
                 "FT02314_F08.S0659", "FT02323_F08.S0839", "FT02783_F07.S0623",
                 "FT02783_F08.S0622", "FT00410_F07.S0611")]
ms2$confidence <- c(rep("high", 5), rep("low", 3))
```

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

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


### dAMP

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

```{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, "WP", std_ms2)
```

The extracted MS2 spectra (cleaned) 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 mix07-serum-pos-damp-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 mix07-serum-pos-damp-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 FT05471 match with good similarity (those from FT06398
with low) some of the HMDB reference spectra for `r std`. Below we show the
mirror plots of the best matches (similarity score  greater than 0.8).

```{r mix07-serum-pos-damp-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

We find similar results with MassBank comparison as shown below.

```{r mix07-serum-pos-damp-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

- FT05471 (RT = 209.2, ion `[M+H]+`) with confidence **A**.
- FT06012 (RT = 210.53, ion `[M+Na]+`) with confidence **A-**.
- FT06398 (RT = 208.92, ion `[M+K]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT05471_F07.S0734, FT05471_F07.S0760,
  FT05471_F08.S0753; high confidence. `[M+K]+`: FT06398_F07.S0746,
  FT06398_F08.S0754; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT05471", "FT06012", "FT06398"),
                  confidence_level = c("A", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT05471_F07.S0734", "FT05471_F07.S0760", "FT05471_F08.S0753",
                 "FT06398_F07.S0746", "FT06398_F08.S0754")]
ms2$confidence <- c(rep("high", 3), rep("low", 2))
```

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

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


### Caffeine

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

```{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 mix07-serum-pos-caffeine-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 mix07-serum-pos-caffeine-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 mix07-serum-pos-caffeine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

We find similar results with MassBank comparison as shown below.

```{r mix07-serum-pos-caffeine-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

- FT02162 (RT = 35.8, `[M+H]+` ion) with confidence level **A**.
- FT02934 (RT = 39.3, `[M+K]+` ion) with confidence level **A-**.
- Reference MS2: `[M+H]+`: FT02162_F07.S0126, FT02162_F08.S0121; high
  confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT02162_F07.S0126", "FT02162_F08.S0121")]
ms2$confidence <- c("high")
```

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


### CDP-choline

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

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

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

The extracted 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 mix07-serum-pos-cdp-choline-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 mix07-serum-pos-cdp-choline-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 feature FT08696 match with good similarity some of the HMDB
reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-cdp-choline-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 mix07-serum-pos-cdp-choline-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

- FT08696 (RT = 219.39, ion `[M+H]+`) with confidence **A**.
- FT09042 (RT = 219.9, ion `[M+Na]+`) with confidence **A-**.
- FT09310 (RT = 220.2, ion `[M+K]+`) with confidence **A-**.
- FT03111 (RT = 220.1, ion `[M+2H]2+`) with confidence **A-**.
- FT03296 (RT = 219.64, ion `[M+H+Na]2+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT08696_F07.S0768, FT08696_F07.S0800,
  FT08696_F08.S0766, FT08696_F08.S0797; high confidence. `[M+Na]+`:
  FT09042_F07.S0770, FT09042_F08.S0774; low confidence. `[M+K]+`:
  FT09310_F07.S0779, FT09310_F08.S0776; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT08696", "FT09042", "FT09310", "FT03111",
                                 "FT03296"),
                  confidence_level = c("A", "A-", "A-", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT08696_F07.S0768", "FT08696_F07.S0800", "FT08696_F08.S0766",
                 "FT08696_F08.S0797", "FT09042_F07.S0770", "FT09042_F08.S0774",
                 "FT09310_F07.S0779", "FT09310_F08.S0776")]
ms2$confidence <- c(rep("high", 4), rep("low", 4))
```

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

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


### Creatinine

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

```{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 mix07-serum-pos-creatinine-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 mix07-serum-pos-creatinine-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 FT00609, FT00613, FT00610 match with good similarity some
of the HMDB reference spectra for `r std`. Below we show the mirror plots of
the best matches (similarity score  greater than 0.85).

```{r mix07-serum-pos-creatinine-mirror-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)
```

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

```{r mix07-serum-pos-creatinine-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 mix07-serum-pos-creatinine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.85, 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 broad and consists of multiple features along the retention
  time. We below select the first feature.
- FT00609 (RT = 89.01, ion `[M+H]+`) with confidence **A**.
- FT00988 (RT = 87.73, ion `[M+Na]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT00609_F07.S0286, FT00609_F07.S0335,
  FT00609_F07.S0358, FT00609_F08.S0300, FT00609_F08.S0335; high
  confidence. `[M+Na]+`: FT00988_F07.S0308, FT00988_F07.S0346,
  FT00988_F08.S0301, FT00988_F08.S0336; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT00609", "FT00988"),
                  confidence_level = c("A", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT00609_F07.S0286", "FT00609_F07.S0335", "FT00609_F07.S0358",
                 "FT00609_F08.S0300", "FT00609_F08.S0335", "FT00988_F07.S0308",
                 "FT00988_F07.S0346", "FT00988_F08.S0301", "FT00988_F08.S0336")]
ms2$confidence <- c(rep("high", 5), rep("low", 4))
```

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

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


### Glutaric acid

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

```{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 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 mix07-serum-pos-glutaric-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive; needs validation from negative polarity or water data. As an
  alternative to the two features selected below also FT01659 `[M+K]+` ion could
  be used.
- FT00627 (RT = 29.8, `[M+H-H2O]+` ion) with confidence level **D**.
- FT00942 (RT = 29.8, `[M+H]+` ion) with confidence level **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT00627", "FT00942"),
                  confidence_level = c("D", "D"))
```

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

  
### Glycero-phosphocholine

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

```{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 mix07-serum-pos-glycero-phosphocholine-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 but we don't find any match.

```{r mix07-serum-pos-glycero-phosphocholine-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 mix07-serum-pos-glycero-phosphocholine-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", "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)
```

#### Summary

- Inconclusive; needs validation by negative polarity or water data.
- FT03337 (RT = 191.3, `[M+H]+` ion) with confidence level **D**.
- FT03832 (RT = 191.2, `[M+Na]+` ion) with confidence level **D**.
- FT04395 (RT = 191.3, `[M+K]+` ion) with confidence level **D**.
- FT03029 (RT = 191.8, `[M+H-H2O]+` ion) with confidence level **D**.
- Reference MS2: `[M+H]+`: FT03337_F07.S0658, FT03337_F08.S0660; low
  confidence. `[M+Na]+`: FT03832_F08.S0670; low confidence. `[M+K]+`:
  FT04395_F07.S0677; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT03337", "FT03832", "FT04395", "FT03029"),
                  confidence_level = c("D", "D", "D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT03337_F07.S0658", "FT03337_F08.S0660", "FT03832_F08.S0670",
                 "FT04395_F07.S0677")]
ms2$confidence <- c("low")
```

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

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


### Methionine

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

```{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 mix07-serum-pos-methionine-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 mix07-serum-pos-methionine-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 FT01287 match with good similarity some of the HMDB
reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-methionine-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 mix07-serum-pos-methionine-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

- FT01287 (RT = 163.05, ion `[M+H]+`) with confidence **B**.
- FT01711 (RT = 162.76, ion `[M+Na]+`) with confidence **B-**.
- FT02147 (RT = 162.49, ion `[M+2Na-H]+`) with confidence **B-**.
- FT00941 (RT = 163.8, ion `[M+H-NH3]+`) with confidence **B-**.
- Reference MS2: `[M+H]+`: FT01287_F07.S0551; high confidence. `[M+2Na-H]+`:
  FT02147_F08.S0552; low confidence. `[M+H-NH3]+`: FT00941_F07.S0550,
  FT00941_F08.S0550; low confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT01287_F07.S0551", "FT02147_F08.S0552", "FT00941_F07.S0550",
                 "FT00941_F08.S0550")]
ms2$confidence <- c(rep("high", 1), rep("low", 3))
```

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


### 3-Methylhistidine

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

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

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

```{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 mix07-serum-pos-3-methylhistidine-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 mix07-serum-pos-3-methylhistidine-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 FT01635, FT01637, FT01636, FT01643 match with good
similarity some of the HMDB reference spectra for `r std`. Below we show the
mirror plots of the best matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-3-methylhistidine-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 mix07-serum-pos-3-methylhistidine-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

- FT01635 (RT = 187.17, ion `[M+H]+`) with confidence **A**.
- FT02121 (RT = 188.21, ion `[M+Na]+`) with confidence **A-**.
- FT02598 (RT = 185.97, ion `[M+2Na-H]+`) with confidence **A-**.
- FT01352 (RT = 188.5, ion `[M+H-NH3]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT01635_F07.S0634, FT01635_F07.S0689,
  FT01635_F08.S0644, FT01636_F07.S0813, FT01636_F08.S0791, FT01636_F08.S0817, 
  FT01636_F08.S0848; high confidence. `[M+H-NH3]+`: FT01352_F07.S0654; low
  confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT01635", "FT02121", "FT02598", "FT01352"),
                  confidence_level = c("A", "A-", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT01635_F07.S0634", "FT01635_F07.S0689", "FT01635_F08.S0644",
                 "FT01636_F07.S0813", "FT01636_F08.S0791", "FT01636_F08.S0817",
                 "FT01636_F08.S0848", "FT01352_F07.S0654")]
ms2$confidence <- c(rep("high", 7), "low")
```

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

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


### 1-Methyluric acid

```{r, echo = FALSE}
std <- "1-Methyluric 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 mix07-serum-pos-1-methyluric-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 mix07-serum-pos-1-methyluric-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 from feature FT01951 match with good similarity some of the HMDB
reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-1-methyluric-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 mix07-serum-pos-1-methyluric-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

- FT01951 (RT = 146.03, ion `[M+H]+`) with confidence **A**.
- FT02390 (RT = 146.1, ion `[M+Na]+`) with confidence **A-**.
- FT02714 (RT = 145.3, ion `[M+K]+`) with confidence **A-**.
- FT03353 (RT = 145.4, ion `[M+2K-H]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT01951_F07.S0457, FT01951_F08.S0463; high
  confidence. `[M+2K-H]+`: FT03353_F08.S0464; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT01951", "FT02390", "FT02714", "FT03353"),
                  confidence_level = c("A", "A-", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT01951_F07.S0457", "FT01951_F08.S0463", "FT03353_F08.S0464")]
ms2$confidence <- c(rep("high", 2), "low")
```

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

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


### Phenylpyruvic acid

```{r, echo = FALSE}
std <- "Phenylpyruvic 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 mix07-serum-pos-phenylpyruvic-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 but we don't find any match.

```{r mix07-serum-pos-phenylpyruvic-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 mix07-serum-pos-phenylpyruvic-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, 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 good matches (low similarity ones for spectra FT2089 and FT2087).

```{r mix07-serum-pos-phenylpyruvic-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 mix07-serum-pos-phenylpyruvic-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)
```

#### Summary

- Inconclusive; needs negative polarity or water data for confirmation.
- (Eventually) FT02298 (RT = 35.74, `[M+K]+` ion) with confidence **D**. But the
  ppm error is relatively large.


### Serine

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

```{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 mix07-serum-pos-serine-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 mix07-serum-pos-serine-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 FT00470, FT00471 match with good similarity some
of the HMDB reference spectra for `r std`. Below we show the mirror plots of
the best matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-serine-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 mix07-serum-pos-serine-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 mix07-serum-pos-serine-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.4, 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

- FT00471 (RT = 185.69, ion `[M+H]+`) with confidence **A**.
- FT00256 (RT = 185.69, ion `[M+H-H2O]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT00471_F07.S0631; high confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT00471_F07.S0631")]
ms2$confidence <- c("high")
```

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


### Sphingosine

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

```{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 mix07-serum-pos-sphingosine-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 mix07-serum-pos-sphingosine-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 many feature matching some of the HMDB reference spectra for `r std`.
Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix07-serum-pos-sphingosinede-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 mix07-serum-pos-sphingosine-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

- FT04495 (RT = 37.7, `[M+H]+` ion) with confidence level **B**.
- FT05253 (RT = 37.9, `[M+Na]+` ion) with confidence level **B-**.
- FT03899 (RT = 37.8, `[M+H-H2O]+` ion) with confidence level **B-**.
- (Eventually) FT03948 (RT = 31.82, `[M+H-NH3]+` ion) with confidence level
  **B-**.
- Reference MS2: `[M+H]+`: FT04495_F07.S0110, FT04495_F07.S0200,
  FT04495_F08.S0146, FT04495_F08.S0212; high confidence. `[M+Na]+`:
  FT05253_F08.S0166; low confidence. `[M+H-H2O]+`: FT03899_F07.S0168,
  FT03899_F08.S0144; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT04495", "FT05253", "FT03899"),
                  confidence_level = c("B", "B-", "B-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT04495_F07.S0110", "FT04495_F07.S0200", "FT04495_F08.S0146",
                 "FT04495_F08.S0212", "FT05253_F08.S0166", "FT03899_F07.S0168",
                 "FT03899_F08.S0144")]
ms2$confidence <- c(rep("high", 4), rep("low", 3))
```

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

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


### Taurine

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

```{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 mix07-serum-pos-taurine-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 mix07-serum-pos-taurine-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 feature FT00809 match with good similarity some of the
HMDB reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-pos-taurine-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 mix07-serum-pos-taurine-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

- FT00809 (RT = 172.46, ion `[M+H]+`) with confidence **A**.
- FT01246 (RT = 172.19, ion `[M+Na]+`) with confidence **A-**. Note that the MS2
  spectra matches L-Glutamate.
- FT01549 (RT = 172.5, ion `[M+K]+`) with confidence **A-**.
- FT01625 (RT = 171.9, ion `[M+2Na-H]+`) with confidence **A-**.
- FT00496 (RT = 172.33, ion `[M+H-H2O]+`) with confidence **A-**.
- Reference MS2: `[M+H]+`: FT00809_F07.S0591, FT00809_F08.S0586; high
  confidence. `[M+Na]+`: FT01246_F07.S0529; low confidence. `[M+H-H2O]+`:
  FT00496_F07.S0589, FT00496_F08.S0585; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT00809", "FT01246", "FT01549", "FT01625",
                                 "FT00496"),
                  confidence_level = c("A", "A-", "A-", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT00809_F07.S0591", "FT00809_F08.S0586", "FT01246_F07.S0592",
                 "FT00496_F07.S0589", "FT00496_F08.S0585")]
ms2$confidence <- c(rep("high", 2), rep("low", 3))
```

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

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


### Threonic Acid

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

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

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

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

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

```{r mix07-serum-pos-threonic-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 but we don't find any match.

```{r mix07-serum-pos-threonic-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 mix07-serum-pos-threonic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

We also perform neutral loss comparison but still no match is found.

```{r mix07-serum-neg-threonic-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 mix07-serum-neg-threonic-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)
```

#### Summary

- Inconclusive; needs validation with negative polarity or water data. Best
  candidate is the feature selected below (highest intensity).
- FT02577 (RT = 131.8, `[M+2K-H]+` ion) with confidence level **D**.
- Reference MS2: `[M+2K-H]+`: FT02577_F07.S0412, FT02577_F08.S0407,
  FT02577_F08.S0447; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT02577"),
                  confidence_level = c("D"))
```

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


# Serum, negative polarity

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

### ADMA

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

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

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

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

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

```{r mix07-serum-neg-adma-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 mix07-serum-neg-adma-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 mix07-serum-neg-adma-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

- Inconclusive; needs validation by positive polarity and water data.
- FT1002 (RT = 174.8, `[M-H]-` ion) with confidence level **D**.
- FT1463 (RT = 175.4, `[M+Cl]-` ion) with confidence level **D**.
- FT1575 (RT = 175, `[M+HCOO]-` ion) with confidence level **D**.
- Reference MS2: `[M-H]-`: FT1002_F15.S0543, FT1002_F16.S0541; low confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT1002", "FT1463", "FT1575"),
                  confidence_level = c("D", "D", "D"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT1002_F15.S0543", "FT1002_F16.S0541")]
ms2$confidence <- c("low")
```

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

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


### dAMP

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

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

Many features have been assigned to this standard based on m/z matching. We next
plot its EIC and visually inspect it (has very low intensity).

```{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 mix07-serum-neg-damp-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

```{r mix07-serum-neg-damp-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 FT2842, FT2843 match with good similarity some of the
HMDB reference spectra for `r std`. Below we show the mirror plots of the
best matches (similarity score  greater than 0.7).

```{r mix07-serum-neg-damp-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

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

```{r mix07-serum-neg-damp-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 mix07-serum-neg-damp-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

- FT2842 (RT = 207.54, ion `[M-H]-`) with confidence **A**.
- FT3453 (RT = 205.54, ion `[M+Cl]-`) with confidence **A-**.
- FT3624 (RT = 207.2, ion `[M+HCOO]-`) with confidence **A-**.
- Reference MS2: `[M-H]-`: FT2842_F15.S0680, FT2842_F15.S0717, FT2842_F15.S0752,
  FT2842_F16.S0675; high confidence.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2842", "FT3453", "FT3624"),
                  confidence_level = c("A", "A-", "A-"))

## Get MS2 spectra:
ms2 <- std_ms2[c("FT2842_F15.S0680", "FT2842_F15.S0717", "FT2842_F15.S0752",
                 "FT2842_F16.S0675")]
ms2$confidence <- c("high")
```

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

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


### Caffeine

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

```{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, "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 mix07-serum-neg-caffeine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Seems not detectable in negative polarity.


### CDP-choline

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

```{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 mix07-serum-neg-cdp-choline-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 mix07-serum-neg-cdp-choline-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 FT5324, FT5809 match some of the HMDB reference spectra
for `r std`. Below we show the mirror plots of the best matches (similarity
score  greater than 0.7).

```{r mix07-serum-neg-cdp-choline-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5,
                               ppm = csp@ppm, tolerance = csp@tolerance)
```

We find similar results for MassBank as shown below.

```{r mix07-serum-neg-cdp-choline-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

- FT5324 (RT = 217.94, ion `[M-H]-`) with confidence **C**.
- FT5724 (RT = 216.42, ion `[M+Cl]-`) with confidence **C-**.
- FT5809 (RT = 216.42, ion `[M+HCOO]-`) with confidence **C**.
- Reference MS2: `[M-H]-`: FT5324_F15.S0722, FT5324_F16.S0705; low
  confidence. `[M+HCOO]-`: FT5809_F15.S0725, FT5809_F16.S0708; low confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT5324_F15.S0722", "FT5324_F16.S0705", "FT5809_F15.S0725",
                 "FT5809_F16.S0708")]
ms2$confidence <- c("low")
```

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


### Creatinine

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

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

Three features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from 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 mix07-serum-neg-creatinine-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 but we don't find any match as shown below.

```{r mix07-serum-neg-creatinine-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 FT0101, FT0100, FT0099 match some of the HMDB reference
spectra for `r std`. Below we show the mirror plots of the best matches
(similarity score  greater than 0.7) which however involve only one peak.

```{r mix07-serum-neg-creatinine-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)
```

No match is found with the MassBank comparison as shown below.

```{r mix07-serum-neg-creatinine-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

- FT0101 (RT = 85.52, ion `[M-H]-`) with confidence **C**.
- Reference MS2: `[M-H]-`: FT0101_F15.S0248, FT0101_F16.S0271, FT0100_F15.S0301,
  FT0100_F16.S0358; low confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0101_F15.S0248", "FT0101_F16.S0271", "FT0100_F15.S0301",
                 "FT0100_F16.S0358")]
ms2$confidence <- c("low")
```

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


### Glutaric acid

```{r, echo = FALSE}
std <- "Glutaric 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 mix07-serum-neg-glutaric-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 mix07-serum-neg-glutaric-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 from features FT0237, FT0239 match with good similarity some of the
HMDB reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-neg-glutaric-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 repeat the same with MassBank and we obtain similar results as shown below.

```{r mix07-serum-neg-glutaric-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 mix07-serum-neg-glutaric-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

- FT0237 (RT = 37.42, ion `[M-H]-`) with confidence **B**.
- Reference MS2: `[M-H]-`: FT0237_F15.S0150, FT0237_F16.S0158, FT0237_F16.S0199;
  high confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0237_F15.S0150", "FT0237_F16.S0158", "FT0237_F16.S0199")]
ms2$confidence <- c("high")
```

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


### Glycero-phosphocholine

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

```{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 mix07-serum-neg-glycero-phosphocholine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive; needs validation with positive polarity and water data.
- FT2158 (RT = 189.4, `[M+Cl]-` ion) with confidence **D**.
- FT2318 (RT = 188.5, `[M+HCOO]-` ion) with confidence **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT2158", "FT2318"),
                  confidence_level = c("D", "D"))
```

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


### Methionine

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

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

Three features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from 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 mix07-serum-neg-methionine-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 mix07-serum-neg-methionine-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 repeat the same with MassBank and we obtain similar results as shown below.

```{r mix07-serum-neg-methionine-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 validation from positive polarity or water data.
- FT0403 (RT = 162, `[M-H]-` ion) with confidence **D**.
- FT0785 (RT = 162, `[M+Cl]-` ion) with confidence **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0785"),
                  confidence_level = c("D"))
```

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


### 3-Methylhistidine

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

```{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 mix07-serum-neg-3-methylhistidine-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 mix07-serum-neg-3-methylhistidine-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 FT0602, FT0603 match with good similarity some of the
HMDB reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix07-serum-neg-3-methylhistidine-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 the MassBank comparison as we show below.

```{r mix07-serum-neg-3-methylhistidine-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

- FT0602 (RT = 186.1, ion `[M-H]-`) with confidence **A**.
- FT1041 (RT = 184.61, ion `[M+Cl]-`) with confidence **A-**.
- FT1172 (RT = 185.4, ion `[M-HCOO]-`) with confidence **A-**.
- Reference MS2: `[M-H]-`: FT0602_F15.S0601, FT0602_F16.S0585; high confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0602_F15.S0601", "FT0602_F16.S0585")]
ms2$confidence <- c("high")
```

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


### 1-Methyluric acid

```{r, echo = FALSE}
std <- "1-Methyluric 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, "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 mix07-serum-neg-1-methyluric-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive; needs validation with positive polarity and water data.
- FT0732 (RT = 144.8, ion `[M-H]-`) with confidence **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0732"),
                  confidence_level = c("D"))
```

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


### Phenylpyruvic acid

```{r, echo = FALSE}
std <- "Phenylpyruvic 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 mix07-serum-neg-phenylpyruvic-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 but we don't obtain good matches.

```{r mix07-serum-neg-phenylpyruvic-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 mix07-serum-neg-phenylpyruvic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.05, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

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

```{r mix07-serum-neg-phenylpyruvic-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

- Inconclusive; needs validation with positive polarity and water data.
- FT0540 (RT = 36.83, `[M-H]-` ion) with confidence **D**.
- FT1107 (RT = 35.2, `[M+HCOO]-` ion) with confidence **D**.
- Reference MS2: `[M-H]-`: FT0540_F15.S0128, FT0540_F15.S0187, FT0540_F15.S0204,
  FT0540_F16.S0138; low confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0540_F15.S0128", "FT0540_F15.S0187", "FT0540_F15.S0204",
                 "FT0540_F16.S0138")]
ms2$confidence <- c("low")
```

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


### Serine

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

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

Three features have been assigned to this standard based on m/z matching. Based
on their retention times, these seem however to represent ions from 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 mix07-serum-neg-serine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive: needs validation from positive polarity or water data. EIC data
  looks nice and clear.
- FT0083 (RT = 178.6, `[M-H]-` ion) with confidence **D**.
- FT0330 (RT = 180.2, `[M+Cl]-` ion) with confidence **D**.

```{r, echo = FALSE}
## Define the feature (ion) and MS2 spectra to insert
fts <- data.frame(feature_id = c("FT0083", "FT0330"),
                  confidence_level = c("D", "D"))
```

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


### Sphingosine

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

```{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 mix07-serum-neg-sphingosine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- Inconclusive; needs validation with positive polarity or water data. EIC
  signal is low.
- FT3096 (RT = 37.4, `[M+HCOO]-` ion) with confidence level **D**.


### Taurine

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

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

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

```{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 mix07-serum-neg-taurine-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 but we don't find any match.

```{r mix07-serum-neg-taurine-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 mix07-serum-neg-taurine-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 mix07-serum-neg-taurine-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

- FT0175 (RT = 170.9, `[M-H]-` ion) with confidence **A**.
- Reference MS2: `[M-H]-`: FT0175_F16.S0515; high confidence.

```{r, echo = FALSE}
## Get MS2 spectra:
ms2 <- std_ms2[c("FT0175_F16.S0515")]
ms2$confidence <- c("high")
```

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


### Threonic Acid

```{r, echo = FALSE}
std <- "Threonic 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, "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 mix07-serum-neg-threonic-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 but we don't find any match.

```{r mix07-serum-neg-threonic-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 mix07-serum-neg-threonic-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

- Inconclusive; needs validation with positive polarity or serum data. The MS2
  spectra match Hypoxanthine, which is however not part of this mix (endogenous
  signal?). EIC data shows however clear concentration difference.
- FT0306 (RT = 133.1, `[M-H]-` ion) with confidence **D**.
- Reference MS2: `[M-H]-`: FT0306_F15.S0328, FT0306_F16.S0359; low confidence.


## 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()
```