match-standards-serum-mix05.Rmd

---
title: "Identifying mix05 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 <- 5
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: 0.10.1, 2024-10-29.**

# 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, echo = FALSE, results = "asis"}
std_dilution <- read.table("data/standards_dilution.txt",
                           sep = "\t", header = TRUE)
std_dilution$exactmass <- calculateMass(std_dilution$formula)
colnames(std_dilution)[colnames(std_dilution) == "HMDB.code"] <- "HMDB"
std_dilution <- std_dilution[std_dilution$mix == MIX, ]
std_dilution$RT[std_dilution$name == "5-MTA"] <- 62
pandoc.table(std_dilution[, c("name", "formula", "HMDB", "RT", "POS", "NEG")], 
             style = "rmarkdown", split.tables = Inf,
             caption = paste0("Standards of ", MIX_NAME, ". Columns RT, POS ",
                              "and NEG contain expected retention times and ",
                              "adducts from a previous manual analysis."))

```

# 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 evaluation

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}
```

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

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

#### Summary

- No signal.


### dCDP

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

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

#### Summary

- No signal.


### CoA

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

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

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

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

#### Summary

- No clear signal. Two potential candidate peaks, but difference between low and
  high is not very large. Needs validation/cross-verification with negative
  polarity data.


### 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, "SP", std_ms2)
```

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

```{r mix05-serum-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-serum-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)
```

```{r mix05-serum-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, sim)
```

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

```{r mix05-serum-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)
```

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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT06036", "FT05544", "FT10922", "FT04609",
                                 "FT05090", "FT06460"),
                  confidence_level = c("A-", "A", "A-", "A-", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT05544_F07.S0132", "FT05544_F08.S0131",
                       "FT04609_F07.S0129", "FT04609_F08.S0128",
                       "FT05090_F07.S0130", "FT05090_F08.S0129"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high", "high", "low", "low", "low", "low")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### 1,3-Dimethyluric acid

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

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

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

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches similarity score greater than
0.7).

```{r mix05-serum-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, sim)
```

```{r mix05-serum-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)
```

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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT02204", "FT03474", "FT02852"),
                  confidence_level = c("B", "B-", "B-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT03475_F07.S0362", "FT03475_F08.S0324",
                       "FT02204_F07.S0317", "FT02204_F07.S0357",
                       "FT02204_F07.S0390", "FT02204_F07.S0429",
                       "FT02204_F08.S0323", "FT02204_F08.S0369",
                       "FT02204_F08.S0411", "FT03474_F07.S0362",
                       "FT03474_F07.S0395", "FT03474_F07.S0431",
                       "FT03474_F08.S0392"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", rep("high", 7), rep("low", 4))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### Glycerophospho-inositol

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

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

#### Summary

- No signal.

### GTP

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

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

#### Summary

- No signal.


### 2-Ketobutyric acid

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

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

#### Summary

- No signal.


### Mannose

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

```{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, "SP", std_ms2)
```

```{r mix05-serum-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-serum-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)
```

```{r mix05-serum-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)
```

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 show clear difference between low and high concentration.

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT02219", "FT02308"),
                  confidence_level = c("D", "D"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT02308_F08.S0541"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### 5-MTA

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

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

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

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches.

```{r mix05-serum-pos-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, sim)
```

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

```{r mix05-serum-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT04085", "FT04876"),
                  confidence_level = c("A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT04085_F07.S0141", "FT04085_F07.S0217",
                       "FT04085_F07.S0255", "FT04085_F08.S0145",
                       "FT04085_F08.S0218", "FT04085_F08.S0264",
                       "FT04876_F07.S0251", "FT04876_F08.S0261"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### NADP

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

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT06104"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### Nicotinic acid

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

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

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

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

#### Summary

- No signal.


### 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 feature and visually inspect these.

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.4).

```{r mix05-serum-pos-orotic-acid-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.4, ppm = csp@ppm,
                               tolerance = csp@tolerance, sim)
```

Matches are not too bad. We obtain similar results with the MassBank comparison
as we show below.

```{r mix05-serum-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT01512"),
                  confidence_level = c("C"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT01512_F07.S0716", "FT01512_F08.S0697"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### PABA

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

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT01117", "FT00766"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### Threonine

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

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

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

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-pos-threonine-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, sim)
```

```{r mix05-serum-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)
```

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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT01198", "FT00152", "FT00768",
                                 "FT00465", "FT00272"),
                  confidence_level = c("B-", "B-", "B", "B-", "B-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT00152_F07.S0646", "FT00152_F08.S0631",
                       "FT00768_F07.S0632", "FT00768_F08.S0619",
                       "FT00465_F07.S0648", "FT00272_F07.S0647",
                       "FT00272_F07.S0712", "FT00272_F08.S0633"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", "high", "high", rep("low", 4))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


# 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
csp@tolerance <- 0.01
```

```{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 evaluation

### dADP

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

```{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, "SN", std_ms2)
```

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-neg-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, sim)
```

```{r mix05-serum-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)
```

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

- The EIC signal is very low, but the MS2 spectrum clearly matches (well) to the
  reference. We thus add the feature and MS2 spectrum to the database.

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT4261"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT4261_F16.S0806"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### dCDP

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT3849"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### CoA

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

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

#### Summary

No signal.

### Corticosterone

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

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT3957"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### 1,3-Dimethyluric acid

```{r, echo = FALSE}
std <- "1,3-Dimethyluric 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, "SN", std_ms2)
```

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-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, sim)
```

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

```{r mix05-serum-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)
```

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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT3952", "FT0917"),
                  confidence_level = c("B-", "B"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0918_F15.S0291", "FT0918_F15.S0316",
                       "FT0918_F16.S0290", "FT0918_F16.S0318"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### Glycerophospho-inositol

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

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

#### Summary

No signal.


### GTP

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

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

#### Summary

No signal.


### 2-Ketobutyric acid

```{r, echo = FALSE}
std <- "2-Ketobutyric 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, "SN", 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-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-neg-2-ketobutyric-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, sim)
```

Match is however based on a single peak.  We obtain similar results with the
MassBank comparison as we show below.

```{r mix05-serum-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0610", "FT0083"),
                  confidence_level = c("B-", "B"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0083_F15.S0131", "FT0083_F16.S0126"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### 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, "SN", std_ms2)
```

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT1190"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### 5-MTA

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

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

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

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-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, sim)
```

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

```{r mix05-serum-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

Chromatographic data looks pretty ugly with very broad signal/peak. We thus add
several features along RT.

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT2908", "FT3092", "FT2228", "FT3094",
                                 "FT2907", "FT3093", "FT2229"),
                  confidence_level = c("A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT2911_F15.S0254", "FT2911_F16.S0246",
                       "FT2907_F15.S0254", "FT3095_F15.S0302"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", "low", "high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### NADP

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

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

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

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

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

```{r mix05-serum-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-serum-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7) and we observe that only one peak matches.

```{r mix05-serum-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, sim)
```

```{r mix05-serum-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT8023"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT8025_F15.S0852", "FT8025_F15.S0871"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### Nicotinic acid

```{r, echo = FALSE}
std <- "Nicotinic 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, "SN", std_ms2)
```

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

```{r mix05-serum-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-serum-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-serum-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, sim)
```

```{r mix05-serum-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0185", "FT0182"),
                  confidence_level = c("B", "B-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0185_F15.S0142"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### 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, "SN", std_ms2)
```

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

```{r mix05-serum-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-serum-neg-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)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.7).

```{r mix05-serum-neg-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, sim)
```

```{r mix05-serum-neg-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

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0459", "FT2524"),
                  confidence_level = c("A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0459_F15.S0610", "FT0459_F16.S0652"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

```{r, add-ions, echo = FALSE, results = "asis"}
```

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


### 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, "SN", std_ms2)
```

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0323", "FT1874"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


### Threonine

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0169", "FT1446"),
                  confidence_level = c("D"))
```

```{r, add-ions, echo = FALSE, results = "asis"}
```


# Summary on the ion database

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

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

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

# Session information

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

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