match-standards-serum-mix11.Rmd

---
title: "Identifying mix11 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 <- 11
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 water or added to human serum sample
pools in two different concentration and these samples were measured with the
LC-MS setup from Eurac (used also to generate the CHRIS untargeted metabolomics
data). Assignment of retention time and observed ion can be performed for each
standard based on MS1 information such as expected m/z but also based on the
expected difference in signal between samples with low and high concentration of
the standards. Finally, experimental fragment spectra provide the third level of
evidence. Thus, the present data set allows to annotate features to standards
based on 3 levels of evidence: expected m/z, difference in measured intensity
and MS2-based annotation.

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


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

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

# Data import

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

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

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

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

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

# Initial evaluation of available MS2 data

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

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

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

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

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

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

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

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

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

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

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

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


# Serum, positive polarity

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

### ADP

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

```{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

- Inconclusive. No MS2 spectra available. EICs don't look good.

### L-Carnitine

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

```{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 available 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 mix11-serum-pos-l-carnitine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

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

```{r mix11-serum-pos-l-carnitine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim_hmdb <- 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 mix11-serum-pos-l-carnitine-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_hmdb)
```

Similar results are obtained with MassBank comparison as shown below.

```{r mix11-serum-pos-l-carnitine-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("FT02124", "FT01642", "FT05411", "FT02424"),
                  confidence_level = c("A-", "A", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT01642_F07.S0308", "FT01642_F07.S0337",
                       "FT01642_F07.S0368", "FT02424_F07.S0463",
                       "FT02424_F08.S0500"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high", "high", "high", "low", "low")
```

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

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

### dCTP

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

No features found.

### L,L-Cyclo(leucylprolyl)

```{r, echo = FALSE}
std <- "L,L-Cyclo(leucylprolyl)"
```

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

```{r, echo = FALSE}
plot_eics(data, std, feature_table, "SP", 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 mix11-serum-pos-l,l-cyclo(leucylprolyl)-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 but we don't find any match.

```{r mix11-serum-pos-l,l-cyclo(leucylprolyl)-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_hmdb <- 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 mix11-serum-pos-l-cyclo(leucylprolyl)-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_hmdb)
```

```{r mix11-serum-pos-l,l-cyclo(leucylprolyl)-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("FT02316", "FT07607", "FT02699"),
                  confidence_level = c("A-", "A-", "A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT02699_F07.S0129", "FT02699_F08.S0130"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

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

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


### Dihydroorotic acid

```{r, echo = FALSE}
std <- "Dihydroorotic 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 mix11-serum-pos-dihydroorotic-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 mix11-serum-pos-dihydroorotic-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_hmdb <- 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 mix11-serum-pos-dihydroorotic-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_hmdb)
```

```{r mix11-serum-pos-dihydroorotic-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("FT01575", "FT03153"),
                  confidence_level = c("A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT01575_F07.S0403", "FT01575_F07.S0440",
                       "FT01575_F08.S0410", "FT01575_F08.S0454",
                       "FT03153_F07.S0422", "FT03153_F08.S0420"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high", "low", "low", "low", "low", "low")
```

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

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


### Glutathione Reduced

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

```{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 mix11-serum-pos-glutathione-reduced-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 good matches.

```{r mix11-serum-pos-glutathione-reduced-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-pos-glutathione-reduced-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

- We assign **A** because the compound matches.

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT10235", "FT10939", "FT04837",
                                 "FT10468", "FT10750"),
                  confidence_level = c("A-", "A-", "A-", "A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT10235_F07.S0808", "FT10235_F08.S0807",
                       "FT10939_F07.S0812", "FT10939_F08.S0809",
                       "FT10468_F07.S0809", "FT10468_F07.S0810",
                       "FT10468_F08.S0801", "FT10750_F07.S0811",
                       "FT10750_F08.S0808"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", "low", "low", "high", "high", "high",
                    "low", "low")
```

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

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


### Alpha-ketoisovaleric acid

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

```{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, "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 mix11-serum-pos-alpha-ketoisovaleric-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 good matches.

```{r mix11-serum-pos-alpha-ketoisovaleric-acid-reduced-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-pos-alpha-ketoisovaleric-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

- EICs are ambiguous, thus we don't add anything.


### Lysine

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

```{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 mix11-serum-pos-lysine-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 mix11-serum-pos-lysine-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_hmdb <- 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 mix11-serum-pos-lysine-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_hmdb)
```

We obtain similar results with the MassBank comparison.

```{r mix11-serum-pos-lysine-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("FT00930", "FT01309", "FT00902", "FT04422",
                                 "FT01820", "FT00143"),
                  confidence_level = c("A", "A", "A-", "A-", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT00930_F07.S0709", "FT00930_F08.S0719",
                       "FT01309_F07.S0733", "FT01309_F08.S0735"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

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

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


### Malic Acid

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

No features assigned.


### 3-Methylxanthine

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

```{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 mix11-serum-pos-3-methylxanthine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

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

```{r mix11-serum-pos-3-methylxanthine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim_hmdb <- 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 mix11-serum-pos-3-methylxanthine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance, sim_hmdb)
```

We obtain similar results with the MassBank comparison.

```{r mix11-serum-pos-3-methylxanthine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT01778"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT01778_F07.S0322", "FT01778_F07.S0349",
                       "FT01778_F08.S0350"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

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

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


### 2-Phosphoglyceric Acid

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


### SDMA

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

```{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 mix11-serum-pos-sdma-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 mix11-serum-pos-sdma-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_hmdb <- 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 mix11-serum-pos-sdma-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_hmdb)
```

```{r mix11-serum-pos-sdma-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("FT03399", "FT02980", "FT03276", "FT07282",
                                 "FT02503", "FT00438"),
                  confidence_level = c("A-", "A-", "A-", "A-", "A", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT02980_F08.S0674", "FT02980_F07.S0653",
                       "FT02503_F07.S0650", "FT02503_F07.S0712",
                       "FT02503_F08.S0655", "FT02503_F08.S0721",
                       "FT00438_F08.S0648", "FT00438_F07.S0643"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", "high", "high", "high", "high", "low", "low")
```

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

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


### Tetrahydrobiopterin

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

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

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

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

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

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

```{r mix11-serum-pos-tetrahydrobiopterin-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 mix11-serum-pos-tetrahydrobiopterin-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-pos-tetrahydrobiopterin-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                           tolerance = csp@tolerance, sim_hmdb)
```

```{r mix11-serum-pos-tetrahydrobiopterin-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

Given the ppm error for the EIC is pretty large and the MS2 match is difficult
(because of a sodium ion) we don't add any ion for this compound.


### p-Tyramine

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

```{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 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 mix11-serum-pos-p-tyramine-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 mix11-serum-pos-p-tyramine-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_hmdb <- 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 mix11-serum-pos-p-tyramine-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_hmdb)
```

We have many very good matches. We obtain similar results with the MassBank
comparison as we show below.

```{r mix11-serum-pos-p-tyramine-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("FT00752", "FT01088"),
                  confidence_level = c("A-", "A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT00752_F07.S0481", "FT00752_F08.S0492",
                       "FT01088_F07.S0484", "FT01088_F08.S0495"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low", "low", "high", "high")
```

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

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



### UDP-Glucose

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

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT07270"),
                  confidence_level = c("D"))
```

```{r, add-ions, 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 with matching features

### ADP

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

```{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 mix11-serum-neg-adp-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 mix11-serum-neg-adp-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_hmdb <- 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 mix11-serum-neg-adp-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_hmdb)
```

```{r mix11-serum-neg-adp-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("FT4786"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT4786_F15.S0840", "FT4786_F16.S0830",
                       "FT4786_F16.S0859"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

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

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


### L-Carnitine

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

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

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

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

```{r mix11-serum-neg-l-carnitine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

```{r mix11-serum-neg-l-carnitine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-neg-l-carnitine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

In 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("FT1083"),
                  confidence_level = c("D"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT1083_F16.S0426"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

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

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

### dCTP

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

No features assigned.

### Dihydroorotic acid

```{r, echo = FALSE}
std <- "Dihydroorotic 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 mix11-serum-neg-dihydroorotic-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 as shown below.

```{r mix11-serum-neg-dihydroorotic-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.1).

```{r mix11-serum-neg-dihydroorotic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1, ppm = csp@ppm,
                           tolerance = csp@tolerance, sim_hmdb)
```

```{r mix11-serum-neg-dihydroorotic-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("FT0481", "FT2732"),
                  confidence_level = c("C", "C-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0481_F15.S0337", "FT0481_F15.S0367",
                       "FT0481_F16.S0338", "FT0481_F16.S0379",
                       "FT2732_F15.S0348", "FT2732_F16.S0360"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

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

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


### Glutathione Reduced

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

```{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 mix11-serum-neg-glutathione-reduced-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 mix11-serum-neg-glutathione-reduced-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.3).

```{r mix11-serum-neg-glutathione-reduced-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.3, ppm = csp@ppm,
                           tolerance = csp@tolerance, sim_hmdb)
```

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

```{r mix11-serum-neg-glutathione-reduced-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("FT7329"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT7329_F15.S0725", "FT7329_F15.S0753",
                       "FT7329_F16.S0732", "FT7329_F16.S0761"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high")
```

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

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


### Alpha-ketoisovaleric acid

```{r, echo = FALSE}
std <- "Alpha-ketoisovaleric 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 mix11-serum-neg-alpha-ketoisovaleric-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 mix11-serum-neg-alpha-ketoisovaleric-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-neg-alpha-ketoisovaleric-acid-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0765", "FT0135"),
                  confidence_level = c("C-", "C"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0135_F15.S0128", "FT0135_F16.S0132"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("low")
```

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

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


### Lysine

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

```{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("FT2286", "FT0377", "FT0881", "FT1173"),
                  confidence_level = c("D"))
```

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


### Malic Acid

```{r, echo = FALSE}
std <- "Malic 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 mix11-serum-neg-malic-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 mix11-serum-neg-malic-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix11-serum-neg-malic-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_hmdb)
```

```{r mix11-serum-neg-malic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

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

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0276"),
                  confidence_level = c("A"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT0276_F15.S0298", "FT0276_F16.S0292"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high", "low")
```

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

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


### 3-Methylxanthine

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

No matching features.

### 2-Phosphoglyceric Acid


```{r, echo = FALSE}
std <- "2-Phosphoglyceric 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)
```

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT0789", "FT3789"),
                  confidence_level = c("D"))
```

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


### SDMA

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

```{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 mix11-serum-neg-sdma-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 mix11-serum-neg-sdma-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_hmdb <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Below we show the mirror plots of the best matches (similarity score greater
than 0.3).

```{r mix11-serum-neg-tetrahydrobiopterin-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.3, ppm = csp@ppm,
                           tolerance = csp@tolerance, sim_hmdb)
```

```{r mix11-serum-neg-sdma-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("FT1020", "FT4393", "FT1607", "FT1898",
                                 "FT1484"),
                  confidence_level = c("A", "A-", "A-", "A-", "A-"))
## Get MS2 spectra:
ms2 <- std_ms2[match(c("FT1020_F16.S0592", "FT1898_F15.S0578"),
                     spectraNames(std_ms2))]
ms2$confidence <- c("high", "low")
```

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

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


### Tetrahydrobiopterin

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

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

#### Summary

```{r, echo = FALSE}
fts <- data.frame(feature_id = c("FT2588"),
                  confidence_level = c("D"))
```

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

### p-Tyramine

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

```{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

- EIC has very low intensity.


### UDP-Glucose

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

No features.

## Standards without any matching feature

### L,L-Cyclo(leucylprolyl)

### p-Tyramine

# 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()
```