match-standards-serum-mix12.Rmd

---
title: "Identifying mix12 standards in serum"
author: "Marilyn De Graeve, 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 <- 12
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, 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}
```
 
Positive polarity:

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"}
```

Negative polarity:

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. Lastly, from mixture 12 onward the maximum retention time deviation 
from the expected 'Target_RT' is less than 10 seconds.

```{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 (all, incl not found/matched)

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.

### Argininosuccinic acid

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

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

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

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

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

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 mix12-serum-pos-Argininosuccinic-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. The results are shown in the heatmaps below.

Heat maps:

```{r mix12-serum-pos-Argininosuccinic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix12-serum-pos-Argininosuccinic-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)
```

For spectra from untargeted features that match with good similarity to some of 
the HMDB reference spectra for `r std`, we show the mirror plots of the best 
matches.

Mirror plots (check D -> C or B?):

```{r mix12-serum-pos-Argininosuccinic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix12-serum-pos-Argininosuccinic-acid-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

Highest confidence matches (check B -> A?):

```{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

- No spectral match.
- **D** for:
  - FT04313 (RT = 199.4, 3.6 ppm, `[M+H]+` ion) with confidence **D**
  - FT01398 (RT = 199.5, 5.2 ppm, `[M+2H]2+` ion) with confidence **D**
  - FT03838 (RT = 201.1, 1.1 ppm, `[M+H-H2O]+` ion) with confidence **D** 	
- <**D**, high ppm:
  - FT05495 (RT = 200.1, 27.3 ppm, `[M+K]+` ion) with confidence **D** 
- <**D**, high rt dev and high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| FT03869	| 274.1	| 10.82 | 28.86 | 199 | FG.001.001 | [M+H-NH3]+ | 0 | 10.18 | 9.373
| FT04312 | 291.1 | 24.81 | 35.42 | 199 | FG.008.001 | [M+H]+ | 0 | 11.41 | 10.61
| FT05097 | 313.1 | 24.68 | 137.6 | 199 | FG.002.001 | [M+Na]+ | 2 | 16.43 | 12.76
| FT03837 | 273.1 | 2.839 | 187.2 | 199 | FG.007.001 | [M+H-H2O]+ | 2 | 21.21 | 8.757
| FT04314 | 291.1 | 2.339 | 221.7 | 199 | FG.004.001 | [M+H]+ | 2 | 16.03 | 7.853


### Carnosine

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

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

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

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

The extracted MS2 spectra 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 mix12-serum-pos-Carnosine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

For `r std`, there is no reference spectrum available in HMDB.

```{r mix12-serum-pos-Carnosine-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 mix12-serum-pos-Carnosine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- **D** for:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| FT03017 | 227.1 | 2.034 | 184 | 184 | FG.007.001 | [M+H]+ | 2 | 21.86 | 11.38 |
| FT03374 | 249.1 | 0.5137 | 184.8 | 184 | FG.002.001 | [M+Na]+ | 2 | 16.83 | 8.131 |
| FT03641 | 265.1 | 1.122 | 184.8 | 184 | FG.002.001 | [M+K]+ | 1 | 14.52 | 8.073 |
| FT02698 | 209.1 | 1.027 | 185.1 | 184 | FG.001.002 | [M+H-H2O]+ | 0 | 13.98 | NA |
| FT02714 | 210.1 | 0.07326 | 185.1 | 184 | FG.001.001 | [M+H-NH3]+ | 1 | 16.93 | 7.889 |
| FT00630 | 114.1 | 5.492 | 185.2 | 184 | FG.001.001 | [M+2H]2+ | 0 | 20.86 | 7.094 |

- <**D** for, high ppm and/or rt:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| FT00632 | 114.1 | 21.6 | 34.61 | 184 | FG.003.001 | [M+2H]2+ | 0 | 12.07 | 6.834 |
| FT03799 | 271.1 | 0.02287 | 34.78 | 184 | FG.003.001 | [M+2Na-H]+ | 0 | 13.52 | 12.81 |
| FT01111 | 136 | 1.429 | 169.7 | 184 | FG.012.001 | [M+2Na]2+ | 2 | 17.85 | 13.29 |
| FT01112 | 136 | 5.417 | 196.1 | 184 | FG.004.001 | [M+2Na]2+ | 2 | 16.93 | 13 |



### dCMP

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

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

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

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

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

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

```{r mix12-serum-pos-dCMP-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 mix12-serum-pos-dCMP-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 mix12-serum-pos-dCMP-mirror-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)
```

For Massbank:

```{r mix12-serum-pos-dCMP-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

- **D** for:
  - FT04845 (RT	= 203.4, 0.6548 ppm, `[M+H]+` ion) with confidence **D**

- < **D** for, high ppm:

|   &nbsp;    | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct   | n_ms2 | mean_high | mean_low |
|:-----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:----------:|:-----:|:---------:|:--------:|
| FT04304 | 291 | 20.34 | 196.4 | 203 | FG.003.001 | [M+H-NH3]+ | 1 | 15.47 | 8.784 |
| FT05943 | 346 | 25.49 | 203.4 | 203 | FG.001.001 | [M+K]+ | 2 | 17.58 | 9.247 |

- < **D** for, high ppm and/or rt dev:

|   &nbsp;    | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct   | n_ms2 | mean_high | mean_low |
|:-----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:----------:|:-----:|:---------:|:--------:|
| FT06790 | 384 | 20.19 | 167.4 | 203 | FG.006.001 | [M+2K-H]+ | 0 | 14.35 | 9.067 |
| FT06792 | 384 | 0.4597 | 180.1 | 203 | FG.007.001 | [M+2K-H]+ | 0 | 11.59 | 2.129 |

- from previos run:
  - FT10671 (RT = , 4.363 ppm, [2M+H]+ ion) with confidence A


### Folic acid

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

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

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

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

```{r mix12-serum-pos-Folic-acid-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 mix12-serum-pos-Folic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Spectra from features FT07851 match with good similarity some
of the HMDB reference spectra for `r std`. Below we show the mirror plots of
the best matches (similarity score greater than 0.1).

```{r mix12-serum-pos-Folic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

Matches are low proof, as the MSMS spectra FT07851 only contains 1 fragment and
the parent ion but correctly overlap with `[M+H]+` of folic acid. D -> C

```{r mix12-serum-pos-Folic-acid-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix12-serum-pos-Folic-acid-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.8,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

Matches are low proof, as the MSMS spectra FT07851 only contains 1 fragment and
the parent ion but correctly overlap with `[M+H]+` of folic acid. Because match 
larger than 0.7 in MassBank, we assign B instead of C.

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)
```

The MSMS spectra FT07851 not exclusively links to folic acid, other matching 
spectra are found too, due to the scarce spectrum (contains only 1 fragment and
the parent ion). Therefore, no A confidence level can be assigned to this 
adduct.

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

The MSMS spectra FT07851 not exclusively links to folic acid, other matching 
spectra are found too, due to the scarce spectrum (contains only 1 fragment and
the parent ion). Therefore, no A confidence level can be assigned to this 
adduct.

#### Summary

- FT07851 (RT = 442.1, 0.8194 ppm, `[M+H]+` ion) with confidence **B**
- FT08230 (RT = 464.1, 0.2732 ppm, `[M+Na]+` ion) with confidence **D**
- FT08636 (RT = 486.1, 1.367 ppm,	`[M+2Na-H]+` ion) with confidence **D**
- Reference MS2: FT07851 `[M+H]+`: FT07851_F07.S0548 Folic acid (low confidence)


### N-Formyl-L-methionine

```{r, echo = FALSE}
std <- "N-Formyl-L-methionine"
```

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

1 feature (**FT01774**) with low rt deviation can potentially be assigned to 
this standard. Based on its high ppm 10, probably another compound or noise. 
EIC plot shows NOISE.

**FT00283** EIC looks ok but very high rt dev to include.

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

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

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

```{r mix12-serum-pos-N-Formyl-L-methionine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```
No MS2 spectra.

#### Summary

- No MS2 spectra available.
- No MS2 reference spectra available in HMDB and MassBank.
- EIC shows low intensity signal (noise instead of peak).


### Galactitol

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

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

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

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

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

```{r mix12-serum-pos-Galactitol-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- **D** for:
  - FT02599 (RT = 167.1, 1.052 ppm, `[M+Na]+` ion) with confidence **D**
  - FT03005 (RT = 167, 0.7415 ppm, `[M+2Na-H]+` ion) with confidence **D** 	
- < **D** for, high ppm:
  - FT02204 (RT = 167.3, 10.55 ppm, `[M+H]+` ion)
  - FT1183 (RT = 165.8, 22.41 ppm, `[M+Cl]+` ion)
- FT03006 dropped, to high rt dev. 


### Glucose 6-Phosphate

```{r, echo = FALSE}
std <- "Glucose 6-Phosphate"
```

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

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

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

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

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

```{r mix12-serum-pos-Glucose-6-Phosphate-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 mix12-serum-pos-Glucose-6-Phosphate-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 mix12-serum-pos-Glucose-6-Phosphate-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix12-serum-pos-Glucose-6-Phosphate-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.8,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

Match very good, MSMS spectra FT04100 correctly overlaps with 
D-Glucose 6-phophate sodium salt. Because match 
larger than 0.7 in MassBank, we assign B instead of C.

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)
```

The MSMS spectra FT04100 exclusively links to `r std`. Therefore, A confidence 
level can be assigned to this adduct.

#### Summary

- FT04100 (RT = 236.4, 4.984 ppm,	`[M+Na]+` ion) with confidence **A**
- FT03265 (RT = 236.5, 3.858 ppm,	`[M+H-H2O]+` ion) with confidence **D**
- FT03567 (RT = 236.7, 2.993 ppm,	`[M+H]+` ion) with confidence **D**
- FT04494 (RT = 237, 7.477 ppm,	`[M+K]+` ion) with confidence **D**
- from previous run:
  - FT02946 (RT = , 2.632 ppm,	`[M+H-H4O2]+` ion) with confidence **D**
  - FT00379 (RT = , 5.342 ppm,	`[M+H-Hexose-H2O]+` ion) with confidence **D**
  - FT09233 (RT = , 5.632 ppm,	`[2M+H]+` ion) with confidence **D**
- Reference MS2: FT04100 `[M+Na]+`: FT04100_F07.S0851 (high confidence)


### dGMP

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

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

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

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

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

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

```{r mix12-serum-pos-dGMP-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 mix12-serum-pos-dGMP-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 mix12-serum-pos-dGMP-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)
```

Mirror plots:

```{r mix12-serum-pos-dGMP-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.6,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```



```{r mix12-serum-pos-dGMP-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.8,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

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

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


#### Summary

- FT06003 (RT = 348.1, 2.136 ppm, `[M+H]+` ion) with confidence **B**
  side note, other hits >0.7 are eg guanine part of dGMP, so combining rt/mz/ms2 
  actually sure correct.
- FT11828 (RT = 695.1, 6.073 ppm,	`[2M+H]+` ion) with confidence **D**
- FT06844 (RT = 386, 6.883 ppm,	`[M+K]+` ion) with confidence **D**
- FT06518 (RT = 370.1, 3.19 ppm, `[M+Na]+` ion) with confidence **D** 	
- Reference MS2: FT06003 `[M+H]+`: FT06003_F07.S0808 (high confidence), 
  FT06003_F08.S0798 (high confidence)


### Guanidoacetic acid

```{r, echo = FALSE}
std <- "Guanidoacetic 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, "WP", std_ms2)
```

The 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 mix12-serum-pos-Guanidoacetic-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 mix12-serum-pos-Guanidoacetic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix12-serum-pos-Guanidoacetic-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)
```

Mirror plots:

```{r mix12-serum-pos-Guanidoacetic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix12-serum-pos-Guanidoacetic-acid-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

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

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

#### Summary

- FT00757 (RT = 118.1, 1.14 ppm, `[M+H]+` ion) with confidence **B** 	
- FT01801 (RT = 162, 1.416 ppm, `[M+2Na-H]+` ion) with confidence **D** 	
- FT01208 (RT = 140, 0.001894 ppm, `[M+Na]+` ion) with confidence **D** 	
- FT00114 (RT = 72.06, 17.51 ppm, `[M+H-CH2O2]+` ion) with confidence **D** 	
- FT00412 (RT = 101, 2.217 ppm, `[M+H-NH3]+` ion) with confidence **D** 	
- Reference MS2: FT00757 `[M+H]+`: FT00757_F07.S0597 (high confidence)
  

### Homocysteine

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

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

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

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

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

```{r mix12-serum-pos-Homocysteine-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 mix12-serum-pos-Homocysteine-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 mix12-serum-pos-Homocysteine-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)
```

Mirror plots:

```{r mix12-serum-pos-Homocysteine-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

```{r mix12-serum-pos-Homocysteine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

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

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

#### Summary

- FT01111 (RT = 136, 0.25 ppm, `[M+H]+` ion) with confidence **B**
  side note: actually A, all matches >0.7 Homocysteine except 1 to Homocysteine
  thiolactone but mz/rt/ms2 matches so correct.
- FT00754 (RT = 118, 1.332 ppm, `[M+H-H2O]+` ion) is other biological compound!
- FT02126 (RT = 180, 2.232 ppm, `[M+2Na-H]+` ion) with confidence **D**
- FT00300 (RT = 90.04, 8.817 ppm, `[M+H-CH2O2]+` ion) with confidence **D**
- Reference MS2: FT01111 `[M+H]+`: F07.S0582, F08.S0559


### Ketoleucine

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

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

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

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

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

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

```{r mix12-serum-pos-Ketoleucine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```
The EIC of this peak are actually 3 distinct peaks, so won't evaluate further.
Also, not present in the DBs.

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- Bad EIC for the one with bad MS2 spectra (FT01583), exclude.
- FT00606 (RT = 113.1, 1.4 ppm, `[M+H-H2O]+` ion) with confidence **D** 
- FT00996 (RT = 131.1, 9.548 ppm, `[M+H]+` ion) with confidence **D**  	
- FT02024 (RT = 175, 1.067 ppm, `[M+2Na-H]+` ion) with confidence **D** 


### Methotrexate

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

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

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

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

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

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

```{r mix12-serum-pos-Methotrexate-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 mix12-serum-pos-Methotrexate-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Spectra from features FT00678, FT00680, FT00688, FT01001 match with good
similarity some of the HMDB reference spectra for `r std`. Below we show the
mirror plots of the best matches (similarity score  greater than 0.8).

```{r mix12-serum-pos-Methotrexate-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

With the MassBank comparison we don't get any match.

```{r mix12-serum-pos-Methotrexate-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

```{r mix12-serum-pos-Methotrexate-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```


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

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

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

#### Summary

- FYI: Methotrexate and Amethopterin are synonyms
- FT08081 (RT = 455.2, 1.585 ppm, `[M+H]+` ion) with confidence **A**
- FT08863 (RT = 499.1, 1.695 ppm, `[M+2Na-H]+` ion) with confidence **D**
- FT08464 (RT = 477.2, 0.931 ppm, `[M+Na]+` ion) with confidence **D**
- FT03045 (RT = 228.1, 0.1243 ppm, `[M+2H]2+` ion) with confidence **D**
- FT09407 (RT = 531.1, 27.14 ppm, `[M+2K-H]+` ion) with confidence **D**
- Reference MS2: FT08081 `[M+H]+`: F08.S0507, F07.S0524 (high confidence)


### Sebacic acid

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

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

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

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

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

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

```{r mix12-serum-pos-Sebacic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No spectra match.
- FT00606: 2 peaks in EIC so best ignore, also high ppm 10
- FT02954 (RT = 225.1, 0.5944 ppm, `[M+Na]+` ion) with confidence **D** 	


### Thiamine

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

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

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

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

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

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

```{r mix12-serum-pos-Thiamine-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 mix12-serum-pos-Thiamine-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)
```
FYI: no heat map with only 1 row.

Spectra from feature FT01977 match with good similarity some of the HMDB
reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix12-serum-pos-Thiamine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

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

```{r mix12-serum-pos-Thiamine-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)
```
FYI: no heat map with only 1 row.

```{r mix12-serum-neg-8-Oxo-2-Deoxyguanosine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.1,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

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

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

#### Summary

- FT03649 (RT = 265.1, 0.8157 ppm, `[M+H]+` ion) with confidence **B**
- FT01053 (RT = 133.1, 3.246 ppm, `[M+2H]2+` ion) with confidence **D**
- FT01318 (RT = 144, 21.42 ppm, `[M+H+Na]2+` ion) with confidence **D**
- Reference MS2: FT03649 `[M+H]+`: F07.S0497 (high confidence)


### Tryptamine

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

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

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

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

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

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

```{r mix12-serum-pos-Tryptamine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```
No spectral comparison can be performed, as no MS2 reference spectra available 
in HMDB and MassBank.

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- FT02482 (RT = 199.1, 22.99 ppm, `[M+K]+` ion) with confidence **D** 
- FT01782 (RT = 161.1, 0.9333 ppm, `[M+H]+` ion) with confidence **D** 


# Serum, negative polarity

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

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

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

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


## Signal intensity difference

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

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

## Identification of features matching standards

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

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

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

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

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

## Standards evaluation (all, incl not found/matched)

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.

### Argininosuccinic acid

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

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

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

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

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

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 mix12-serum-neg-Argininosuccinic-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. The results are shown in the heatmaps below.

```{r mix12-serum-neg-Argininosuccinic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix12-serum-neg-Argininosuccinic-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)
```

Mirror plots (check D -> C or B?):

```{r mix12-serum-neg-Argininosuccinic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.1, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```
```{r mix12-serum-pos-N-Acetyl-beta-alanine-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.2,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

Highest confidence matches (check B -> A?):

```{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

- No spectral match.
- FT2100 (RT = 198.7, 15.96 ppm, `[M-H]-` ion) with confidence **D**? high ppm
- FT2722 (RT = 198.6, 16.55 ppm, `[M+Cl]-` ion) with confidence **D**? high ppm
- FT6172 (RT = 198.6, 9.5 ppm, `[2M-H]-` ion) with confidence **D**? high ppm


### Carnosine

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

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

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

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

The extracted MS2 spectra 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 mix12-serum-neg-Carnosine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

For `r std`, there is no reference spectrum available in HMDB.

```{r mix12-serum-neg-Carnosine-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 mix12-serum-neg-Carnosine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- <**D** for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT1262** | 225.1 |   17.49   | 183.2 |    184    |  FG.001.001   |    [M-H]-     |   2   |   19.78   |  8.353   |
| **FT4669** | 451.2 |   13.53   | 183.2 |    184    |  FG.001.001   |    [2M-H]-    |   0   |   17.23   |    NA    |
| **FT1677** | 261.1 |   16.37   | 183.5 |    184    |  FG.001.001   |    [M+Cl]-    |   0   |   14.18   |  3.895   |
| **FT2164** | 293.1 |   19.93   |  186  |    184    |  FG.002.001   | [M-H+HCOONa]- |   0   |   14.09   |   4.42   |

- <**D** for, high ppm and/or rt:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| FT1821 | 271.1 | 19.01 | 198.8 | 184 | FG.007.001 | [M+CHO2]- | 0 | 14.82 | 6.615 |


### dCMP

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

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

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

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

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

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

```{r mix12-serum-neg-dCMP-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 mix12-serum-neg-dCMP-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

Spectra from features FT10671 matches with good similarity some
of the HMDB reference spectra for `r std`. Below we show the mirror plots of
the best matches (similarity score greater than 0.4).

```{r mix12-serum-neg-dCMP-mirror-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)
```

For Massbank:

```{r mix12-serum-neg-dCMP-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.

Highest confidence matches (check B -> A?):

```{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

- Reference MS2: FT2372 `[M-H]-`: FT2372_F15.S0748 dCMP
- **A** for, high ppm:

|   &nbsp;    | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct   | n_ms2 | mean_high | mean_low |
|:-----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:----------:|:-----:|:---------:|:--------:|
| **FT2372**  |  306  |   14.98   | 203.1 |    203    |  FG.001.001   |    [M-H]-  |   4   |   19.35   |  14.04   |

- **D** for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT3153** |  352  |   15.81   | 203.4 |    203    |  FG.001.001   |   [M+CHO2]-   |   0   |   13.33   |  6.688   |
| **FT6524** | 613.1 |   8.705   | 203.4 |    203    |  FG.001.001   |    [2M-H]-    |   0   |   16.48   |  4.682   |
| **FT2988** |  342  |   18.89   | 203.5 |    203    |  FG.001.001   |    [M+Cl]-    |   0   |   9.366   |  5.046   |

- < **D** for, high ppm and rt dev:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT2989** |  342  |   28.46   | 34.59 |    203    |  FG.006.001   |    [M+Cl]-    |   0   |   13.59   |  9.319   |


### Folic acid

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

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

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

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

```{r mix12-serum-neg-Folic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- none of EICs convincing, maybe **D** for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT4490** | 440.1 |   14.9    | 159.8 |    160    |  FG.001.001   |    [M-H]-     |   0   |   14.02   |  7.126   |


### N-Formyl-L-methionine

```{r, echo = FALSE}
std <- "N-Formyl-L-methionine"
```

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

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

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

EICs (check D?):

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

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

```{r mix12-serum-neg-N-Formyl-L-methionine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

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

Heat maps:

```{r mix12-serum-neg-N-Formyl-L-methionine-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix12-mix12-serum-neg-N-Formyl-L-methionine-ms2-mbank, echo = FALSE, fig.width = 5, fig.height = 5}
std_mbank <- get_mbank(mbank, inchikey = unique(std_hmdb$inchikey))
sim <- plot_ms2_similarity_heatmap(std_ms2, std_mbank, csp)
```

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- **D** for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct  | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:---------:|:-----:|:---------:|:--------:|
| **FT0692** |  176  |   17.8    | 45.64 |    37     |  FG.002.001   |  [M-H]-   |   5   |   21.12   |  18.63   |
| **FT0689** |  176  |   20.5    | 37.02 |    37     |  FG.001.001   |  [M-H]-   |   0   |   20.26   |  18.72   |
| **FT3170** | 353.1 |   16.05   | 36.93 |    37     |  FG.001.001   |  [2M-H]-  |   0   |   16.75   |  14.55   |
| **FT3171** | 353.1 |   17.74   | 45.77 |    37     |  FG.002.001   |  [2M-H]-  |   0   |   14.56   |  9.446   |



### Galactitol

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

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

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

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

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

```{r mix12-serum-neg-Galactitol-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- **D** for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct  | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:---------:|:-----:|:---------:|:--------:|
| **FT0754** | 181.1 |   19.53   | 166.9 |    167    |  FG.001.001   |  [M-H]-   |   0   |   17.24   |  13.34   |
| **FT1183** |  217  |   22.41   | 165.8 |    167    |  FG.001.001   |  [M+Cl]-  |   0   |   15.4    |  9.959   |
| **FT1296** | 227.1 |   18.88   | 166.9 |    167    |  FG.001.001   | [M+CHO2]- |   0   |   17.35   |  13.48   |

- FT0756 dropped, good EIC but high RT dev.


### Glucose 6-Phosphate

```{r, echo = FALSE}
std <- "Glucose 6-Phosphate"
```

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

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

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

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

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

```{r mix12-serum-neg-Glucose-6-Phosphate-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 mix12-serum-neg-Glucose-6-Phosphate-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 mix12-serum-neg-Glucose-6-Phosphate-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)
```
Mirror plots (check D -> C or B?):

```{r mix12-serum-neg-Glucose-6-Phosphate-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.5, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix12-serum-neg-Glucose-6-Phosphate-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.5,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

Match very good, MSMS spectra FT04100 correctly overlaps with 
D-Glucose 6-phophate sodium salt. Because match 
larger than 0.7 in MassBank, we assign B instead of C.

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)
```

The MSMS spectra FT04100 exclusively links to `r std`. Therefore, A confidence 
level can be assigned to this adduct.

#### Summary

- **A** for:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT5503** |  519  |   6.963   |  240  |    239    |  FG.001.001   |    [2M-H]-    |   3   |   20.05   |  4.906   |

- **B** for (also matches others >0.7, mainly with other hexose-n-phosphates):

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT1643** |  259  |   16.37   | 240.8 |    239    |  FG.001.001   |    [M-H]-     |   1   |   20.45   |    14    |

- **D** for:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT2360** |  305  |   15.81   | 240.4 |    239    |  FG.001.001   |   [M+CHO2]-   |   0   |   12.98   |  4.077   |

- the other peaks did not have nice EICs.


### dGMP

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

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

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

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

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

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

```{r mix12-serum-neg-dGMP-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 mix12-serum-neg-dGMP-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 mix12-serum-neg-dGMP-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)
```

Mirror plots:

```{r mix12-serum-neg-dGMP-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.4,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```



```{r mix12-serum-neg-dGMP-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.4,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

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

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


#### Summary

- B for (other match is very similar, poor MS2 spectra btw):

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT7142** | 693.1 |   2.448   | 224.3 |    223    |  FG.001.001   |    [2M-H]-    |   2   |   17.37   |  2.588   |

- B for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT3063** | 346.1 |   14.72   | 224.3 |    223    |  FG.001.001   |    [M-H]-     |   2   |   20.47   |  13.28   |


### Guanidoacetic acid

```{r, echo = FALSE}
std <- "Guanidoacetic 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, "WP", std_ms2)
```

The 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 mix12-serum-neg-Guanidoacetic-acid-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- D for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT0793** |  184  |   18.96   | 169.4 |    170    |  FG.001.001   | [M-H+HCOONa]- |   0   |   14.23   |  11.99   |


### Homocysteine

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

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

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

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

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

```{r mix12-serum-neg-Homocysteine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- No good EICs.


### Ketoleucine

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

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

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

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

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

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

```{r mix12-serum-neg-Ketoleucine-ms2, echo = FALSE}
plot_spectra(std_ms2)
```
The EIC of this peak are actually 3 distinct peaks, so won't evaluate further.
Also, not present in the DBs.

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- No MS2 spectra available.
- maybe D for, high ppm:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
**FT0678** | 175.1 |   22.24   | 34.4  |    34     |  FG.001.001   | [M+CHO2]- |   0   |   12.34   |  11.16   |


### Methotrexate

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

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

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

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

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

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

```{r mix12-serum-neg-Methotrexate-ms2, echo = FALSE}
plot_spectra(std_ms2)
```

#### Summary

- No MS2 spectra available.
- No good EICs.


### Sebacic acid

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

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

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

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

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

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

```{r mix12-serum-neg-Sebacic-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.

Heat maps:

```{r mix12-serum-neg-Sebacic-acid-ms2-hmdb, echo = FALSE, fig.width = 5, fig.height = 5}
hmdb_id <- std_dilution$HMDB[std_dilution$name == std]
std_hmdb <- Spectra(cdb, filter = ~ compound_id == hmdb_id)
sim <- plot_ms2_similarity_heatmap(std_ms2, std_hmdb, csp)
```

```{r mix12-serum-neg-Sebacic-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)
```

For spectra from untargeted features that match with good similarity to some of 
the HMDB reference spectra for `r std`, we show the mirror plots of the best 
matches.

Mirror plots (check D -> C or B?):

```{r mix12-serum-neg-Sebacic-acid-mirror-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.7, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

```{r mix12-serum-neg-Sebacic-acid-mirror-mbank, echo = TRUE, fig.cap = "Mirror plots"}
tmp_sel <- plot_select_ms2(std_ms2, std_mbank, 0.7,
                           ppm = csp@ppm, tolerance = csp@tolerance)
```

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

Highest confidence matches (check B -> A?):

```{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

- Synonyms: Decanedioic acid, 1,8-Octanedicarboxylic acid, Decane-1,10-dioic acid
- B for (because massbank, A in hmdb):

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT0993** | 201.1 |   18.59   | 37.4  |    36     |  FG.001.001   |    [M-H]-     |   2   |   18.71   |  17.31   |

- D for:

|   &nbsp;   | mzmed | ppm_error | rtmed | target_RT | feature_group |   adduct      | n_ms2 | mean_high | mean_low |
|:----------:|:-----:|:---------:|:-----:|:---------:|:-------------:|:-------------:|:-----:|:---------:|:--------:|
| **FT1792** | 269.1 |   17.18   | 35.93 |    36     |  FG.001.001   | [M-H+HCOONa]- |   0   |   11.35   |  10.34   |

- other peaks are bad EICs. eg FT0994 is noise peak but somehow got 0.74 match 
  in hdmb


### Thiamine

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

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

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

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

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

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

```{r mix12-serum-neg-Thiamine-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 mix12-serum-neg-Thiamine-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)
```

FYI: no heat map with only 1 row.

Spectra from feature FT01977 match with good similarity some of the HMDB
reference spectra for `r std`. Below we show the mirror plots of the best
matches (similarity score  greater than 0.7).

```{r mix12-serum-neg-Thiamine-hmdb, echo = TRUE, fig.cap = "Mirror plots"}
std_ms2_sel <- plot_select_ms2(std_ms2, std_hmdb, 0.2, ppm = csp@ppm,
                               tolerance = csp@tolerance)
```

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

```{r mix12-serum-neg-Thiamine-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)
```
FYI: no heat map with only 1 row.

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

- no good EICs (not 1 rt peak). eg FT1696, have high match with diff compound.


### Tryptamine

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

```{r, table-feature-matches-Tryptamine, results = "asis", echo = FALSE, message = FALSE}
feature_table <- as.data.frame(mD[mD$target_name == std, ])
pandoc.table(feature_table, style = "rmarkdown",
             split.tables = Inf,
             caption = paste0("Feature to standards matches for ", std))
```

#### Summary

- No MS2 reference spectra available in HMDB and MassBank.
- No filtered feature table, no potential peaks found.
- No MS2 spectra available (because no potential peaks for `r std`).


# Summary on the ion database

# Session information

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

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