README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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)
```

# MALDIpqi

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MALDIpqi calculates Parchment Glutamine Index from MALDI TOF ZooMS data.
It is a sample level measure of the glutamine deamidation from a list of peptides.

For details in the data processing and mathematical method and models to estimate PQI, refer to our publication Nair et al. (2022)

The method was initially developed to estimate parchment quality based on deamidation, following the ideas from Wilson et al. (2012). However, it can be applied in other tissues as in van Doorn et al. (2012) or Brown et al. (2021)

## Installation

You can install the released version of MALDIpqi from github with:

```{r, eval=FALSE}
install.packages('devtools')
# We need to tell R to also look in Bioconductor for the packages Spectra and mzR
setRepositories(ind=1:2)
devtools::install_github("ismaRP/MALDIpqi")
```

## Example

```{r}
data_folder = "data/mzML"
```


This is minimal workflow. It assumes the spectra are in `r data_folder`,
in mzML format and with file names in the form "samplename_replicate.ext".
Where the replicate number is 1, 2 or 3 and ext is the extension of the files.
```{r example2, eval=F}
# Read metadata and remove rows that don't have a corresponding mzML file
zooms_metadata = read_csv('./metadata.csv')
zooms_metadata = clean_metadata(zooms_metadata, data_folder)

# Read peptides, or don't provide to use default
peptides = read_csv('peptides.csv'))

# Preprocess spectra
peaks = MALDIpqi::preprocess_spectra(
  indir = data_folder, metadata = zooms_metadata,
  mono_masses = peptides$mass,
  smooth_wma_hws = 4,
  smooth_sg_hws = 6,
  iterations = 50,
  halfWindowSize = 20,
  snr = 2, k = 0L, threshold = 0.33,
  local_bg = FALSE,
  mass_range = 100, bg_cutoff = 0.5, l_cutoff = 1e-8,
  tolerance = 0.4, ppm = 50,
  n_isopeaks = 5,
  min_isopeaks = 4,
  ncores = 6, chunk_size = 60
)
peaks = prepare_peaks(peaks, peptides, n_isopeaks = 5)

# Calculate q2e for each sample, replicate and peptide
q2e_vals = peaks %>% filter(n_peaks > 0) %>%
  group_by(sample, replicate, pep_number) %>%
  summarise(wlm_q2e(norm_int, weight, deam_0, deam_1, deam_2))

# Calculate PQI
pqi_vals = lme_pqi(q2e_vals, logq = TRUE, g = 'free', return_model = TRUE)


```


## References
Nair, B. et al. (2022) ‘Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data’, bioRxiv. [doi:10.1101/2022.03.13.483627](doi:10.1101/2022.03.13.483627).

Wilson, J., van Doorn, N.L. and Collins, M.J. (2012) ‘Assessing the extent of bone degradation using glutamine deamidation in collagen’, Analytical chemistry, 84(21), pp. 9041–9048. [https://doi.org/10.1021/ac301333t](https://doi.org/10.1021/ac301333t)

van Doorn, N.L. et al. (2012) ‘Site-specific deamidation of glutamine: a new marker of bone collagen deterioration’, Rapid communications in mass spectrometry: RCM, 26(19), pp. 2319–2327. [https://doi.org/10.1002/rcm.6351](https://doi.org/10.1002/rcm.6351)

Brown, S. et al. (2021) ‘Examining collagen preservation through glutamine deamidation at Denisova Cave’, Journal of archaeological science, 133, p. 105454. [http://doi.org/10.1016/j.jas.2021.105454](http://doi.org/10.1016/j.jas.2021.105454)

Bethencourt, J.H. et al. (2022) ‘Data from “A biocodicological analysis of the medieval library and archive from Orval abbey, Belgium”’, Journal of open archaeology data, 10(0). Available at: [https://doi.org/10.5334/joad.89](https://doi.org/10.5334/joad.89).

Ruffini-Ronzani, N. et al. (2021) ‘A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium’, Royal Society Open Science, 8(6), p. 210210. [https://doi.org/10.1098/rsos.210210](https://doi.org/10.1098/rsos.210210)