lopims {pRoloc} | R Documentation

## A complete LOPIMS pipeline

### Description

The function processes MSe data using the `synergise` function of the `synapter` package and combines resulting `Synapter` instances into one `"MSnSet"` and organelle marker data is added as a feature-level annotation variable.

### Usage

    lopims(hdmsedir = "HDMSE", msedir = "MSE", pep3ddir = "pep3D",
      fastafile, markerfile, mfdr = 0.025, ...)

### Arguments

`hdmsedir` | A `character` identifying the directory containing the HDMSe final peptide files. Default is `HDMSe`.
`msedir` | A `character` identifying the directory containing the MSe final peptide files. Default is `MSe`.
`pep3ddir` | A `character` identifying the directory containing the MSe pep 3D files. Default is `pep3D`.
`fastafile` | A `character` identifying the protein fasta database. Default is to use the fasta file in the current directory. If several such files exist, the function reports an error.
`markerfile` | A `character` identifying the marker file (see details for format). Default is to use a `csv` file starting with `marker` in the current directory. If several such files exist, the function reports an error.
`mfdr` | The master FDR value. Default is 0.025.
`...` | Additional paramters passed to `synergise`.

### Details

The `LOPIMS` pipeline is composed of 5 steps:

1. The HDMSe final peptide files are used to compute false discovery rates uppon all possible combinations of HDMSe final peptides files and the best combination smaller or equal to `mfdr` is chosen. See `estimateMasterFdr` for details. The corresponding master run is then created as descibed in `makeMaster`. (function `lopims1`)

2. Each MSe/pep3D pair is processed using the HDMSe master file using `synergise`. (function `lopims2`)

3. The respective peptide-level `synergise` output objects are converted and combined into an single `"MSnSet"` instance. (function `lopims3`)

4. Protein-level quantitation is inferred as follows. For each protein, a reference sample/fraction is chosen based on the number of missing values (`NA`). If several samples have a same minimal number of `NA`s, ties are broken using the sum of counts. The peptides that do not display any missing values for each (frac_i, frac_ref) pair are summed and the ratio is reported (see pRoloc:::refNormMeanOfNonNAPepSum for details). (function `lopims4`)

5. The markers defined in the `markerfile` are collated as feature meta-data in the `markers` variable. See `addMarkers` for details. (function `lopims5`)

Intermediate `synergise` reports as well as resulting objects are stored in a `LOPIMS_pipeline` directory. For details, please refer to the `synapter` vignette and reference papers.

### Value

An instance of class `"MSnSet"` with protein level quantitation and respective organelle markers.

### Author(s)

Laurent Gatto

### References

Improving qualitative and quantitative performance for MSE-based label free proteomics N.J. Bond, P.V. Shliaha, K.S. Lilley and L. Gatto Journal of Proteome Research, 2013;12(6):2340-53. PMID: 23510225.

The Effects of Travelling Wave Ion Mobility Separation on Data Independent Acquisition in Proteomics Studies P.V. Shliaha, N.J. Bond, L. Gatto and K.S. Lilley Journal of Proteome Research, 2013;12(6):2323-39. PMID: 23514362.

MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. L. Gatto and KS. Lilley. Bioinformatics. 2012 Jan 15;28(2):288-9. doi: 10.1093/bioinformatics/btr645. Epub 2011 Nov 22. PubMed PMID: 22113085.

[Package pRoloc version 1.30.0 Index]