The function processes MSe data using the
function of the
synapter package and combines resulting
Synapter instances into one
and organelle marker data is added as a feature-level annotation variable.
The master FDR value. Default is 0.025.
Additional paramters passed to
LOPIMS pipeline is composed of 5 steps:
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
Each MSe/pep3D pair is processed using the HDMSe master file
The respective peptide-level
synergise output objects
are converted and combined into an single
"MSnSet" instance. (function
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
NAs, 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
The markers defined in the
markerfile are collated as
feature meta-data in the
markers variable. See
addMarkers for details. (function
synergise reports as well as resulting objects
are stored in a
For details, please refer to the
synapter vignette and
An instance of class
"MSnSet" with protein
level quantitation and respective organelle markers.
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.
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