![]() Therefore, by default, the column logFC in the output data frame contains \(\log_(M_1/M_2)\), where \(M_k\) represent the mean of condition \(k\). The default value for this transformation is log10. # 6 ATP-dependent RNA helicase DED1 (EC 3.6.4.13)īefore limma is called, intensity data are transformed using the transform.fun function (a parameter of limmaDE). # 5 Restriction of telomere capping protein 1 # 4 Vacuolar protein sorting/targeting protein PEP1 (Carboxypeptidase Y receptor) (CPY receptor) (Carboxypeptidase Y-deficient protein 1) (Sortilin VPS10) (Vacuolar carboxypeptidase sorting receptor VPS10) (Vacuolar protein sorting-associated protein 10) (Vacuolar protein-targeting protein 1) # 2 Putative uncharacterized membrane protein YIR036W-A # B significant mean_A mean_B ngood_A ngood_B uniprot # 2 sp|A0A023PYI5|YI036_YEAST NA NaN NA NA NA ![]() Head(res) # protein logFC AveExpr t P.Value adj.P.Val ![]() Hence, if we want to aggregate proteins by protein group rather than razor proteins (which is the default), we need to do the following: The peptide-to-protein table is then used by makeProteinTable to aggregate proteins. We want to have it in the peptide object, in case peptide-level analysis needs to find out which proteins or protein groups these peptides belong to. It might look surprising that protein/group selection is done at the peptide level, but this is where peptide-to-protein conversion table is build and stored in the output object (have a look at pepdat$pep2prot - it’s a data frame with two columns, peptide and protein). The choice of column to aggregate protein data is controlled by parameter l in the makePeptideTable function. We can change this default approach and, instead of razor proteins, we can use protein groups, as listed in Proteins column of the evidence file (this column is renamed to protein_group by readEvidenceFile to avoid confusion). This means that for a given protein makeProteinTable would aggregate data from all peptides having this protein ID in the Leading razor protein column. Here, we added a replicate column, but other information, in particular describing batch effects, can be very useful.īy default protein intensities are aggregated from peptide intensities based on the Leading razor protein column from the evidence file (this column is renamed to protein by readEvidenceFile for simplicity). Other columns can be added and used for the downstream analysis. In our example there are two conditions, hence two values in this column. This information will be used in differential expression. Sample identifies a given experiment/measure uniquely, so sample names must be unique.Ĭolumn condition contains condition names. For clarity, we recommend a short name consisting of a condition and replicate (in a simple design where such decomposition is possible). ![]() In multiplexed data (e.g. TMT) there can be several measurements columns.Ĭolumn sample should contain (short) names corresponding to a given experiment and measure. In case of the label-free experiment there is only one measure column, “Intensity”. # "AIF MS/MS IDs" "Deamidation (NQ) site IDs" # "Number of scans" "Number of isotopic peaks" # "Retention time calibration" "Match time difference" # "Calibrated retention time start" "Calibrated retention time finish" # "Retention length" "Calibrated retention time" # "Uncalibrated mass error " "Uncalibrated mass error " # "Uncalibrated - Calibrated m/z " "Uncalibrated - Calibrated m/z " # "Leading proteins" "Leading razor protein" The data are distributed in a package proteusLabelFree, which needs installing and loading first: We use an example data set from an unpublished experiment by Katharina Trunk, Sarah Coulthurst, Julien Peltier and Matthias Trost. Tutorial de proteus 8 how to#This tutorial demonstrates how to analyse data from label free MS/MS experiment in Proteus.
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