Training, testing and validation of APEX peptide observability models
Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data.
## Default S3 method: APEX(data, ...) ## S3 method for class 'APEX' predict(object, newdata=NULL, ...) ## S3 method for class 'APEX' cval(object, folds=10, ...) ## S3 method for class 'APEX' print(x, ...) ## S3 method for class 'APEX' plot(x, ...)
data |
an R object of type |
object |
an |
newdata |
an R object of type |
folds |
a positive integer value of the number of folds for cross-validation. |
x |
an |
... |
future extensions. |
The APEX module is a reimplementation of the original algorithm (Lu et al., 2006; Vogel et al., 2008) using the randomForest package. It requires apexFeatures input objects and reports the results in an APEX object, which can be used by the ProteinInference module for protein quantification.
An object of class APEX.
George Rosenberger gr2578@cumc.columbia.edu
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotech 25, 117-124 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
set.seed(131) data(APEXMS) APEX_ORBI<-head(APEX_ORBI,50) # Remove this line for real applications APEX_ORBI.af <- apexFeatures(APEX_ORBI) APEX_ORBI.apex <- APEX(data=APEX_ORBI.af) print(APEX_ORBI.apex) APEX_ORBI_cval.apex <- cval(APEX_ORBI.apex, folds=2) plot(APEX_ORBI_cval.apex)
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