aLFQ (version 1.3.6)

APEX: Training, testing and validation of APEX peptide observability models

Description

Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data.

Usage

# S3 method for default
APEX(data, ...)
# S3 method for APEX
predict(object, newdata=NULL, ...)
# S3 method for APEX
cval(object, folds=10, ...)
# S3 method for APEX
print(x, ...)
# S3 method for APEX
plot(x, ...)

Arguments

data

an R object of type "apexFeatures".

object

an APEX object.

newdata

an R object of type "apexFeatures".

folds

a positive integer value of the number of folds for cross-validation.

x

an APEX object.

...

future extensions.

Value

An object of class APEX.

Details

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.

References

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).

See Also

import, ProteinInference, AbsoluteQuantification, ALF, apexFeatures, proteotypic

Examples

# NOT RUN {
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)
# }