APEX {aLFQ}

Training, testing and validation of APEX peptide observability models

Description

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

Usage

## 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, ...)

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.

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.

Value

An object of class APEX.

Author(s)

George Rosenberger [e-mail]

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

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)