fitCPA: CPA for a set of proteins

In mooredf22/protlocassign: Estimation of protein distribution from quantitative subcellular fractionation experiments using a constrained proportional assignment model

CPA for a set of proteins

Description

Carry out constrained proportional assignment on profiles of a set of proteins

Usage

fitCPA(
  profile,
  refLocationProfiles,
  numDataCols,
  startProps = NULL,
  maxit = 10000,
  showProgress = TRUE,
  ind.vary = NULL,
  minVal = FALSE
)

Arguments

profile	data frame of specified protein(row name) profiles
refLocationProfiles	data frame of profiles for the reference compartments
numDataCols	number of fractions in each profile
startProps	starting values for proportional assignments; set equal if this is null (default)
maxit	maximum number of iterations (default is 10000)
showProgress	print out progress if TRUE, the default
ind.vary	if not NULL, indexes of parameters to allow to vary; remaining parameters are fixed at zero
minVal	default is FALSE. If TRUE, return minimum value of goodness of fit

Value

Data frame of CPA estimates for each protein

Examples

data(protRSA_test)
data(refLocProfRSA)
protCPAfromRSA_out <- fitCPA(profile=protRSA_test,
                           refLocationProfiles=refLocProfRSA,
                           numDataCols=9)
# Note that the profile of one protein, AIF1, contains missing values
# which causes the cpa routine to generate a nonconvergence message                           
round(head(protCPAfromRSA_out), digits=4)                           

mooredf22/protlocassign documentation built on Sept. 13, 2023, 3:57 p.m.