Applies BP-Quant to a pepData object
bpquant(statRes, pepData, pi_not = 0.9, max_proteoforms = 5, parallel = TRUE)an object of the class 'statRes'
an omicsData object of the class 'pepData' that includes the e_meta component
numeric value between 0 and 1 indicating the background probability/frequency of a zero signature
a numeric value corresponding to the maximum threshold for the number of possible proteoforms
a logical indicator of whether the calculation will be parallelized
a list of data frames, one for each unique protein. The data frames have three columns, a protein identifier, a peptide identifier, and a "ProteoformID". The class of this list is 'isoformRes'.
The result of this function can be used as one the isoformRes
input argument to protein_quant. The bpquant function
itself operates as follows: The statRes object contains the signatures data
frame, the pepData object is used for its e_meta data frame. Next the
signatures data frame and e_meta are merged by their edata_cname (e.g.
peptide identifier) columns, this new data frame called protein_sig_data
will be input to bpquant_mod in a “foreach” statement. “Foreach” will
subset protein_sig_data for each unique protein and apply bpquant_mod to
each subset and store the results.
# \donttest{
library(pmartRdata)
mypepData <- group_designation(
omicsData = pep_object,
main_effects = c("Phenotype")
)
mypepData = edata_transform(omicsData = mypepData, data_scale = "log2")
imdanova_Filt <- imdanova_filter(omicsData = mypepData)
mypepData <- applyFilt(
filter_object = imdanova_Filt,
omicsData = mypepData,
min_nonmiss_anova = 2
)
imd_anova_res <- imd_anova(
omicsData = mypepData,
test_method = 'combined',
pval_adjust_a_multcomp = 'bon',
pval_adjust_g_multcomp = 'bon'
)
result = bpquant(statRes = imd_anova_res, pepData = mypepData)
# }