Proteomics training differential analysis — proteomics_training

Training differential analysis for the proteome, phosphoproteome, acetylome, and ubiquitylome. Use limma to perform an F-test to test the effect of training across time points. Analysis is performed separately for males and females.

Usage

proteomics_training_da(assay, tissue, exclude_outliers = TRUE)

Arguments

assay: character, abbreviation for proteomics assay to be analyzed as defined by MotrpacRatTraining6moData::ASSAY_ABBREV. One of the following: PROT, PHOSPHO, ACETYL, UBIQ
tissue: character, tissue abbreviation, one of MotrpacRatTraining6moData::TISSUE_ABBREV
exclude_outliers: bool, whether to remove sample outliers specified in MotrpacRatTraining6moData::OUTLIERS. TRUE by default.

Value

a data frame with one row per proteomics feature:

feature_ID: character, MoTrPAC feature identifier
assay: character, assay abbreviation, one of MotrpacRatTraining6moData::ASSAY_ABBREV
assay_code: character, assay code used in data release. See MotrpacBicQC::assay_codes.
tissue: character, tissue abbreviation, one of MotrpacRatTraining6moData::TISSUE_ABBREV
tissue_code: character, tissue code used in data release. See MotrpacBicQC::bic_animal_tissue_code.
removed_samples: character, comma-separated list of outliers (vial labels) removed from differential analysis
fscore_male: double, F statistic for males
fscore_female: double, F statistic for females
p_value_male: double, nominal F-test p-value for males
p_value_female: double, nominal F-test p-value for females
full_model: character, full model used in F-test
reduced_model: character, reduced model used in F-test
p_value: double, combined male and female nominal p-value using the sum of logs

Examples

# Run training differential analysis for heart proteins
res = proteomics_training_da("PROT","HEART")
#> PROT_HEART_NORM_DATA