test_diff {DEP} | R Documentation |
test_diff
performs a differential enrichment test based on
protein-wise linear models and empirical Bayes
statistics using limma. False Discovery Rates are estimated
using fdrtool.
test_diff(se, type = c("control", "all", "manual"), control = NULL, test = NULL, design_formula = formula(~0 + condition))
se |
SummarizedExperiment,
Proteomics data (output from |
type |
"control", "all" or "manual", The type of contrasts that will be tested. This can be all possible pairwise comparisons ("all"), limited to the comparisons versus the control ("control"), or manually defined contrasts ("manual"). |
control |
Character(1), The condition to which contrasts are generated if type = "control" (a control condition would be most appropriate). |
test |
Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC"). |
design_formula |
Formula, Used to create the design matrix. |
A SummarizedExperiment object containing fdr estimates of differential expression.
# Load example data <- UbiLength data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",] data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";") # Make SummarizedExperiment columns <- grep("LFQ.", colnames(data_unique)) exp_design <- UbiLength_ExpDesign se <- make_se(data_unique, columns, exp_design) # Filter, normalize and impute missing values filt <- filter_missval(se, thr = 0) norm <- normalize_vsn(filt) imputed <- impute(norm, fun = "MinProb", q = 0.01) # Test for differentially expressed proteins diff <- test_diff(imputed, "control", "Ctrl") diff <- test_diff(imputed, "manual", test = c("Ubi4_vs_Ctrl", "Ubi6_vs_Ctrl")) # Test for differentially expressed proteins with a custom design formula diff <- test_diff(imputed, "control", "Ctrl", design_formula = formula(~ 0 + condition + replicate))