filter_proteins {DEP} | R Documentation |
filter_proteins
filters a proteomic dataset based on missing values.
Different types of filtering can be applied, which range from only keeping
proteins without missing values to keeping proteins with a certain percent
valid values in all samples or keeping proteins that are complete
in at least one condition.
filter_proteins(se, type = c("complete", "condition", "fraction"), thr = NULL, min = NULL)
se |
SummarizedExperiment,
Proteomics data (output from |
type |
"complete", "condition" or "fraction", Sets the type of filtering applied. "complete" will only keep proteins with valid values in all samples. "condition" will keep proteins that have a maximum of 'thr' missing values in at least one condition. "fraction" will keep proteins that have a certain fraction of valid values in all samples. |
thr |
Integer(1), Sets the threshold for the allowed number of missing values in at least one condition if type = "condition". |
min |
Numeric(1), Sets the threshold for the minimum fraction of valid values allowed for any protein if type = "fraction". |
A filtered SummarizedExperiment object.
# 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 stringent_filter <- filter_proteins(se, type = "complete") less_stringent_filter <- filter_proteins(se, type = "condition", thr = 0)