process_protein.Rd
Process bulk proteomics data
process_protein(
multiassay,
filter = TRUE,
min_sample = 0.5,
dependent = "diagnosis",
levels = c("Control", "AD"),
imputation = "minimum_value",
remove_feature_outliers = TRUE,
batch_correction = TRUE,
batch = "batch",
correction_method = c("Combat", "Median_centering"),
remove_sample_outliers = TRUE,
denoise = FALSE,
covariates = c("sex", "age")
)
Multiassay experiment object generated by Omix
generate_multiassay
function
Logical whether to perform protein filtering
Percentage of samples that must non missing protein value, or the protein is filtered out protein value, or else the protein is excluded
Dependent variable for groups. If no specific dependent variable exist, set parameter as NULL
Character vector with reference group as first element. Set parameter as NULL if dependent is NULL.
Possible values are distribution
, minimum_value
,zero
default to minimum_value
.
Logical whether to remove feature outliers
Logical whether to perform technical batch correction
Technical batch
Correction methods available to counteract batch
effects. Possible values are Combat
or median_centering
Logical whether to remove sample outliers
Logical whether to denoise biological covariates
Covariates used in linear mixed model for denoising
a MultiAssayExperiment object with protein_processed
slot
Other Pre-processing:
batch_correction_protein()
,
combat_correction()
,
filter_protein()
,
generate_multiassay()
,
median_centering_correction()
,
process_rna()