Run DE analysis on a single normalized data set
Usage
run_DE_single(
se,
method,
comparisons,
condition = NULL,
DE_method = "limma",
covariate = NULL,
logFC = TRUE,
logFC_up = 1,
logFC_down = -1,
p_adj = TRUE,
alpha = 0.05,
B = 100,
K = 500,
trend = TRUE,
robust = TRUE
)Arguments
- se
SummarizedExperiment containing all necessary information of the proteomics data set
- method
String specifying which assay should be used as input
- comparisons
Vector of comparisons that are performed in the DE analysis (from specify_comparisons method)
- condition
column name of condition (if NULL, condition saved in SummarizedExperiment will be taken)
- DE_method
String specifying which DE method should be applied (limma, ROTS)
- covariate
String specifying which column to include as covariate into limma
- logFC
Boolean specifying whether to apply a logFC threshold (TRUE) or not (FALSE)
- logFC_up
Upper log2 fold change threshold (dividing into up regulated)
- logFC_down
Lower log2 fold change threshold (dividing into down regulated)
- p_adj
Boolean specifying whether to apply a threshold on adjusted p-values (TRUE) or on raw p-values (FALSE)
- alpha
Threshold for adjusted p-values or p-values
- B
Number of bootstrapping for ROTS
- K
Number of top-ranked features for reproducibility optimization
- trend
logical, should an intensity-dependent trend be allowed for the prior variance? If FALSE then the prior variance is constant. Alternatively, trend can be a row-wise numeric vector, which will be used as the covariate for the prior variance.
- robust
logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances?