set_DESeq2 {benchdamic} | R Documentation |
Set the parameters for DESeq2 differential abundance detection method.
set_DESeq2( pseudo_count = FALSE, design = NULL, contrast = NULL, alpha = 0.05, norm = c("ratio", "poscounts", "iterate"), weights_logical = FALSE, expand = TRUE )
pseudo_count |
add 1 to all counts if TRUE (default
|
design |
(Required). A
|
contrast |
character vector with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change. |
alpha |
the significance cutoff used for optimizing the independent filtering (by default 0.05). If the adjusted p-value cutoff (FDR) will be a value other than 0.05, alpha should be set to that value. |
norm |
name of the normalization method used to compute the
normalization factors to use in the differential abundance analysis. If
|
weights_logical |
logical vector, if TRUE a matrix of observational
weights will be used for differential abundance analysis (default
|
expand |
logical, if TRUE create all combinations of input parameters
(default |
A named list containing the set of parameters for DA_DESeq2
method.
# Set some basic combinations of parameters for DESeq2 base_DESeq2 <- set_DESeq2(design = ~ group, contrast = c("group", "B", "A")) # Set a specific set of normalization for DESeq2 (even of other packages!) setNorm_DESeq2 <- set_DESeq2(design = ~ group, contrast = c("group", "B", "A"), norm = c("TMM", "poscounts")) # Set many possible combinations of parameters for edgeR all_DESeq2 <- set_DESeq2(pseudo_count = c(TRUE, FALSE), design = ~ group, contrast = c("group", "B", "A"), weights_logical = c(TRUE,FALSE))