| Means_DDCt | R Documentation |
\Delta \Delta C_T) analysis using a fitted modelPerforms relative expression (fold change) analysis based on the
\Delta \Delta C_T method using a fitted model object produced by
ANOVA_DDCt() or REPEATED_DDCt().
Means_DDCt(model, specs, p.adj = "none")
model |
A fitted model object (typically an |
specs |
A character string or character vector specifying the predictors or
combinations of predictors over which fold change values are desired.
This argument follows the specification syntax used by
|
p.adj |
Character string specifying the method for adjusting p-values.
See |
The Means_DDCt function calculates fold change (FC) values using
estimated marginal means derived from a fitted model.
For ANOVA models, FC values can be obtained for main effects,
interactions, and sliced (simple) effects.
For ANCOVA models returned by the rtpcr package, only simple
effects are supported.
Internally, this function relies on the emmeans package to
compute marginal means and contrasts, which are then back-transformed
to fold change values using the \Delta \Delta C_T framework.
A data frame containing estimated fold change values, confidence intervals, p-values, and significance levels derived from the fitted model.
Ghader Mirzaghaderi
# Obtain a fitted model from ANOVA_DDCt
res <- ANOVA_DDCt(
data_3factor,
numberOfrefGenes = 1,
mainFactor.column = 1,
block = NULL
)
# Fold change values for Type main effect
Means_DDCt(res$lm_ANOVA, specs = "Type")
# Fold change values for Concentration main effect
Means_DDCt(res$lm_ANOVA, specs = "Conc")
# Fold change values for Concentration sliced by Type
Means_DDCt(res$lm_ANOVA, specs = "Conc | Type")
# Fold change values for Concentration sliced by Type and SA
Means_DDCt(res$lm_ANOVA, specs = "Conc | Type * SA")
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