splineDiffExprs(eSetObject, df, cutoff.adj.pVal=1, reference, intercept=TRUE)
ExpressionSet
object of class ExpressionSet
containing log-ratios or log-values of expression for a series of microarraysTRUE
, F-test includes all parameters; if FALSE
, F-test includes shape parameters only; default is TRUE
The first columns contain all feature data of the eSetObject
(fData(eSetObject)
), if any feature data were defined. Otherwise, only one column row_IDs
, containing the row names is created. The b_0
, b_1
,..., b_m
coefficients correspond to the reference model parameters. The d_0
, d_1
,..., d_m
coefficients represent the differences between the reference model parameters and the model parameters in the compared group. AveExprs
refers to the average log2-expression for a probe (representing a gene) over all arrays. The F
column contains moderate F-statistics, P.Value
raw p-value and adj.P.Value
Benjamini-Hochberg adjusted p-value.
The input eSetObject
must be provided as an object of class ExpressionSet
which contains SampleName
, Time
, Treatment
and if applicable Replicates
variables (columns) included in the phenotypic data of the eSetObject
(pData(eSetObject)
). Two types of Treatment
defining two groups to compare have to be definied.
Replicates are not required. The time points for compared treatment groups should be identical.
User has to define number of degrees of freedom (df
) for the spline regression model. Choosing effective degrees of freedom in range 3-5 is reasonable.
Time dependent differential expression of a gene is determined by the application of empirical Bayes moderate F-statistics on the differences of coefficient values of the fitted natural cubic spline regression models for the same gene in the two compared treatment groups. In other words, comparing the coefficient values of the fitted splines in both groups allows the detection of differences in the shape of the curves, which represent the gene expressions changes over time. Ouptut table containing Benjamini-Hochberg adjusted p-value (adj.P.Value
) is used to define differentially expressed genes. The default value for cutoff.adj.pVal
is set to 1
, which means that all genes are included in output table.
limma
## load "eSetObject" containing simulated time-course data
data(TCsimData)
pData(TCsimData)
## define function parameters
df <- 3
cutoff.adj.pVal <- 0.01
reference <- "T1"
intercept <- TRUE
diffExprs <- splineDiffExprs(eSetObject = TCsimData, df, cutoff.adj.pVal, reference, intercept)
head(diffExprs,3)
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