Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/splineDiffExprs.R
The function compares time dependent behaviour of genes in two different groups. Applying empirical Bayes moderate Fstatistic on differences in coefficients of fitted natural cubic spline regression models, differentially expressed in time genes are determined. The function is a wrapper of other Rfunctions to simplify differential expression analysis of timecourse data.
1  splineDiffExprs(eSetObject, df, cutoff.adj.pVal=1, reference, intercept=TRUE)

eSetObject 

df 
number of degrees of freedom 
cutoff.adj.pVal 
BenjaminiHochberg adjusted pvalue cutoff 
reference 
character defining which treatment group should be considered as reference 
intercept 
if 
The function fits a temporal trend using a natural cubic spline regression to simulate nonlinear behaviour of genes over time.
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 35 is reasonable.
Time dependent differential expression of a gene is determined by the application of empirical Bayes moderate Fstatistics 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 BenjaminiHochberg adjusted pvalue (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.
A data.frame with rows defining names/IDs of differentially expressed genes and additional columns described below.
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 log2expression for a probe (representing a gene) over all arrays. The F
column contains moderate Fstatistics, P.Value
raw pvalue and adj.P.Value
BenjaminiHochberg adjusted pvalue.
Agata Michna
1 2 3 4 5 6 7 8 9 10 11 12  ## load "eSetObject" containing simulated timecourse 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)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.