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)

Loading required package: Biobase
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rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
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SampleName Time Treatment Replicate
1 T1_1_A 1 T1 A
2 T1_4_A 4 T1 A
3 T1_8_A 8 T1 A
4 T1_16_A 16 T1 A
5 T1_24_A 24 T1 A
6 T1_32_A 32 T1 A
7 T1_40_A 40 T1 A
8 T1_48_A 48 T1 A
9 T1_1_B 1 T1 B
10 T1_4_B 4 T1 B
11 T1_8_B 8 T1 B
12 T1_16_B 16 T1 B
13 T1_24_B 24 T1 B
14 T1_32_B 32 T1 B
15 T1_40_B 40 T1 B
16 T1_48_B 48 T1 B
17 T2_1_A 1 T2 A
18 T2_4_A 4 T2 A
19 T2_8_A 8 T2 A
20 T2_16_A 16 T2 A
21 T2_24_A 24 T2 A
22 T2_32_A 32 T2 A
23 T2_40_A 40 T2 A
24 T2_48_A 48 T2 A
25 T2_1_B 1 T2 B
26 T2_4_B 4 T2 B
27 T2_8_B 8 T2 B
28 T2_16_B 16 T2 B
29 T2_24_B 24 T2 B
30 T2_32_B 32 T2 B
31 T2_40_B 40 T2 B
32 T2_48_B 48 T2 B

Differential analysis done for df = 3 and adj.P.Val <= 0.01
Number of differentially expressed genes: 952
row_IDs b_0 b_1 b_2 b_3 d_0
BRAF BRAF 0.2248186 0.06672491 0.11466685 0.01113329 0.04537218
EEF2 EEF2 0.1662325 0.03093095 0.02415684 0.05805703 0.04218390
OR5W2 OR5W2 0.5600514 0.11141556 0.26764248 0.13582290 0.10798569
d_1 d_2 d_3 AveExpr F P.Value
BRAF 0.09884069 0.32667028 0.1362235 0.2616200 100.47655 8.552316e19
EEF2 0.03907564 0.36776057 0.1663112 0.2632523 75.34076 8.442937e17
OR5W2 0.29355341 0.08948516 0.1577280 0.5454758 71.98754 1.723753e16
adj.P.Val
BRAF 1.710463e15
EEF2 8.442937e14
OR5W2 1.149169e13
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