Simulated data for 5 gene sets of 50 genes each. Gene expression is simulated at 5 time points for 10 patients.
data(data_simu_TcGSA)See Details.
See Details.
a data frame with 5 variables:
Patient_ID: a factor that contains the patient ID.
TimePoint: a numeric vector or a factor that contains the time points at which gene expression was measured.
sample_name: a character vector with the names of the sample (corresponding to the names of the columns of expr_1grp and of expr_2grp).
group.var: a factor that indicates to which of the 2 treatment groups each sample belongs to.
Group_paired_ID
a random paired identifier for paired couples (one in each of the 2 treatment groups) of patients.
a gmt object containing the gene sets definition. See GSA.read.gmt and GMT definition on www.broadinstitute.org.
In expr_1grp all patients belong to the same unique treatment group. The first 2 gene sets are simulated under the null hypothesis. The gene sets 3 and 4 are simulated under the alternative hypothesis that there is a significant homogeneous time trend within the gene set. The gene set 5 is simulated under the alternative hypothesis that there are significant heterogeneous time trends within the gene set.
In expr_2grp all patients belong to 2 treatment groups. The 5 first patients belong to the treatment group 'T', The 5 other patients belong to the treatment group 'C'. The first 2 gene sets are simulated under the null hypothesis that there is no difference in the time trend between the 2 treatment groups. The gene sets 3 and 4 are simulated under the alternative hypothesis that there are significantly different homogeneous time trends within the gene set between the 2 treatment groups. The gene set 5 is simulated under the alternative hypothesis that there are significantly different heterogeneous time trends between the 2 treatment groups within the gene set.
# NOT RUN {
data(data_simu_TcGSA)
summary(expr_1grp)
summary(design)
gmt_sim
# }
Run the code above in your browser using DataLab