I need help with Time series testing using DESeq2.
- I have 5 subjects given same treatment at 11 different time points (for each subject: single observation at 1 day interval from 0d through 10d). 

I). the sampledata table looks like (tab-delimited),
subject    time (d)
A    D0
...
A    D10

B    D0
...
B    D10

C    D0
...
C    D10

D    D0
...
D    D10

E    D0
...
E    D10

II). The objective is to compare each time point with 0d (baseline reference) within each subject, separately: for example, D_D10 vs D_D0 (not the default baseline A_D0). I tried to set A, B, C, D & E by turn as baseline subjects and D0 as baseline time and use contrast shown below in example for subject D.

#set relevel for subject 
sampledata$subject <- relevel(sampledata$subject, ref = "D");

#set relevel for time factor (default anyway)
sampledata$time <- relevel(sampledata$time, ref = "D0");

#create DESeq data object & define model design
ddsD <- DESeqDataSetFromMatrix(countData = countsdata, colData = sampledata, design = ~ subject + time);

#LRT test with reduced model
ddsD <- DESeq(ddsD, test="LRT", reduced = ~subject); #or ~time (get the same comparison terms)

#resultsNames
> resultsNames(ddsD)
 [1] "Intercept"          "subject_A_vs_D" "subject_B_vs_D"
 [4] "subject_C_vs_D" "subject_E_vs_D" "time_D1_vs_D0"     
 [7] "time_D2_vs_D0"      "time_D3_vs_D0"      "time_D4_vs_D0"     
[10] "time_D5_vs_D0"      "time_D6_vs_D0"      "time_D7_vs_D0"     
[13] "time_D8_vs_D0"      "time_D9_vs_D0"      "time_D10_vs_D0"

#ignore subject effect terms &  use only (baseline) time point comparison terms as below
results(ddsD, name= "time_D10_vs_D0", test="Wald");

Will this work for my objective? I am not sure which 'D0' is being used here (is it from subject A or D?). If this is wrong, how should I proceed with the test? Thanks.

How do you expect to compare a single observation to a single observation ("compare each time point with 0...within each subject, separately")? There is no replication for that level of analysis. I would recommend that you consult with a local statistician on your experimental design.