I am new to time-series and multifactorial design. I have read the forum discussions and DESeq2 vignette that deal with multifactorial designs and have build my model. However, I have few questions that would help me understand and interpret the output.
My data contains four time points (T0, T8, T16, T24), two genotypes (m4, p5) and three conditions (control, susceptible and resilient) as shown below:
samples time genotype condition T0_1 T0 m3 control T0_2 T0 m3 control T0_3 T0 m3 control T0_1 T0 m3 resilient T0_2 T0 m3 resilient T0_3 T0 m3 resilient T0_1 T0 m3 susceptible T0_2 T0 m3 susceptible T0_3 T0 m3 susceptible T0_1 T0 p5 control T0_2 T0 p5 control T0_3 T0 p5 control T0_1 T0 p5 resilient T0_2 T0 p5 resilient T0_3 T0 p5 resilient T0_1 T0 p5 susceptible T0_2 T0 p5 susceptible T0_3 T0 p5 susceptible T8_1 T8 m3 control T8_2 T8 m3 control T8_3 T8 m3 control T8_1 T8 m3 resilient T8_2 T8 m3 resilient T8_3 T8 m3 resilient T8_1 T8 m3 susceptible T8_2 T8 m3 susceptible T8_3 T8 m3 susceptible T8_1 T8 p5 control T8_2 T8 p5 control T8_3 T8 p5 control T8_1 T8 p5 resilient T8_2 T8 p5 resilient T8_3 T8 p5 resilient T8_1 T8 p5 susceptible T8_2 T8 p5 susceptible T8_3 T8 p5 susceptible T16_1 T16 m3 control T16_2 T16 m3 control T6_3 T16 m3 control T16_1 T16 m3 resilient T16_2 T16 m3 resilient T16_3 T16 m3 resilient T16_1 T16 m3 susceptible T16_2 T16 m3 susceptible T16_3 T16 m3 susceptible T16_1 T16 p5 control T16_2 T16 p5 control T16_3 T16 p5 control T16_1 T16 p5 resilient T16_2 T16 p5 resilient T16_3 T16 p5 resilient T16_1 T16 p5 susceptible T16_2 T16 p5 susceptible T16_3 T16 p5 susceptible T24_1 T24 m3 control T24_2 T24 m3 control T24_3 T24 m3 control T24_1 T24 m3 resilient T24_2 T24 m3 resilient T24_3 T24 m3 resilient T24_1 T24 m3 susceptible T24_2 T24 m3 susceptible T24_3 T24 m3 susceptible T24_1 T24 p5 control T24_2 T24 p5 control T24_3 T24 p5 control T24_1 T24 p5 resilient T24_2 T24 p5 resilient T24_3 T24 p5 resilient T24_1 T24 p5 susceptible T24_2 T24 p5 susceptible T24_3 T24 p5 susceptible
I want to see the effect of condition on genotype over 4 timepoints. So for this, I made the following full model:
readcounts <- read.csv("data-2022-10-20_2.csv", row.names = 1) metadata <- read.csv("metadata_1.csv", row.names = 1) dds <- DESeqDataSetFromMatrix(countData = readcounts, colData = metadata, design = ~ time + genotype + condition + time:condition)
And reduced model with likelihood ratio test:
dds_reduced <- DESeq(dds, test="LRT", reduced = ~ time + genotype + condition)
My first question is my model correct?
Next, when I run the following script to see the results:
I get the following combinations:
 "Intercept" "time_T16_vs_T8" "time_T24_vs_T8"
 "time_T0_vs_T8" "genotype_p5_vs_m3" "condition_resilient_vs_control"
 "condition_susceptible._vs_control" "timeT16.conditonresilient" "timeT24.conditionresilient"
 "timeT0.conditonresilient" "timeT16.conditonsusceptible." "timeT24.conditionsusceptible."
So, my second question is why do I get only these selected 12 combination? What about other possible combinations... let's say "time_T24_vs_T0", "time_T24_vs_T16", "timeT0.conditioncontrol" and "timeT8.conditionresilient" that are not present there? and so on....
I want to clarify if my model or script is not working properly here or that's how time-series multifactorial design works? Any logic behind having these selected combinations?
Many thanks in advance!