Interpreting results from DESeq2 time-series multifactorial data analysis
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Entering edit mode
arshad1292 • 0
@a7a0e2fe
Last seen 6 weeks ago
United Arab Emirates

Hello,

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:

resultsNames(dds_reduced)

I get the following combinations:

[1] "Intercept" "time_T16_vs_T8" "time_T24_vs_T8"
[4] "time_T0_vs_T8" "genotype_p5_vs_m3" "condition_resilient_vs_control"
[7] "condition_susceptible._vs_control" "timeT16.conditonresilient" "timeT24.conditionresilient"
[10] "timeT0.conditonresilient" "timeT16.conditonsusceptible." "timeT24.conditionsusceptible."
[13] "timeT0.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!

DESeq2 time-series timecoursedata timecourse multifactorial • 112 views
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Entering edit mode
@mikelove
Last seen 1 day ago
United States

Sorry for the delay in reply, but I don't have sufficient time to answer statistical design questions, I have to restrict myself to software related issues on the support site. I recommend to find a local statistician or someone familiar with linear models in R to work on the statistical design.

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