Understanding DESeq2 contrast

[sarahtanja]

 [1] "Intercept"                                          
 [2] "embryonic_phase_prawnchip_vs_cleavage"              
 [3] "embryonic_phase_earlygastrula_vs_cleavage"           
 [4] "pvc_leachate_level_low_vs_control"                  
 [5] "pvc_leachate_level_mid_vs_control"                   
 [6] "pvc_leachate_level_high_vs_control"                 
 [7] "embryonic_phaseprawnchip.pvc_leachate_levellow"      
 [8] "embryonic_phaseearlygastrula.pvc_leachate_levellow" 
 [9] "embryonic_phaseprawnchip.pvc_leachate_levelmid"      
 [10] "embryonic_phaseearlygastrula.pvc_leachate_levelmid" 
 [11] "embryonic_phaseprawnchip.pvc_leachate_levelhigh"     
 [12] "embryonic_phaseearlygastrula.pvc_leachate_levelhigh"

[sarahtanja]

Contrasts
A contrast is a linear combination of estimated log2 fold changes, which can be used to test if differences between groups are equal to zero. The simplest use case for contrasts is an experimental design containing a factor with three levels, say A, B and C. Contrasts enable the user to generate results for all 3 possible differences: log2 fold change of B vs A, of C vs A, and of C vs B. The contrast argument of results function is used to extract test results of log2 fold changes of interest, for example:

results(dds, contrast=c("condition","C","B"))
Log2 fold changes can also be added and subtracted by providing a list to the contrast argument which has two elements: the names of the log2 fold changes to add, and the names of the log2 fold changes to subtract. The names used in the list should come from resultsNames(dds). Alternatively, a numeric vector of the length of resultsNames(dds) can be provided, for manually specifying the linear combination of terms. A tutorial describing the use of numeric contrasts for DESeq2 explains a general approach to comparing across groups of samples. Demonstrations of the use of contrasts for various designs can be found in the examples section of the help page ?results. The mathematical formula that is used to generate the contrasts can be found below.

[sleidholt]

https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2021.649909/full#S10

Octocoral Experiement
Bulk vs WAF
Four oil treatments

[sarahtanja]

Looking for details & examples of multi-factor contrasts from ?results page:

Multiple results can be returned for analyses beyond a simple two group comparison, so results takes arguments contrast and name to help the user pick out the comparisons of interest for printing a results table. The use of the contrast argument is recommended for exact specification of the levels which should be compared and their order. - from ?results page

## Example 3: two conditions, three genotypes

# ~~~ Using interaction terms ~~~

dds <- makeExampleDESeqDataSet(n=100,m=18)
dds$genotype <- factor(rep(rep(c("I","II","III"),each=3),2))
design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds)
resultsNames(dds)

# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))

# the condition effect for genotype III.
# this is the main effect *plus* the interaction term
# (the extra condition effect in genotype III compared to genotype I).
results(dds, contrast=list( c("condition_B_vs_A","genotypeIII.conditionB") ))
 
# the interaction term for condition effect in genotype III vs genotype I.
# this tests if the condition effect is different in III compared to I
results(dds, name="genotypeIII.conditionB")

# the interaction term for condition effect in genotype III vs genotype II.
# this tests if the condition effect is different in III compared to II
results(dds, contrast=list("genotypeIII.conditionB", "genotypeII.conditionB"))

# Note that a likelihood ratio could be used to test if there are any
# differences in the condition effect between the three genotypes.

# ~~~ Using a grouping variable ~~~

# This is a useful construction when users just want to compare
# specific groups which are combinations of variables.

dds$group <- factor(paste0(dds$genotype, dds$condition))
design(dds) <- ~ group
dds <- DESeq(dds)
resultsNames(dds)

# the condition effect for genotypeIII
results(dds, contrast=c("group", "IIIB", "IIIA"))

[sarahtanja]

Fitting Multifactorial Models of Differential Expression

[sleidholt]

https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15495?casa_token=UUksq7yfeF0AAAAA%3AYjqyh2UNmXP3gXGY3_B__XRcq91OqZCcI-RgmFAPMR9LG5LaDqsw5d79Y-sGCaAvWeSgXGrTjJszgdM0