[BioC] Follow-up: Multiple comparisons using contrast

[Yoong [guest]]

Hi Mike, 
I am writing to follow up the multiple comparison using contrasts. 
To remind you again, here’s my experimental design: 
Genotypes: 4 different genotypes
Timepoint: 3 different timepoints (6h, 12h, and 24h)
Temperature: Low and high temperatures
3 biological replicates for each condition.

In the previous post, you suggested the following design: 
~ genotype + time + temp + genotype:temp + time:temp

And then call 

resultsNames(dds)

In order to see all the interactions which are available for generating tests. For example:

results(dds, name="genotypeA:tempHi")

...will provide you with the results of a test of whether the high temperature vs the low temperature has a specific effect for genotype A, over all time points.

My questions are: 

1.	From your suggestions, to make sure we are on the same page, did you recommend me using all gene counts from all genotypes at all timepoints and temperatures for differential expression step (dds) before calling results (dds) for comparisons of interest? Or, should I just pull out gene counts of genotypes/timepoints/temperature I am interested in for the dds step before calling results (dds) for comparisons of interest? 

What I have done is 
i)	using gene counts from all samples for differential expression step (dds) before calling results(dds); see output#3
ii)	using gene counts from a subset of my samples for differential expression step (dds) before calling results(dds);see output#2

When I tried performing differential expression using gene counts from all samples (at all timepoints and temperatures), I received these warning messages from R (please see output #1 in the box below). 

On a different note, when I tried using only the gene counts of a subset of samples I wanted to compare, DESeq2(version 1.2.6) automatically determined the types of comparisons I could make in the resultsNames(dds). For example,

>resultsNames(dds2)
  "Intercept"  "temp_temp1_vs_temp2" "time_24h_vs_12h" "time_6h_vs_12h" "temp2.time24h"  "temp2.time6h" 

How are all these comparisons pre-determined? When I called results(dds), does it compare effect of  tempHi versus tempLow on genotype A, over all time points? Please see the output #3 for reference. 

Finally, the results of differentially expressed genes for i) and ii) are different. So, I’d like to make sure which step I should be doing and if there is anything wrong with my R-command lines. 

Many thanks, 
Yoong 


 -- output of sessionInfo(): 

Output#1
> dds1= DESeq(dds,betaPrior=FALSE)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
There were 12 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: algorithm did not converge
3: glm.fit: algorithm did not converge
4: glm.fit: algorithm did not converge
5: glm.fit: algorithm did not converge
6: glm.fit: algorithm did not converge
7: glm.fit: algorithm did not converge
8: glm.fit: algorithm did not converge
9: glm.fit: algorithm did not converge
10: glm.fit: algorithm did not converge
11: glm.fit: algorithm did not converge
12: In parametricDispersionFit(mcols(objectNZ)$baseMean[useForFit],  ... :
  dispersion fit did not converge

Output#2:
>dds1 = DESeqDataSetFromMatrix(countData = GenotypeA, colData = colData, design = ~temp+time+time:temp)
>dds2= DESeq(dds1,betaPrior=FALSE)
>resultsNames(dds2)

 "Intercept"  "temp_TempHi_vs_TempLow" "time_24h_vs_12h" "time_6h_vs_12h" "TempLow.time24h"  "TempHi.time6h" 

>results(dds2,name="temp_TempHi_vs_TempLow")

Output#3:

>dds3 = DESeqDataSetFromMatrix(countData = allData, colData = colData, design = ~genotype+time+temp+genotype:temp+ time:temp)
>dds4= DESeq(dds3,betaPrior=FALSE)
WARNINGS


> resultsNames(dds4)
 [1] "Intercept"            "genotypeC_vs_ GenotypeB "  "genotypeA_vs_ GenotypeB"  
 [4] "genotype_GenotypeD_vs_GenotypeB"  "time_24h_vs_12h"        "time_6h_vs_12h"        
 [7] "temp_TempHi_vs_TempLow"      "genotypeC.TempHi" "genotypeA.TempHi" 
[10] "genotypeD.TempHi" "time24h.TempHi"       "time6h.TempHi"    
 
>results(dds4,name="temp_TempHi_vs_TempLow")




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