[R-sig-ME] co-occurence of mutations as explanatory and response variable ?

[Caroline Zanchi]

Hello,

I hope I am still in the topic of this mailing list !

A colleague of mine has performed experimental evolution of some 
bacteria populations in the presence of several antimicrobial agents. 
The experiment has been replicated twice (2 blocks containing each 3 
replicate populations per condition). We have resequenced some 
individual colonies of these populations at the end of the experimental 
evolution.

There were 5 genes which were overrepresented as having snps. In some 
clones, there were several of these 5 genes which showed snps. In the 
end, my colleague measured the difference in minimum inhibitory 
concentration reached at the end of the evolution versus what it was at 
the beginning (MIC fold-change). The sample size is not so high in the 
end, which is typical of this kind of experiment, but I would still like 
to be able to analyze whether some combinations of mutations were 
selected in some regimes.

My dataset contains one column per each of these 5 genes with “0” 
meaning no snp, “1” meaning snp. I also added one column which is a 
concatenation of the affected genes, i.e. the mutations combination. 
Some combinations emerge only once of course... I attach my dataset to 
this email, it might be much easier to understand than my description :)

I have performed a clogit regression with the survival package with each 
of the 5 genes in turn as a response variable (presence/absence), and 
the other remaining genes as explanatory variables (presence/absence), 
with the block as a random factor.

I would not be surprised if there was a much better alternative, which 
is why I am asking the opinion of the community. I would really love to 
be able to ask for combination of mutations + regime +(1|block) for 
example.

Ultimately it would be great to know whether the difference in MIC 
between end and beginning is affected by the mutation combination or 
individual mutations !

I thank you in advance for any feedback !

Caroline