Dear Bioconductor community,
I have timecourse RNA-seq data.
Some sample are low quality (high RNA degradation, low coverage on genes), this is why I wanted to use limma-voom with
voomWithQualityWeights
Animals received 2 differents treatments (TreatmentA, TreatmentB).
There are different number of animals in each group (5 or 6).
For each animal,blood were collacted before any treatment (D0) and at different time points after treatment: D1, D5, D28.
I don't have data for all animal at each time point. For example, in treatment A, animal A74 doesn't have data at Day5.
I also have other condition for which i don't have value for animals at Day 0.
I want to determine :
1/ Which genes respond at either the Day1, Day5 or day28 in the different group
2/ Which genes respond differently over time in the different groups
Here is my analysis :
Blocked_Design_model<-model.matrix(~0+Group,data=Design_clean_simple) dge <- DGEList(counts=Count) keep <- filterByExpr(dge, Blocked_Design_model) dge <- dge[keep,,keep.lib.sizes=FALSE] dge.norm <- calcNormFactors(dge) v.wts.bl<- voomWithQualityWeights(dge.norm, Blocked_Design_model, plot=TRUE) corfit.wts.bl <- duplicateCorrelation(v.wts.bl,Blocked_Design_model,block=Design_clean_simple$Animal) v.wts.bl.corfit <- voomWithQualityWeights(dge.norm, Blocked_Design_model, plot=TRUE, correlation=corfit.wts.bl$consensus) fit.wts.bl.corfit <- lmFit(v.wts.bl.corfit, Blocked_Design_model,block=Design_clean_simple$Animal ,correlation=corfit.wts.bl$consensus)
contrast.matrix.blocked<-makeContrasts(GpA.D1=GroupTreatmentA.1-GroupTreatment.0, GpA.D5=GroupTreatment.5-GroupTreatment.0, GpA.D27=GroupTreatmentA.27-GroupTreatmentA.0, GpAvsGpB.D1 = (GroupTreatmentA.1-GroupTreatmentA.0)-(GroupTreatmentB.1-GroupTreatmentB.0),GpAvsGpB.D5 = (GroupTreatmentA.5-GroupTreatmentA.0)-(GroupTreatmentB.5-GroupTreatmentB.0),levels=colnames(Blocked_Design_model)) fit2.wts.bl.corfit <- contrasts.fit(fit.wts.bl.corfit, contrast.matrix.blocked) fit2.wts.bl.corfit <- eBayes(fit2.wts.bl.corfit,robust=TRUE)
My question : as each subject doesn't yields a value at each time point, is it relevant to use Block model ?
In general cases, I have data set with missing information at some time point, how to deal with it ? Is my model correct ?
Thanks in advance for you help
Is it a problem to use Limma with blocked model when the dataset contain 2 groups with time points that are different ?
As an example, I have Group A with D0,D3,D7 and D27 and Group B with D0,D1,D3,D5,D7 and D28 ? I would like to identify genes which respond differently over time in the different groups :
Which model would be the best ?
I have to precise that number of animal at each time point in Group B is very variable (animal are dying).
I had to pool 2 experiments so animals are not the same at D1 and D3 but for both I have D0. This is why I think I shouldn't use blocked model on animals ... Any suggestion ?
If you are talking about a different dataset to that presented in the original post, please write a new question.
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