Incorporating factors of unwanted variation from RUVr into EdgeR cell means model for DE
0
0
Entering edit mode
garbuzov • 0
@garbuzov-20174
Last seen 2.1 years ago

Hi there, I'm new to working with EdgeR's generalized linear models, but I have a data set where I'm trying to get complex comparisons between groups, times, and treatments, so here I am...

I've figured out that the best approach for me is to use the cell means model. Right now I have two groups and two treatments (at each time point, but ignore that for now).

Here is my code:

#countsDGE_d3 is my count matrix
>key_3d
                       sample    group condition
F.3dayInjury.IP1 F.3dayInjury forelimb    injury
F.3dayInjury.IP2 F.3dayInjury forelimb    injury 
F.3dayInjury.IP3 F.3dayInjury forelimb    injury
F.Intact.IP1         F.Intact forelimb no_lesion 
F.Intact.IP2         F.Intact forelimb no_lesion 
F.Intact.IP3         F.Intact forelimb no_lesion 
H.3dayInjury.IP1 H.3dayInjury hindlimb    injury 
H.3dayInjury.IP2 H.3dayInjury hindlimb    injury 
H.3dayInjury.IP3 H.3dayInjury hindlimb    injury 
H.Intact.IP1         H.Intact hindlimb no_lesion 
H.Intact.IP2         H.Intact hindlimb no_lesion 
H.Intact.IP3         H.Intact hindlimb no_lesion 
H.Intact.IP4         H.Intact hindlimb no_lesion

design3d <- model.matrix(~0+key_3d$sample)
colnames(design3d) <- levels(key_3d$sample)

countsDGE_d3 <- calcNormFactors(countsDGE_d3)
countsDGE_d3 <- estimateDisp(countsDGE_d3, design3d, robust = TRUE)
countsDGE_d3$common.dispersion

fit <- glmQLFit(countsDGE_d3, design3d)

cont.matrix <- makeContrasts(NvsINJinFL=F.3dayInjury-F.Intact, 
                             NvsINJinHL=H.3dayInjury-H.Intact,
                             Diff=(F.3dayInjury-F.Intact)-(H.3dayInjury-H.Intact),
                             levels=design3d)

qlf <- glmQLFTest(fit, contrast=cont.matrix[,"NvsINJinFL"])
topTags(qlf, n = 20)
table <- topTags(qlf, n = Inf)
write.csv( as.data.frame(table), file="All_FLDay3_InjuredvsIntact_results.csv")

I then repeat running glmQLFTest on all three contrasts and export the topTags table.

My question is, due to the data being pretty noisy, I used the RUVseq package to calculate the factors of unwanted variation for my samples. I would like to incorporate these into my design matrix and into my contrasts. How do I construct this model matrix and contrast matrix?

Does this make sense?

design3d <- model.matrix(~0+key_3d$sample+W_1, data=pData_3d)

I'm not sure what my contrasts should be. I tried:

cont.matrixW1 <- makeContrasts(NvsINJinFL=(F.3dayInjury-F.Intact)-W_1, 
                               NvsINJinHL=(H.3dayInjury-H.Intact)-W_1,
                               Diff=((F.3dayInjury-F.Intact)-(H.3dayInjury-H.Intact))-W_1,
                               levels=design3dW1)

Where pData_3d is a matrix of the factors of unwanted variation from:

seq_set <- newSeqExpressionSet(as.matrix(dge_counts, phenoData = key$sample))

design <- model.matrix(~0 + key$sample)
colnames(design) <- levels(key$sample)
dge <- DGEList(counts = dge_counts, group = key$sample)
dge <- calcNormFactors(dge, method="upperquartile", rubust = TRUE)
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)

fit <- glmFit(dge, design)
res <- residuals(fit, type = "deviance")

#use all the genes to estimate the factors of unwanted variation.
seqUQ <- betweenLaneNormalization(seq_set, which="upper")
controls <- rownames(seq_set)
seqRUVr <- RUVr(seqUQ, controls, k=1, res)

Thank you!

edger RUVseq RUVr GLM RNAseq • 279 views
ADD COMMENT

Login before adding your answer.

Traffic: 483 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6