Question

Setting up the design for paired analysis

0

Entering edit mode

candida.vaz ▴ 50

@candidavaz-6923

Last seen 5.9 years ago

Singapore

Dear Bioconductor support team,

I have samples from two time points namely V1 and V4. The samples belong to two categories: Treatment and Placebo as follows:

005v1 Placebo 005v4 Placebo

008v1 Treatment 008v4 Treatment

The ID number 005 etc represent the same sample at two time points. At V1 time-point there is no treatment given, its just that the samples are assigned to be given a treatment (treatment) or not (placebo). At V4 time-point there would be difference in placebo and treatment sample.

Now, if the question is to identify the changes caused due to the treatment, how should I define the design? Should V1 time-point be taken into consideration or not, given that the treatment intervention was given only after the assignment of samples at V1.

It seems like a paired analysis, where the blocking factor is the IDs of the samples, but I'm not sure of setting up the design. Could someone give suggestions. Thanks in advance!

Regards,

Candida Vaz

edger limma paired design • 1.2k views

ADD COMMENT • link 6.0 years ago • updated 5.9 years ago candida.vaz ▴ 50

score 1 · Answer 1 · 2018-12-03

1

Entering edit mode

James W. MacDonald 67k

@james-w-macdonald-5106

Last seen 2 minutes ago

United States

The edgeR User's Guide has a whole section (3.4) on Additive models and blocking, which should answer most if not all of your questions.

ADD COMMENT • link 6.0 years ago James W. MacDonald 67k

0

Entering edit mode

Thank you James for the suggestion,

ADD REPLY • link 5.9 years ago candida.vaz ▴ 50

0

Entering edit mode

Hi guys, I have some questions about model.matrix design for paired samples. I have checked the edgeR user's guide but I still have doubts.

My data: samples collected longitudinally from 8 individuals treated from day 1 to day 17, DAY 0 is the control (no treatment)

#Model matrix
subject <- factor(c(rep(1:8,3), rep(5:8,6)))
timepoint <- factor(c(rep("DAY0",8), rep("DAY1",8), rep("DAY3",8), rep("DAY5",4), rep("DAY7",4),
                      rep("DAY10",4), rep("DAY12",4), rep("DAY15",4), rep("DAY17",4)))
sampleTable <- data.frame(subject = as.factor(subject),
                          timepoint = as.factor(timepoint))
design <- model.matrix(~0 + subject + timepoint)
contr.matrix <- makeContrasts(
  D17vsD0 = timepointDAY17, #1
  D15vsD0 = timepointDAY15, #2
  D12vsD0 = timepointDAY12, #3
  D10vsD0 = timepointDAY10, #4
  D7vsD0 = timepointDAY7, #5
  D5vsD0 = timepointDAY5, #6
  D3vsD0 = timepointDAY3, #7
  D1vsD0 = timepointDAY1, #8
  levels=design) 

#Count data
x <- counts.cpm.filtered
DGEx <- DGEList(counts=x)
DGEx <- calcNormFactors(DGEx, method = "TMM")

#Stats
v <- voom(DGEx, design, plot=TRUE)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
summary(decideTests(efit))

Q1: I saw many model.matrix for paired samples including or not ~0 on the design. What are the implications of using ~0 (intercept)? How it works?

Q2: As DAY0 is not on colnames of my design (is the intercept), I was not able to specify it on my contrast. The contrast will do comparisons against DAY0?

Q3: I’m not sure that the subject (paired samples) is being considered on the analysis because all subjects show 0 on the contrast.

Q4: I have no technical replicates, It is ok to apply this model?

ADD REPLY • link 4.2 years ago starick.marick • 0