Question: DE analysis of multiple time points and conditions
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gravatar for scheitelt
7 months ago by
scheitelt0
scheitelt0 wrote:

Hi,

we have a data set consisting of 48 samples in total. we have 4 time points (0,1,2,3), a control group (which has triplicates for each of the four time points) as well as three different conditions.

metadata <-
structure(list(condtion = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("KO1", "KO2", 
"KO3", "WT"), class = "factor"), timepoint = structure(c(1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 
4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("0h", 
"1h", "2h", "3h"), class = "factor"), replicate = c(1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)), class = "data.frame", row.names = c("sample1", 
"sample2", "sample3", "sample4", "sample5", "sample6", "sample7", 
"sample8", "sample9", "sample10", "sample11", "sample12", "sample13", 
"sample14", "sample15", "sample16", "sample17", "sample18", "sample19", 
"sample20", "sample21", "sample22", "sample23", "sample24", "sample25", 
"sample26", "sample27", "sample28", "sample29", "sample30", "sample31", 
"sample32", "sample33", "sample34", "sample35", "sample36", "sample37", 
"sample38", "sample39", "sample40", "sample41", "sample42", "sample43", 
"sample44", "sample45", "sample46", "sample47", "sample48"))

To analyze the data I have followed the user manual and created this design matrix

Group <- factor(paste(metadata$condtion,metadata$timepoint,sep="."))
design <- model.matrix(~0+Group)
colnames(design) <- levels(Group)

Now i would like to implore several comparisons. For that I need to create a contrast matrix

with this contrast matrix:

my.contrasts <- makeContrasts(
   KO1vsWT.0h = KO1.0h-WT.0h,
   KO1vsWT.1h = (KO1.1h-KO1.0h)-(WT.1h-WT.0h),
   KO1vsWT.2h = (KO1.2h-KO1.0h)-(WT.2h-WT.0h),
   KO1vsWT.3h = (KO1.3h-KO1.0h)-(WT.3h-WT.0h),
   levels=design)

Q1. Can I, with this design matrix, identify genes differentially expressed in each time-point between the KO and the control (this simple pair-wise comparison would be done for each of the three KOs separately)?

But I would also like to identify genes changed over time, first within each condition and second between the KOs and the control. Here I would probably need a more complex (nested?) design. Here I'm struggling with the design/contrast matrix

Again, following the examples in the manual, I would like to use this design matrix

 metadata$condtion <- relevel(metadata$condtion, ref="WT")
 design2 <- model.matrix(~condtion * timepoint, data=metadata)
 colnames(design2)
 [1] "(Intercept)"             "condtionKO1"            
 [3] "condtionKO2"             "condtionKO3"            
 [5] "timepoint1h"             "timepoint2h"            
 [7] "timepoint3h"             "condtionKO1:timepoint1h"
 [9] "condtionKO2:timepoint1h" "condtionKO3:timepoint1h"
 [11] "condtionKO1:timepoint2h" "condtionKO2:timepoint2h"
 [13] "condtionKO3:timepoint2h" "condtionKO1:timepoint3h"
 [15] "condtionKO2:timepoint3h" "condtionKO3:timepoint3h"

Q2. How Can I now identify genes with a changed expression over time with a specific condition (e.g. only within the KO1 or KO2) over all timepoints?

Does edger can calculate this kind if behavior?

Q3. Is there also a way to compare the behavior over time between KO1 and the control?

  qlf <- glmQLFTest(fit, coef=c(8,11,14))

Can this combination identify genes changed over all time points with a differential behavior between KO1and WT?

thanks a lot for any suggestions or corrections

ADD COMMENTlink written 7 months ago by scheitelt0
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