For the Differential analysis I'm using "limma" R package. I'm using RSEM log2 transformed counts data from XENA browser. A matrix "U" with 20,000 genes as rows and more than 300 samples as columns. Initially I have done some filtering steps like following:
y <- normalizeQuantiles(U)
keep <- rowSums(y > log2(11)) >= 14
table(keep)
keep
FALSE TRUE
3624 16906
y2 <- y[keep,]
table(G$MB)
MB1 MB2
286 80
design2 <- model.matrix(~ G$MB -1)
colnames(design2) <- c("MB1","MB2")
head(design2)
MB1 MB2
1 1 0
2 1 0
3 0 1
4 1 0
5 1 0
6 1 0
fit <- lmFit(y2,design2)
contrast.matrix <- makeContrasts(MB2-MB1, levels=design2)
contrast.matrix
Contrasts
Levels MB2 - MB1
MB1 -1
MB2 1
fitC <- contrasts.fit(fit,contrast.matrix)
fitC <- eBayes(fitC,robust=TRUE,trend=TRUE)
tab <- topTable(fitC,adjust="BH",n=Inf)
summary(decideTests(fitC))
MB2 - MB1
-1 1
0 16904
1 1
Does this mean there are two differentially expressed genes between the "MB2-MB1" comparison?
And when I did like follwoing:
sig.deg = subset(tab, abs(logFC)>0.5 & tab$P.Value < 0.05)
head(sig.deg)
logFC AveExpr t P.Value adj.P.Val B
PRR18 -0.7536122 5.163854 -3.396209 0.0007577785 0.3583424 -0.7996981
DACH2 0.6016372 0.845996 3.375142 0.0008163282 0.3583424 -0.8561809
HELLS 0.5986486 5.800163 3.353323 0.0008813889 0.3583424 -0.9143339
OSBPL6 0.6416259 5.887980 3.226050 0.0013674819 0.412833 -1.2465415
POLQ 0.5932946 5.515293 3.212282 0.0014328446 0.4131078 -1.2817619
MYH1 -0.6999817 1.053430 -3.211477 0.0014367504 0.4131078 -1.2838148
Here the genes are with adj.P.Val > 0.05. I don't understand which are differentially expressed genes. my statistical knowledge is pretty low. Can anyone tell me about this?
y <- normalizeQuantiles(U)
keep <- rowSums(y > log2(11)) >= 14
table(keep)
keep
FALSE TRUE
3624 16906
y2 <- y[keep,]
table(G$MB)
MB1 MB2
286 80
design2 <- model.matrix(~ G$MB -1)
colnames(design2) <- c("MB1","MB2")
head(design2)
MB1 MB2
1 1 0
2 1 0
3 0 1
4 1 0
5 1 0
6 1 0
fit <- lmFit(y2,design2)
contrast.matrix <- makeContrasts(MB2-MB1, levels=design2)
contrast.matrix
Contrasts
Levels MB2 - MB1
MB1 -1
MB2 1
fitC <- contrasts.fit(fit,contrast.matrix)
fitC <- eBayes(fitC,robust=TRUE,trend=TRUE)
tab <- topTable(fitC,adjust="BH",n=Inf)
summary(decideTests(fitC))
MB2 - MB1
-1 1
0 16904
1 1
Does this mean there are two differentially expressed genes between the "MB2-MB1" comparison?
And when I did like follwoing:
sig.deg = subset(tab, abs(logFC)>0.5 & tab$P.Value < 0.05)
head(sig.deg)
logFC AveExpr t P.Value adj.P.Val B
PRR18 -0.7536122 5.163854 -3.396209 0.0007577785 0.3583424 -0.7996981
DACH2 0.6016372 0.845996 3.375142 0.0008163282 0.3583424 -0.8561809
HELLS 0.5986486 5.800163 3.353323 0.0008813889 0.3583424 -0.9143339
OSBPL6 0.6416259 5.887980 3.226050 0.0013674819 0.412833 -1.2465415
POLQ 0.5932946 5.515293 3.212282 0.0014328446 0.4131078 -1.2817619
MYH1 -0.6999817 1.053430 -3.211477 0.0014367504 0.4131078 -1.2838148
Here the genes are with adj.P.Val > 0.05. I don't understand which are differentially expressed genes. my statistical knowledge is pretty low. Can anyone tell me about this?
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