hi every one
I have a problem with limma, I annotated microarray data and then I read it by following command in R
GSE23881_Sal <- read.delim("GSE23881_by_gene.txt", header = T, row.names = 1)
> head(GSE23881_Sal)
GSM589082 GSM589083 GSM589084 GSM589085 GSM589086 GSM589087 GSM589088 GSM589089 GSM589090
A1CF 3.8789 3.82150 3.84570 4.05050 3.66320 3.6776 3.99520 3.81040 3.7640
A2ML2 3.9025 3.70645 3.88135 3.94815 3.79510 3.7922 3.89240 3.79505 3.9316
A2ML4 4.8197 4.53920 4.82955 4.74460 4.61515 4.6105 4.79785 4.72325 4.6393
A4GALT 4.8698 4.84260 4.87800 5.30540 5.25380 4.9064 5.05990 4.94090 5.0059
A4GNT 5.5963 5.47640 5.71940 5.51420 5.55970 5.4114 5.78610 5.71030 5.5237
AAAS 9.1472 8.87970 8.95340 9.03555 8.98115 8.9938 8.79250 9.06600 8.9517
GSM589091 GSM589092 GSM589093 GSM589094 GSM589095 GSM589096 GSM589097 GSM589098 GSM589099
A1CF 3.69040 3.72630 3.78830 3.9579 3.71840 3.8525 3.61950 3.89360 3.73620
A2ML2 3.94755 3.69815 3.99395 3.6007 3.84380 4.0757 3.81345 3.96205 3.95620
A2ML4 4.64975 4.59095 4.76360 4.5900 4.79340 4.8722 4.74945 4.79270 4.86235
A4GALT 4.95290 4.89030 4.98270 4.7433 4.74330 5.0175 4.89530 5.03110 5.04090
A4GNT 5.68750 5.72410 5.45030 5.5001 5.51130 5.8292 5.63270 5.56560 5.52350
AAAS 9.11110 9.06480 9.05920 8.9122 9.11045 8.9326 9.07685 9.08885 8.97400
GSM589100 GSM589101
A1CF 3.79720 3.93840
A2ML2 3.93245 4.10320
A2ML4 4.80005 5.04395
A4GALT 5.20450 5.18370
A4GNT 5.86780 5.67800
AAAS 9.03500 9.08680
when I go to find DEGs by following way, I got an error
data <- GSE23881_Sal - rowMeans(GSE23881_Sal)
data$Symbols <- factor(Samples)
design <- model.matrix(~ Symbols + 0, as.data.frame(data))
colnames(design) <- levels(factor(Samples))
fit <- lmFit(as.matrix(GSE23881_Sal), design)
Error in lmFit(as.matrix(GSE23881_Sal), design) :
row dimension of design doesn't match column dimension of data object
while
> dim(GSE23881_Sal)
[1] 13780 20
> dim(design)
[1] 13780 2
Which part of my path is wrong?
Hi, why are you doing this?
data <- GSE23881_Sal - rowMeans(GSE23881_Sal)
What is in Samples?
How did you obtain (retrieve or download) the data?
I downloaded GSE23881 from GEO dataset as CEL files and after reading and normalizing of data, annotation of them was done (adding gene symbols instead of probe names).
Samples defined as below
Samples <- c(rep("Case", 1), rep("Control", 3), rep("Case", 2), rep("Control", 1), rep("Case", 3), rep("Control", 1), rep("Case", 1), rep("Control", 3), rep("Case", 5))
Regarding the first question, I have to say that it was a friend's suggestion to improve the results of the PCA. Isn't it needed?
I see, regarding the line with rowMeans(), this will center your data around 0, which is 'yes' okay for PCA. This is the same as doing scale(x, center = TRUE, scale = FALSE). It is not necessary for differential expression analysis, though.
One problem is that you add your 'Samples' vector to your main expression data, and then later force this to numerical via as.matrix()
data <- GSE23881_Sal - rowMeans(GSE23881_Sal)
metadata <- data.frame(Samples = factor(Samples))
design <- model.matrix(~ 0 + Samples, metadata)
design
colnames(design)
colnames(design) <- levels(factor(Samples))
fit <- lmFit(t(GSE23881_Sal), design)
# then run `eBayes()` and perform your comparisons
Another key thing to check is that the order of the variables in Samples is equal to the order of columns in GSE23881_Sal
Unfortunately, i got same error.
> fit <- lmFit(t(GSE23881_Sal), design)
Error in lmFit(t(GSE23881_Sal), design) :
row dimension of design doesn't match column dimension of data object
> dim(design)
[1] 20 2
> dim(GSE23881_Sal)
[1] 13780 20
in my main question dim of "design" and "GSE23881" was ok but now the situation is different. Anyway, thank you very much for trying to help me.
Please try:
fit <- lmFit(GSE23881_Sal, design)
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