Hello, experts.

I'm here to ask for your kind helps. I'm currently working on DEG analysis. briefly, I want to compare DEG differences between (P07_T01-P07_N01 & P08_T01-P08_N01) vs (P07_T02& P08_T02). This is to compare T01's solely with T02's.

Yet, there are 2 problems.

First, I keep getting an error from y <- estimateGLMCommonDisp(y, design) statement. the error contet is below.

**

Error in dispCoxReid(y, design = design, offset = offset, subset = subset, : no data rows with required number of counts In addition: Warning message: In matrix(x, dim[1], dim[2], byrow = TRUE) : data length exceeds size of matrix

** It's weird cause, the next statement works without any problems. (y <- estimateGLMTrendedDisp(y, design))

The second problem is that I'd like to adjust my glmLRT statement using contrast to make (P07_T01-P07_N01 & P08_T01-P08_N01) vs (P07_T02& P08_T02) and I don't know how to. I'll attach my code below. It would be really nice if you can give me any advice. Big thanks in advance.

> #round up
> trimmed_RNA<-round(RNA_count, 0) 
> trimmed_RNA<-subset(trimmed_RNA)
> final_RNA<-cbind(P07_N01_RNA.genes$V1, trimmed_RNA) 
> colnames(final_RNA) = c("geneID", "p07_N01", "p07_T01","p08_N01", "p08_T01", "p07_T02", "p08_T02")
> 
> #Data setting 
> c_data <- final_RNA[,2:7] 
> rownames(c_data) <- final_RNA[,1] 
> y <- DGEList(counts=c_data, genes=final_RNA[,1], group=matrix ) 
> y <- calcNormFactors(y) 
> plotMDS(y)
> 
> # 2.3 filtering 
> countsPerMillion <- cpm(y) 
> countCheck <- countsPerMillion > 1 
> keep <- which(rowSums(countCheck) >= 10) 
> y <- y[keep,]
> 
> # 2.4 Normalization 
> y <- calcNormFactors(y, method="TMM")
> 
> #creating group factor and Setting up the Model 
> matrix <- factor(c("NM","CC","NM", "CC", "LC", "LC")) 
> group <- c("NM","CC","NM", "CC", "LC", "LC")
> data.frame(sample =colnames(y), matrix)
> status=factor(c("Normal", "Cancer", "Normal", "Cancer", "Cancer", "Cancer")) 
> design <- model.matrix(~group+group:status) 
> 
> matrix <- factor(c("NM","CC","NM", "CC", "LC", "LC"))
> data.frame(sample =colnames(y),matrix) 
> design <-model.matrix(~0+matrix)
> 
> #Setting up the Model  
> rownames(design) = colnames(y)

> #estimating Dispersions 
> y <- estimateGLMCommonDisp(y, design) 
> y <- estimateGLMTrendedDisp(y, design) 
> plotBCV(y)

> #Fitting and Making Comparisons 
> fit <- glmFit(y,design)   
> lrt <- glmLRT(fit, coef=2)
> top4<-topTags(lrt, n=Inf)
> table <- top4$table