I am working on my portfolio in R, my dataset is Agilent Microarray data and i have issues with the gene ontology analysis. Below are my lines of code.

library(limma)
list.files(path = "C:/Users/User/Documents/Extracted_R")
alzh.port <- list.files(path = "C:/Users/User/Documents/Extracted_R", full.names = T)

adf <- read.maimages(files = alzh.port, source = "agilent.median", green.only = TRUE, other.columns = 'gIsWellAboveBG')

##########
library("GEOquery")
z <- getGEO("GPL16699")
alzhdf <- Table(z)

#Match the Elist to the columns on thegene annotation file alzhdf
adf$genes$Gene.ID <- (alzhdf$GENE_SYMBOL)
adf$genes$RefSeq <- (alzhdf$REFSEQ)


# Correct background and normalize between arrays
adf_bgc<- backgroundCorrect(adf, method = "normexp")
adf_nba <- normalizeBetweenArrays(adf_bgc, method = 'quantile')

#Gene filtering
Control <- adf_nba$genes$ControlType==1L 
NoGene.ID <- is.na(adf_nba$genes$Gene.ID)
IsExpr <- rowSums(adf_nba$other$gIsWellAboveBG > 0) >= 4

adf_nbafilt <- adf_nba[!Control & !NoGene.ID & IsExpr, ]
dim(adf_nbafilt)

#Removing the unwanted columns
adf_nbafilt <- adf_nbafilt$genes[,c("ProbeName","Gene.ID",'RefSeq')]
head(adf_nbafilt)

#Differential Expression
Condition <- read.csv('C:/Users/User/Documents/BIS7017-B_R_Workshops/Portfolio/Input/Condition.csv')
Condition <- factor(c('Control', 'Control', 'Control', 'Control', 'Control', 'Control', 'Control', 'Control', 'Control', 'Control', 
                      'Alzhemier','Alzhemier', 'Alzhemier','Alzhemier', 'Alzhemier', 'Alzhemier', 'Alzhemier', 'Alzhemier','Alzhemier' ),
                    levels = c ('Control', 'Alzhemier'))
table(Condition)
design <-model.matrix(~Condition, data=adf_nbafilt)


fit <- lmFit(data=adf_nbafilt,design)
fit <- eBayes(fit, proportion=0.01, stdev.coef.lim=c(0.1,4),
       trend=FALSE, robust=FALSE, winsor.tail.p=c(0.05,0.1))

TableAlhz <- topTable(fit,coef=1,number=100)

summary(decideTests(fit))


AlhzGO <- goana(fit, coef=1, species="Hs", geneid="Gene.ID")

Your use of coef=1 is incorrect, for reasons explained in the limma User's Guide. If you had simply left coef at the default, then things would have been correct. You don't actually need to set any arguments for eBayes except for fit.