Hello I'm having issues generating a Manhattan plot in Deseq2 with the following script . Every time I run the script no Manhattan plot is generated despite using the following commands:

# Import count table
countdata <- read.table("family_revised_RNA-seq.counts_fixed.txt", header=TRUE, row.names=1)

# Remove .bam or .sam from filenames
colnames(countdata) <- gsub("\\.[sb]am$", "", colnames(countdata))

# Convert to matrix
countdata <- as.matrix(countdata)
head(countdata)

# Assign condition (affected versus unaffected)
condition <- factor(c("affected","affected","affected","unaffected","unaffected","unaffected"),levels=c("affected","unaffected"))
condition <- relevel(condition, ref = "unaffected")

#libraries needed to load to run DESeq2#
library(S4Vectors)
library(stats4)
library(BiocGenerics)
library(parallel)
library(IRanges)
library(GenomicRanges)
library(GenomeInfoDb)
library(SummarizedExperiment)
library(Biobase)
library(DelayedArray)
library(matrixStats)
library(BiocParallel)

#load DESeq2#
library(DESeq2)

# Create a coldata frame and instantiate the DESeqDataSet
coldata <- data.frame(row.names=colnames(countdata),condition)

dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design = ~ condition)
dds



#pre-filtering to keep only rows that have at least 1 reads total
keep <- rowSums(counts(dds)) > 1
dds <- dds[keep,]

# Run the DESeq
dds <- DESeq(dds)


# Regularized log transformation for clustering/heatmaps
rld <- rlogTransformation(dds)
head(assay(rld))
hist(assay(rld))

# Colors for plots below
library(RColorBrewer)
(mycols <- brewer.pal(8, "Dark2")[1:length(unique(condition))])

# Sample distance heatmap
sampleDists <- as.matrix(dist(t(assay(rld))))
library(gplots)
#png("qc-heatmap_baker.png", w=1000, h=1000, pointsize=20)
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
          col=colorpanel(100, "black", "white"),
          ColSideColors=mycols[condition], RowSideColors=mycols[condition],
          margin=c(10, 10), main="Sample Distance Matrix")
#dev.off()

# Get differential expression results
res <- results(dds)
table(res$padj<0.05)

## Order by adjusted p-value
res <- res[order(res$padj), ]
## Merge with normalized count data
resdata <- merge(as.data.frame(res), as.data.frame(counts(dds, normalized=TRUE)), by="row.names", sort=FALSE)
names(resdata)[1] <- "Gene"
head(resdata)
#get significant results (FDR<0.05)
## Write results
write.csv(resdata, file="sig_diffexpr-results.csv")

## MA plot
DESeq2::plotMA(dds, ylim=c(-28.5,8.5))

Check out:

?plotMA

And see the workflow “rnaseqGene” for a description of this plot.