Hello, First, I know that DESeq2 is not meant to be run without replicates and results will not be meaningful. However, I have replicates (n=>30) in one group (N), while another group (T) has only a single sample. This is the requirement of the project. I run the DESeq2 without any error and I found a change in the expression of ~9000 genes (padj < 0.05). I am sharing the script and don't if I am doing the correct or not.

library(DESeq2)
require(data.table)
require(tidyverse)
sampleinfo <- read.delim("design.txt")
raw <- data.table::fread(count.txt)
genes <- pull(raw,ID)
cts <- as.matrix(raw[,-1])
rownames(cts) <- genes
#DESeq2 object
dds <- DESeqDataSetFromMatrix(countData = cts, colData = sampleinfo, design = ~condition)
dds <- estimateSizeFactors(dds)
idx <- rowSums( counts(dds, normalized=TRUE) >= 10 ) >= 3
dds <- dds[idx,]
dds <- DESeq(dds)
res <- results(dds, contrast=c("condition","T","N"))
#convert row names into first column
d <- res
ID <- rownames(d)
rownames(d) <- NULL
data <- cbind(ID,d)
#Drop columns which are not required
data <- subset(data, select = -c(2, 4:7))
write.table(data, file="2.txt", quote = F, sep = "\t", row.names = F)

I would like to know, if possible then from creator Michael Love if I am doing it correctly or not? Thanks in advance.

You can perform this analysis, it should run in DESeq2, but I believe there are also some specialized methods to give a single sample something like an outlier score. I’m not sure the exact package name.