Hi, I am performing differential expression (DEG) analysis using DESeq2. I have no replicates, and I understand that without replicates, it is useless to conduct this analysis. However, I have no other option. I have created an R script for cases with no replication. Could you please review it and provide your suggestions?

It will be great helpful for me to complete my work

Load required libraries

library(DESeq2)

Read in the count data

count_data <- read.csv("count.csv", header = TRUE, row.names = 1) head(count_data)

Filter low count genes

keep <- rowSums(count_data >= 10) > 0 count_data_filtered <- count_data[keep, ]

Create metadata

metaData <- data.frame("Condition" = c("COL", "COB")) metaData$Condition = factor(metaData$Condition, levels = c("COL", "COB")) rownames(metaData) = colnames(count_data)

Check metadata

head(metaData) all(rownames(metaData) == colnames(count_data))

Create DESeqDataSet object

dds = DESeqDataSetFromMatrix(countData = count_data_filtered, colData = metaData, design = ~Condition)

Estimate size factors

dds <- estimateSizeFactors(dds)

Set a fixed dispersion value

dispersions(dds) <- 0.1

Run DESeq without dispersion estimation

dds <- nbinomWaldTest(dds)

Get results

res <- results(dds)

Order results by adjusted p-value

resOrdered <- res[order(res$padj), ]

Summary of differential expression

summary(res)