Dear all,
I'm trying to make a Differential expression analysis with RNAseq data. I have two conditions and, unfortunately, no replicates.
I decided to use DEseq for the analysis but the obtained results don't convince me.
I obtained negative p-value, is this possible???
And then, if I look at the genes with a pval<0.01, they all have a padj equal to 1. Which p-value should I consider??
This is the script I used:
count_table <- read.table("counts_17dpf_21dpf.txt", header=T, sep="\t", row.names=1)
head(count_table)
expt_design <- data.frame(row.names = colnames(count_table), condition = c("17dpf","21dpf"))
expt_design
conditions = expt_design$condition
conditions
library("DESeq")
data <- newCountDataSet(count_table, conditions)
head(counts(data))
data <- estimateSizeFactors(data)
sizeFactors(data)
data2 <- data[,c ("X17dpf_rep1","X21dpf_rep1")]
data2 <- estimateSizeFactors(data2)
data2 <- estimateDispersions(data2, method="blind", sharingMode="fit-only", fitType="local")
results2 <- nbinomTest(data2, "17dpf", "21dpf")
write.table(results2,file="DESeq_results.txt",sep="\t",row.names=rownames(results2),col.names=colnames(results2),quote=F)
Any suggestions are very appreciated!!
Marianna
I'm trying to make a Differential expression analysis with RNAseq data. I have two conditions and, unfortunately, no replicates.
I decided to use DEseq for the analysis but the obtained results don't convince me.
I obtained negative p-value, is this possible???
And then, if I look at the genes with a pval<0.01, they all have a padj equal to 1. Which p-value should I consider??
This is the script I used:
count_table <- read.table("counts_17dpf_21dpf.txt", header=T, sep="\t", row.names=1)
head(count_table)
expt_design <- data.frame(row.names = colnames(count_table), condition = c("17dpf","21dpf"))
expt_design
conditions = expt_design$condition
conditions
library("DESeq")
data <- newCountDataSet(count_table, conditions)
head(counts(data))
data <- estimateSizeFactors(data)
sizeFactors(data)
data2 <- data[,c ("X17dpf_rep1","X21dpf_rep1")]
data2 <- estimateSizeFactors(data2)
data2 <- estimateDispersions(data2, method="blind", sharingMode="fit-only", fitType="local")
results2 <- nbinomTest(data2, "17dpf", "21dpf")
write.table(results2,file="DESeq_results.txt",sep="\t",row.names=rownames(results2),col.names=colnames(results2),quote=F)
Any suggestions are very appreciated!!
Marianna
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