We have a very simple experiment, 1 control (C) and 3 independent treatments (T, B, A). So technically I wrote the following script thinking it would compare C to T, then C to B and finally C to A.

Though at the bottom of my pipeline, it seems that DESeq2 is comparing treatments T vs A. Is there a better way to write the condition so it would compare independentely control to each treatment ? (After that I should cluster genes and output a heatmap)

```
# DESeq1 libraries
library( "DESeq2" )
library("Biobase")
# Heatmap libraries
library( "genefilter" )
library(gplots)

r2 = read.table("matrix.txt", header=TRUE, row.names=1)
head(r2)
samples <- data.frame(row.names=c("C1", "T1", "B1", "A1"), condition=as.factor(c("C1", "T1", "B1", "A1")))
dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples, design=~condition)

# Run DESeq2
dds <- DESeq(dds)

# DESeq2 Results
res <- results(dds)
head (res)

**********

> mcols(res, use.names=TRUE)
DataFrame with 6 rows and 2 columns
                       type                                  description
                <character>                                  <character>
baseMean       intermediate                  the base mean over all rows
log2FoldChange      results log2 fold change (MAP): condition T1 vs A1
lfcSE               results         standard error: condition T1 vs A1
stat                results         Wald statistic: condition T1 vs A1
pvalue              results      Wald test p-value: condition T1 vs A1
padj                results                         BH adjusted p-values
```
This has nothing to do with DESeq2. It can't read your mind that you want C1 to be the base level. You just need to `samples$condition <- relevel(samples$condition, "C1")` before making `dds`.