```
Hi all
I have a single cell data experimental design which looks like this:
cluster_metadata
Sample_N Condition Treatment Sample_ID Sample_Name
1 healthy untreated S1 H1
2 healthy treated S1 H2
3 healthy untreated S2 H3
4 healthy treated S2 H4
5 healthy untreated S3 H1
6 healthy treated S3 H2
7 healthy untreated S4 H3
8 healthy treated S4 H4
9 disease untreated S5 DB1
10 disease treated S5 DB2
11 disease untreated S6 DB3
12 disease treated S6 DB1
13 disease untreated S7 DB2
14 disease treated S7 DB3
I have 4 healthy individuals (S1,S2,S3,S4) and 3 individuals with disease (S5,S6,S7) which have been treated or not treated with a drug and I would like to determined the effect of the drug treament. It is a paired experimental design since the same individual has been treated or not treated with the drug. I have run some analysis and obtained a count matrix (cluster_counts) reached to the point where I have to design a formula:
dds <- DESeqDataSetFromMatrix(cluster_counts,
colData = cluster_metadata,
design = ~ sample_Name + Treatment )
However, this formula does not seem correct as I see wrong comparisons in the output.
After running :
dds <- DESeq(dds)
resultsNames(dds)
it gives:
> resultsNames(dds)
[1] "Intercept" "Sample_Name_DB2_vs_DB1" "Sample_Name_DB3_vs_DB1"
[4] "Sample_Name_H1_vs_DB1" "Sample_Name_H2_vs_DB1" "Sample_Name_H3_vs_DB1"
[7] "ISample_Name_H4_vs_DB1" "condition_treated_vs_untreated"
What is the correct formula in this experimental design? Thanks