DEG analysis without biological Replication
https://www.researchgate.net/post/DEG_analysis_without_biological_Replication
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Without
replicates, you cannot estimate which genes are differentially
expressed using EdgeR or DESeq2. You can only calculate fold changes
based on normalized read counts (preferentially CPM normalized by TMM
method included in any EdgeR analysis) and apply a stringent fold-change
cut-off to determine which genes are more or less expressed depending
on the condition.
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it might be able to assume certain samples
as biological replicates. I recommend you check your samples' clustering
using a PCA plot (explained in the DESeq2 manual/workflow), this is a
good way of exploring your data. For example, if you have 4 control
samples and 4 treatment samples it might be that all your control
samples make one o group and they together differentiate from your
treatment samples. If this is the case, one could argue that for the
purpose of analysing DEG between treatment and control one could
consider all the control samples as replicates, same would apply for the
treatment samples. However, the relevance of this approach will depend
on the nature of the experimental setup.
