This is a sanity-check question about how I ought to correctly use DESeq2 with knockout experiments. If I have two conditions, a control and a knockout, am I correct in putting them into one data set and then later ignoring the specific result for the knocked-out gene? I would guess that should a normally high-count gene be knocked out, the subsequent scaling up of every other gene is accounted for by the analysis, but that genes previously unseen (zero count) that are then seen in the knockout condition may have their significance mis-estimated (under-estimated?) due to uncertainty about their true frequency when unseen.. but would welcome clarification on this.

If instead I have multiple conditions with different genes knocked out should they all be put together for normalisation/analysis, or should I construct sub-tables with the control and each knockout separately?

How many genes are expressed in control and knock-out, roughly? E.g. how many genes with counts > 10 for samples in these two conditions? The size factor estimation uses the median of ratios over all genes so it is certainly robust to a single gene, and is typically robust to there being many DE genes, as long as the center of the distribution of log ratios is not shifted: e.g. not all genes up-regulated or down-regulated.