Hi, I have a dataset like this:
Subject_ID Treatment Duration Pasient_Kontroll Age CTP_NPC NSC-2019-290 K5137 DMSO 6hours Control 47 0.28845794 NSC-2019-291 K5039 DMSO 6hours Control 62 0.06154937 NSC-2019-292 K50128 DMSO 6hours Control 45 0.25351167 NSC-2019-293 U815 DMSO 6hours Patient 32 0.22540896 NSC-2019-294 U793 DMSO 6hours Patient 23 0.25467268 NSC-2019-295 U162 DMSO 6hours Patient 38 0.20031763 NSC-2019-299 K5137 DMSO 1week Control 47 0.21114240 NSC-2019-300 K5039 DMSO 1week Control 62 0.11281822 NSC-2019-301 K50128 DMSO 1week Control 45 0.23429267 NSC-2019-302 U815 DMSO 1week Patient 32 0.20184951 NSC-2019-303 U793 DMSO 1week Patient 23 0.35476381 NSC-2019-304 U162 DMSO 1week Patient 38 0.19938699 NSC-2019-308 K5137 Li 6hours Control 47 0.30317057 NSC-2019-309 K5039 Li 6hours Control 62 0.28853031 NSC-2019-310 K50128 Li 6hours Control 45 0.31602120 NSC-2019-311 U815 Li 6hours Patient 32 0.42000711 NSC-2019-312 U793 Li 6hours Patient 23 0.36429453 NSC-2019-313 U162 Li 6hours Patient 38 0.37220548 NSC-2019-317 K5137 Li 1week Control 47 0.18130258 NSC-2019-318 K5039 Li 1week Control 62 0.12880417 NSC-2019-319 K50128 Li 1week Control 45 0.31397647 NSC-2019-320 U815 Li 1week Patient 32 0.14236717 NSC-2019-321 U793 Li 1week Patient 23 0.28045362 NSC-2019-322 U162 Li 1week Patient 38 0.30689310
I.e., 6 donors: 3 controls and 3 patients. For each donor, we have generated iPSC-derived NPCs and split these into 4 samples before running RNA-seq. One sample treated with DMSO (control treatment) for 6 hours, one sample treated with lithium (Li) for 6 hours, one sample treated with DMSO for 1 week, and one sample treated for lithium for 1 week. In total, 24 samples.
We want to identify the genes that are DE between Li-DMSO at 6 hours and Li-DMSO at 1 week across patient/control status (i.e. different treatment response in patients and controls) while adjusting for age and CPT_NPC effects. What makes this design different from all other posted designs I have seen, is that this one is a nested paired design. I.e., I need to somehow extract the DMSO effect from the treatment effect (since these are separate samples from the same subject) before running the comparison. I am pretty sure DEseq2 cannot do this, but maybe it is possible with limma?
I have seen some authors generate robust Z scores (RZS), which combine the DMSO values and the treatment values into a single score and then run DE analysis. This is mostly done with microarray measurements, but can a similar approach be followed for RNA-seq data and then run limma?