Drug response pre- and post-treatment between timepoints across conditions
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Entering edit mode
@ibrahimakkouh-23133
Last seen 15 months ago

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. I understand that this is a very complex design (including paired samples), and I have not seen anyone do something similar before. What would be the appropriate R package and procedure? Any help is highly appreciated.

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Entering edit mode
@kevin
Last seen 2 hours ago
Naas, Republic of Ireland

I would take a look at DESeq2's vignette. There are also useful examples given at the end of the manual page for results(), accessible via ?results.

In summary, you can do what you want by merging 2 or more of the metadata columns, and creating an interaction. A quick look tells me that you could probably do:

~ Treatment_PasientKontroll + Duration + Treatment_PasientKontroll:Duration + Age + CTP_NPC


Here, Treatment_PasientKontroll is a merged variable.

Kevin

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Entering edit mode

Hi Kevin Blighe,

Your solution does not seem to take into account the paired design, i.e. multiple samples from the same subject. I am pretty sure DEseq2 cannot handle paired designs while accounting for additonal variables. I think limma is more suitable for this kind of analysis, but I am not sure how to proceed.

Best, Ibrahim

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Entering edit mode

Paired designs are usually handled by including the sample pairing as a covariate / blocking factor (Subject_ID). In this case, your design formula is already expansive, but you may want to try that. There are also many previous questions on this topic. If you have a particular / specific question regarding pairing in, e.g., limma, then you could possibly create a new question on that very topic. This current question (above) was somewhat general).

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