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MacrophageObserver

Karolina Maciag

Recently Published

2013-03-30 WT vs IRF3-IRF7 DKO IFNb pre-GCA
use this one
2013-03-30 WT vs IRF3-IRF7 DKO anti-IFNAR pre-GCA
Use this one, NOT 2013-05-03 for same parameter combination
2013-05-03 WT vs IRF3-IRF7 DKO anti-IFNAR pre-GCA
post-stim didn't work well here. Will use 2013-03-30 experiment instead.
2013-03-08 WT vs IFNAR pre-GCA
2013-05-03 WT vs IRF3-IRF7 DKO pre-GCA
2013-08-07 WT vs IRF7 SKO pre-GCA
2013-11-13 WT vs MAVS STING pre-GCA
2013-05-03 WT vs IRF3-IRF7 DKO pre-GCA
2013-08-07 WT vs IRF7 SKO pre-GCA
2013-08-07 WT vs IRF3 SKO pre-GCA
2013-11-13 WT vs MAVS STING pre-GCA
2013-06-20 WT vs MYD88-TRIF pre-GCA
2013-08-07 WT vs IRF7 SKO pre-GCA
2013-08-07 WT vs IRF3 SKO pre-GCA
2013-05-03 WT vs IRF3IRF7 pre-GCA
WT vs IRF3, post-stim, 100U
quad and cubic
WT vs IRF3, post-stim, 10U
quad and cubic
WT vs IRF3, post-stim, 3U
quad and cubic
WT vs IRF3, post-stim, 0U
quad and cubic
WT vs IRF3, prestim, 100U
quad and cubic
WT vs IRF3, prestim, 10U
quad and cubic
WT vs IRF3, prestim, 3U
quad and cubic
WT vs IRF3, prestim, 0U
quad and cubic
IRF3/7 vs WT, post-stim, 100U IFNg
quad and cubic
IRF3/7 vs WT, post-stim, 10U IFNg
quad and cubic
IRF3/7 vs WT, post-stim, 3U IFNg
quad and cubic
IRF3/7 vs WT, post-stim, 0U IFNg
quad and cubic
IRF3/7 vs WT, prestim, 100U IFNg
Quad and cubic
IRF3/7 vs WT, prestim, 10U IFNg
Quad and cubic
IRF3/7 vs WT, prestim, 3U IFNg
Quad and cubic
IRF3/7 vs WT, prestim, 0U IFNg
quad and cubic models
Quadratic model - normalizing to BL
Quadratic model - without normalizing to BL
Linear model - without normalizing to BL
Cubic model - without normalizing to baseline
Cubic model - using (ot1+ot2)*Genotype for m2
Cubic model - using ot1:Genotype + ot2:Genotype for m2
Simplified to only base, 0, 1, 2, 3 For 2: use poly_m.2 <- lmer(log2bact ~ (ot1+ot2+ot3) + Genotype + ot1:Genotype + ot2:Genotype + (1+ot1+ot2+ot3|BiolRepl/TechRep), data=d3, REML = FALSE)
GCA cubic comparisons - maximal permutations, with p-values
Normalize to baseline (uses avg of techreps for each biolrep for this step) Create eight lmer models and compare them All models nest techreps within biolreps Shows summary stats and pvalues
GCA cubic comparisons - maximal permutations
Normalize to baseline (uses avg of techreps for each biolrep for this step) Create eight lmer models and compare them All models nest techreps within biolreps
GCA cubic with nested biol/techreps, normalization to avg baseline over techreps
Uses Lexi's code adapting GCA using cubic models to bacterial luminescence growth curve data Data are normalized to baseline (first timepoint), using average over tech reps given high variability among tech reps at this timept