Statistical tests were performed within the R programming language and were two-sided. When multiple samples were being considered at once, the one-way ANOVA test was performed with post-hoc Tukey’s HSD test to examine each pairwise difference as it was designed to handle multiple comparisons. Below are the results of the stats tests and summary of values per figure panel. #Fig 1c > aggregate(x= df_ter$rLUC.fLUC, + # Specify group indicator + by = list(df_ter$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 FRT-35Ster 3.2355949 2 FRT-OCS 0.3635326 3 FRT-OCS-TB 0.4609406 4 FRT-OCStrunc 0.3394271 5 FRT-TB 1.7075450 6 FRT-uORF 2.0520739 7 TMV 2.8030355 > aggregate(x= df_ter$rLUC.fLUC, + # Specify group indicator + by = list(df_ter$Sample.name), + # Specify function (i.e. standard deviation) + FUN = sd) Group.1 x 1 FRT-35Ster 0.51326429 2 FRT-OCS 0.05113160 3 FRT-OCS-TB 0.04658855 4 FRT-OCStrunc 0.05169483 5 FRT-TB 0.26170454 6 FRT-uORF 0.21866330 7 TMV 0.19069786 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_ter) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 6 34.98 5.830 96.36 3.53e-14 *** Residuals 21 1.27 0.061 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_ter) $Sample.name diff lwr upr p adj FRT-OCS-FRT-35Ster -2.87206222 -3.4374997 -2.3066247 0.0000000 FRT-OCS-TB-FRT-35Ster -2.77465421 -3.3400917 -2.2092167 0.0000000 FRT-OCStrunc-FRT-35Ster -2.89616776 -3.4616052 -2.3307303 0.0000000 FRT-TB-FRT-35Ster -1.52804981 -2.0934873 -0.9626123 0.0000003 FRT-uORF-FRT-35Ster -1.18352096 -1.7489584 -0.6180835 0.0000181 TMV-FRT-35Ster -0.43255930 -0.9979968 0.1328782 0.2138053 FRT-OCS-TB-FRT-OCS 0.09740801 -0.4680295 0.6628455 0.9973357 FRT-OCStrunc-FRT-OCS -0.02410554 -0.5895430 0.5413319 0.9999992 FRT-TB-FRT-OCS 1.34401242 0.7785749 1.9094499 0.0000027 FRT-uORF-FRT-OCS 1.68854126 1.1231038 2.2539787 0.0000001 TMV-FRT-OCS 2.43950292 1.8740654 3.0049404 0.0000000 FRT-OCStrunc-FRT-OCS-TB -0.12151355 -0.6869510 0.4439239 0.9912338 FRT-TB-FRT-OCS-TB 1.24660441 0.6811669 1.8120419 0.0000084 FRT-uORF-FRT-OCS-TB 1.59113326 1.0256958 2.1565707 0.0000002 TMV-FRT-OCS-TB 2.34209491 1.7766574 2.9075324 0.0000000 FRT-TB-FRT-OCStrunc 1.36811795 0.8026805 1.9335554 0.0000020 FRT-uORF-FRT-OCStrunc 1.71264680 1.1472093 2.2780843 0.0000000 TMV-FRT-OCStrunc 2.46360845 1.8981710 3.0290459 0.0000000 FRT-uORF-FRT-TB 0.34452885 -0.2209086 0.9099663 0.4543104 TMV-FRT-TB 1.09549050 0.5300530 1.6609280 0.0000543 TMV-FRT-uORF 0.75096165 0.1855242 1.3163991 0.0047917 #Fig 1e > aggregate(x= df_rec_pro3$Rluc.Fluc, + # Specify group indicator + by = list(df_rec_pro3$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 Act2_Flp 2.0629399 2 FRT_OCS 0.5181803 3 NOS_Flp 2.2003789 4 s35S_Flp 2.0380832 5 TCTP_Flp 4.3688314 6 TMV 3.7631733 > df_rec_pro3$on <- dfPRO[as.character(df_rec_pro3$Sample.name)] > aggregate(x= df_rec_pro3$Rluc.Fluc, + # Specify group indicator + by = list(df_rec_pro3$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 Act2_Flp 0.21681913 2 FRT_OCS 0.06873824 3 NOS_Flp 0.32293933 4 s35S_Flp 0.28584083 5 TCTP_Flp 0.81885748 6 TMV 0.71660589 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_rec_pro3) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 50.12 10.023 31.74 2.35e-09 *** Residuals 22 6.95 0.316 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_rec_pro3) $Sample.name diff lwr upr p adj FRT_OCS-Act2_Flp -1.54475959 -2.7825973 -0.3069218 0.0090265 NOS_Flp-Act2_Flp 0.13743897 -1.1003988 1.3752767 0.9992491 s35S_Flp-Act2_Flp -0.02485671 -1.2626945 1.2129810 0.9999998 TCTP_Flp-Act2_Flp 2.30589142 1.2338925 3.3778904 0.0000132 TMV-Act2_Flp 1.70023332 0.4623956 2.9380711 0.0036341 NOS_Flp-FRT_OCS 1.68219855 0.4443608 2.9200363 0.0040419 s35S_Flp-FRT_OCS 1.51990287 0.2820651 2.7577406 0.0104207 TCTP_Flp-FRT_OCS 3.85065101 2.7786521 4.9226499 0.0000000 TMV-FRT_OCS 3.24499291 2.0071552 4.4828306 0.0000006 s35S_Flp-NOS_Flp -0.16229568 -1.4001334 1.0755421 0.9983294 TCTP_Flp-NOS_Flp 2.16845246 1.0964535 3.2404514 0.0000323 TMV-NOS_Flp 1.56279436 0.3249566 2.8006321 0.0081302 TCTP_Flp-s35S_Flp 2.33074814 1.2587492 3.4027471 0.0000112 TMV-s35S_Flp 1.72509004 0.4872523 2.9629278 0.0031380 TMV-TCTP_Flp -0.60565810 -1.6776570 0.4663408 0.5096576 #Fig 2b > aggregate(x= df_Cre_OR$rLUC.fLUC, + # Specify group indicator + by = list(df_Cre_OR$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 OR-OCS_rLUC 0.3648032 2 OR-OCS_rLUC_s35S-Cre 0.2235430 3 OR-OCS_rLUC_TCTP-Flp 2.9159496 4 OR-OCS_rLUC_TCTP-Flp_s35S-Cre 0.3712713 5 TMV_rLUC 2.8695536 > aggregate(x= df_Cre_OR$rLUC.fLUC, + # Specify group indicator + by = list(df_Cre_OR$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 OR-OCS_rLUC 0.16101342 2 OR-OCS_rLUC_s35S-Cre 0.06536871 3 OR-OCS_rLUC_TCTP-Flp 0.71907920 4 OR-OCS_rLUC_TCTP-Flp_s35S-Cre 0.03929988 5 TMV_rLUC 0.29278352 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_Cre_OR) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 4 31.83 7.959 62.71 3.49e-09 *** Residuals 15 1.90 0.127 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_Cre_OR) $Sample.name diff lwr upr p adj OR-OCS_rLUC_s35S-Cre-OR-OCS_rLUC -0.141260194 -0.9191107 0.6365903 0.9788160 OR-OCS_rLUC_TCTP-Flp-OR-OCS_rLUC 2.551146368 1.7732958 3.3289969 0.0000004 OR-OCS_rLUC_TCTP-Flp_s35S-Cre-OR-OCS_rLUC 0.006468155 -0.7713824 0.7843187 0.9999999 TMV_rLUC-OR-OCS_rLUC 2.504750416 1.7268999 3.2826010 0.0000005 OR-OCS_rLUC_TCTP-Flp-OR-OCS_rLUC_s35S-Cre 2.692406561 1.9145560 3.4702571 0.0000002 OR-OCS_rLUC_TCTP-Flp_s35S-Cre-OR-OCS_rLUC_s35S-Cre 0.147728349 -0.6301222 0.9255789 0.9750761 TMV_rLUC-OR-OCS_rLUC_s35S-Cre 2.646010610 1.8681601 3.4238612 0.0000002 OR-OCS_rLUC_TCTP-Flp_s35S-Cre-OR-OCS_rLUC_TCTP-Flp -2.544678213 -3.3225288 -1.7668277 0.0000004 TMV_rLUC-OR-OCS_rLUC_TCTP-Flp -0.046395951 -0.8242465 0.7314546 0.9997104 TMV_rLUC-OR-OCS_rLUC_TCTP-Flp_s35S-Cre 2.498282261 1.7204317 3.2761328 0.0000005 #Fig 2d > aggregate(x= df_B3$rLUC.fLUC, + # Specify group indicator + by = list(df_B3$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 B3RT-OCS 0.8026258 2 B3RT-OCS_TCTP-B3 5.4710433 3 FRT-OCS 0.4984908 4 FRT-OCS_TCTP-Flp 4.5564095 5 TMV 5.0914359 > df_B3$on <- dfB3[as.character(df_B3$Sample.name)] > aggregate(x= df_B3$rLUC.fLUC, + # Specify group indicator + by = list(df_B3$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 B3RT-OCS 0.18122205 2 B3RT-OCS_TCTP-B3 2.39710016 3 FRT-OCS 0.07669691 4 FRT-OCS_TCTP-Flp 0.56486199 5 TMV 0.92245946 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_B3) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 4 94.34 23.585 16.96 1.94e-05 *** Residuals 15 20.86 1.391 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_B3) $Sample.name diff lwr upr p adj B3RT-OCS_TCTP-B3-B3RT-OCS 4.6684175 2.093227 7.243608 0.0004190 FRT-OCS-B3RT-OCS -0.3041350 -2.879326 2.271056 0.9957979 FRT-OCS_TCTP-Flp-B3RT-OCS 3.7537837 1.178593 6.328975 0.0032982 TMV-B3RT-OCS 4.2888102 1.713619 6.864001 0.0009730 FRT-OCS-B3RT-OCS_TCTP-B3 -4.9725525 -7.547743 -2.397362 0.0002170 FRT-OCS_TCTP-Flp-B3RT-OCS_TCTP-B3 -0.9146338 -3.489825 1.660557 0.8055576 TMV-B3RT-OCS_TCTP-B3 -0.3796074 -2.954798 2.195583 0.9902274 FRT-OCS_TCTP-Flp-FRT-OCS 4.0579187 1.482728 6.633110 0.0016410 TMV-FRT-OCS 4.5929452 2.017754 7.168136 0.0004945 TMV-FRT-OCS_TCTP-Flp 0.5350265 -2.040164 3.110217 0.9656231 #Fig 2f > aggregate(x= df_CR$rLUC.fLUC, + # Specify group indicator + by = list(df_CR$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 B3RT-OCS 0.8138221 2 B3RT-OCS_TCTP-B3 6.1963106 3 CR_B3RT_Flp 0.9728441 4 CR_FRT_B3 0.5780683 5 FRT-OCS 0.4268586 6 FRT-OCS_TCTP-Flp 4.2604310 7 TMV 3.2779496 > aggregate(x= df_CR$rLUC.fLUC, + # Specify group indicator + by = list(df_CR$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 B3RT-OCS 0.21463820 2 B3RT-OCS_TCTP-B3 2.25253780 3 CR_B3RT_Flp 0.13804487 4 CR_FRT_B3 0.13865737 5 FRT-OCS 0.06845048 6 FRT-OCS_TCTP-Flp 0.98738962 7 TMV 0.22693942 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_CR) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 6 121.59 20.266 22.92 3.37e-08 *** Residuals 21 18.57 0.884 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_CR) $Sample.name diff lwr upr p adj B3RT-OCS_TCTP-B3-B3RT-OCS 5.3824885 3.2210226 7.5439545 0.0000013 CR_B3RT_Flp-B3RT-OCS 0.1590219 -2.0024440 2.3204879 0.9999801 CR_FRT_B3-B3RT-OCS -0.2357538 -2.3972197 1.9257121 0.9998006 FRT-OCS-B3RT-OCS -0.3869635 -2.5484294 1.7745024 0.9967093 FRT-OCS_TCTP-Flp-B3RT-OCS 3.4466089 1.2851430 5.6080748 0.0006614 TMV-B3RT-OCS 2.4641275 0.3026616 4.6255935 0.0188763 CR_B3RT_Flp-B3RT-OCS_TCTP-B3 -5.2234666 -7.3849325 -3.0620007 0.0000021 CR_FRT_B3-B3RT-OCS_TCTP-B3 -5.6182423 -7.7797082 -3.4567764 0.0000006 FRT-OCS-B3RT-OCS_TCTP-B3 -5.7694521 -7.9309180 -3.6079861 0.0000004 FRT-OCS_TCTP-Flp-B3RT-OCS_TCTP-B3 -1.9358796 -4.0973455 0.2255863 0.0988013 TMV-B3RT-OCS_TCTP-B3 -2.9183610 -5.0798269 -0.7568951 0.0040693 CR_FRT_B3-CR_B3RT_Flp -0.3947757 -2.5562416 1.7666902 0.9963301 FRT-OCS-CR_B3RT_Flp -0.5459855 -2.7074514 1.6154805 0.9799251 FRT-OCS_TCTP-Flp-CR_B3RT_Flp 3.2875870 1.1261211 5.4490529 0.0011424 TMV-CR_B3RT_Flp 2.3051056 0.1436397 4.4665715 0.0317118 FRT-OCS-CR_FRT_B3 -0.1512097 -2.3126757 2.0102562 0.9999853 FRT-OCS_TCTP-Flp-CR_FRT_B3 3.6823627 1.5208968 5.8438286 0.0002955 TMV-CR_FRT_B3 2.6998813 0.5384154 4.8613472 0.0085722 FRT-OCS_TCTP-Flp-FRT-OCS 3.8335724 1.6721065 5.9950384 0.0001771 TMV-FRT-OCS 2.8510911 0.6896251 5.0125570 0.0051234 TMV-FRT-OCS_TCTP-Flp -0.9824814 -3.1439473 1.1789845 0.7542951 #Fig 3b 24h > aggregate(x= df_OR_24$rLUC.fLUC, + # Specify group indicator + by = list(df_OR_24$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 OR_0I 0.5435613 2 OR_1IB 4.2822327 3 OR_1IF 3.0492277 4 OR_2IBF 4.7692661 5 OR_2IFB 3.7607316 6 TMV 2.7486585 > aggregate(x= df_OR_24$rLUC.fLUC, + # Specify group indicator + by = list(df_OR_24$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 OR_0I 0.03074095 2 OR_1IB 0.95805242 3 OR_1IF 0.30521458 4 OR_2IBF 1.23962579 5 OR_2IFB 0.20376245 6 TMV 0.50526139 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_OR_24) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 44.92 8.985 18.95 1.32e-06 *** Residuals 18 8.54 0.474 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_OR_24) $Sample.name diff lwr upr p adj OR_1IB-OR_0I 3.7386714 2.1911267 5.28621604 0.0000057 OR_1IF-OR_0I 2.5056663 0.9581217 4.05321100 0.0008201 OR_2IBF-OR_0I 4.2257048 2.6781601 5.77324945 0.0000010 OR_2IFB-OR_0I 3.2171703 1.6696256 4.76471495 0.0000427 TMV-OR_0I 2.2050972 0.6575525 3.75264186 0.0030145 OR_1IF-OR_1IB -1.2330050 -2.7805497 0.31453962 0.1664190 OR_2IBF-OR_1IB 0.4870334 -1.0605113 2.03457806 0.9119054 OR_2IFB-OR_1IB -0.5215011 -2.0690458 1.02604357 0.8865171 TMV-OR_1IB -1.5335742 -3.0811188 0.01397048 0.0529106 OR_2IBF-OR_1IF 1.7200384 0.1724938 3.26758311 0.0244625 OR_2IFB-OR_1IF 0.7115039 -0.8360407 2.25904861 0.6914301 TMV-OR_1IF -0.3005691 -1.8481138 1.24697552 0.9882818 OR_2IFB-OR_2IBF -1.0085345 -2.5560792 0.53901016 0.3441357 TMV-OR_2IBF -2.0206076 -3.5681522 -0.47306292 0.0067284 TMV-OR_2IFB -1.0120731 -2.5596177 0.53547158 0.3406084 #Fig 3d 24h > aggregate(x= df_AND_24$rLUC.fLUC, + # Specify group indicator + by = list(df_AND_24$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 AND_0I 0.3105064 2 AND_1IB 0.4181476 3 AND_1IF 0.4665180 4 AND_2IBF 1.7044870 5 AND_2IFB 1.5087098 6 TMV 2.7486585 > aggregate(x= df_AND_24$rLUC.fLUC, + # Specify group indicator + by = list(df_AND_24$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 AND_0I 0.03671583 2 AND_1IB 0.06162972 3 AND_1IF 0.07604984 4 AND_2IBF 0.48192038 5 AND_2IFB 0.19325260 6 TMV 0.50526139 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_AND_24) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 18.753 3.751 42 2.63e-09 *** Residuals 18 1.607 0.089 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_AND_24) $Sample.name diff lwr upr p adj AND_1IB-AND_0I 0.10764114 -0.5639030 0.7791853 0.9951302 AND_1IF-AND_0I 0.15601158 -0.5155326 0.8275557 0.9742574 AND_2IBF-AND_0I 1.39398054 0.7224364 2.0655247 0.0000435 AND_2IFB-AND_0I 1.19820335 0.5266592 1.8697475 0.0002766 TMV-AND_0I 2.43815209 1.7666080 3.1096962 0.0000000 AND_1IF-AND_1IB 0.04837044 -0.6231737 0.7199146 0.9998970 AND_2IBF-AND_1IB 1.28633940 0.6147953 1.9578835 0.0001189 AND_2IFB-AND_1IB 1.09056221 0.4190181 1.7621063 0.0007942 TMV-AND_1IB 2.33051095 1.6589668 3.0020551 0.0000000 AND_2IBF-AND_1IF 1.23796896 0.5664248 1.9095131 0.0001885 AND_2IFB-AND_1IF 1.04219177 0.3706476 1.7137359 0.0012840 TMV-AND_1IF 2.28214051 1.6105964 2.9536846 0.0000000 AND_2IFB-AND_2IBF -0.19577720 -0.8673213 0.4757669 0.9344066 TMV-AND_2IBF 1.04417155 0.3726274 1.7157157 0.0012589 TMV-AND_2IFB 1.23994874 0.5684046 1.9114929 0.0001850 #Fig 3b 24h > aggregate(x= df_OR_48$rLUC.fLUC, + # Specify group indicator + by = list(df_OR_48$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 OR_0I 0.5433246 2 OR_1IB 10.2518628 3 OR_1IF 2.8831563 4 OR_2IBF 6.4202426 5 OR_2IFB 4.7614321 6 TMV 3.1513612 > aggregate(x= df_OR_48$rLUC.fLUC, + # Specify group indicator + by = list(df_OR_48$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 OR_0I 0.08986009 2 OR_1IB 2.18039988 3 OR_1IF 0.29282757 4 OR_2IBF 2.14706068 5 OR_2IFB 1.23090023 6 TMV 0.55103083 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_OR_48) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 227.03 45.41 24.16 2.12e-07 *** Residuals 18 33.83 1.88 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_OR_48) $Sample.name diff lwr upr p adj OR_1IB-OR_0I 9.7085381 6.6277852 12.7892910 0.0000001 OR_1IF-OR_0I 2.3398316 -0.7409213 5.4205845 0.2031674 OR_2IBF-OR_0I 5.8769180 2.7961651 8.9576709 0.0001250 OR_2IFB-OR_0I 4.2181075 1.1373546 7.2988604 0.0043881 TMV-OR_0I 2.6080365 -0.4727164 5.6887894 0.1259679 OR_1IF-OR_1IB -7.3687065 -10.4494594 -4.2879536 0.0000066 OR_2IBF-OR_1IB -3.8316202 -6.9123731 -0.7508673 0.0101946 OR_2IFB-OR_1IB -5.4904307 -8.5711836 -2.4096778 0.0002804 TMV-OR_1IB -7.1005016 -10.1812545 -4.0197487 0.0000109 OR_2IBF-OR_1IF 3.5370863 0.4563334 6.6178392 0.0192356 OR_2IFB-OR_1IF 1.8782758 -1.2024771 4.9590287 0.4127254 TMV-OR_1IF 0.2682049 -2.8125480 3.3489578 0.9997388 OR_2IFB-OR_2IBF -1.6588105 -4.7395634 1.4219424 0.5420793 TMV-OR_2IBF -3.2688814 -6.3496343 -0.1881285 0.0339113 TMV-OR_2IFB -1.6100709 -4.6908238 1.4706820 0.5721578 #Fig 3d 48h > aggregate(x= df_AND_48$rLUC.fLUC, + # Specify group indicator + by = list(df_AND_48$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 AND_0I 0.3062737 2 AND_1IB 0.5999841 3 AND_1IF 0.4776859 4 AND_2IBF 4.4024840 5 AND_2IFB 3.3189622 6 TMV 3.1513612 > aggregate(x= df_AND_48$rLUC.fLUC, + # Specify group indicator + by = list(df_AND_48$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 AND_0I 0.03738729 2 AND_1IB 0.09690683 3 AND_1IF 0.05775253 4 AND_2IBF 1.34869177 5 AND_2IFB 0.47375872 6 TMV 0.55103083 > res.aov <- aov(rLUC.fLUC ~ Sample.name, data = df_AND_48) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 63.89 12.778 32.47 2.1e-08 *** Residuals 18 7.08 0.394 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample.name, data = df_AND_48) $Sample.name diff lwr upr p adj AND_1IB-AND_0I 0.2937104 -1.116008 1.7034283 0.9839797 AND_1IF-AND_0I 0.1714122 -1.238306 1.5811301 0.9986804 AND_2IBF-AND_0I 4.0962102 2.686492 5.5059281 0.0000004 AND_2IFB-AND_0I 3.0126885 1.602971 4.4224064 0.0000299 TMV-AND_0I 2.8450875 1.435370 4.2548053 0.0000622 AND_1IF-AND_1IB -0.1222981 -1.532016 1.2874197 0.9997432 AND_2IBF-AND_1IB 3.8024999 2.392782 5.2122178 0.0000012 AND_2IFB-AND_1IB 2.7189781 1.309260 4.1286960 0.0001093 TMV-AND_1IB 2.5513771 1.141659 3.9610950 0.0002343 AND_2IBF-AND_1IF 3.9247980 2.515080 5.3345159 0.0000008 AND_2IFB-AND_1IF 2.8412763 1.431558 4.2509942 0.0000633 TMV-AND_1IF 2.6736752 1.263957 4.0833931 0.0001341 AND_2IFB-AND_2IBF -1.0835217 -2.493240 0.3261961 0.1936364 TMV-AND_2IBF -1.2511228 -2.660841 0.1585951 0.0993161 TMV-AND_2IFB -0.1676010 -1.577319 1.2421168 0.9988153 #Fig 4a > aggregate(x= df_NOT_timecourse_norm_FRT_gen1$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_FRT_gen1$Sample_TimePoint), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NOT_FRTv1_0I_24 1.00000000 2 NOT_FRTv1_0I_48 1.00000000 3 NOT_FRTv1_0I_64 1.00000000 4 NOT_FRTv1_1IF_24 0.18312891 5 NOT_FRTv1_1IF_48 0.04507642 6 NOT_FRTv1_1IF_64 0.03588895 > aggregate(x= df_NOT_timecourse_norm_FRT_gen1$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_FRT_gen1$Sample_TimePoint), + # Specify function (SD) + FUN = sd) Group.1 x 1 NOT_FRTv1_0I_24 0.058769845 2 NOT_FRTv1_0I_48 0.598307699 3 NOT_FRTv1_0I_64 0.237154140 4 NOT_FRTv1_1IF_24 0.030694073 5 NOT_FRTv1_1IF_48 0.002550122 6 NOT_FRTv1_1IF_64 0.008031044 > res.aov <- aov(Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_FRT_gen1) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample_TimePoint 5 5.045 1.0089 14.46 9.12e-06 *** Residuals 18 1.256 0.0698 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_FRT_gen1) $Sample_TimePoint diff lwr upr p adj NOT_FRTv1_0I_48-NOT_FRTv1_0I_24 -6.661338e-16 -0.5936217 0.5936217 1.0000000 NOT_FRTv1_0I_64-NOT_FRTv1_0I_24 -2.500005e-10 -0.5936217 0.5936217 1.0000000 NOT_FRTv1_1IF_24-NOT_FRTv1_0I_24 -8.168711e-01 -1.4104928 -0.2232494 0.0041888 NOT_FRTv1_1IF_48-NOT_FRTv1_0I_24 -9.549236e-01 -1.5485453 -0.3613019 0.0008794 NOT_FRTv1_1IF_64-NOT_FRTv1_0I_24 -9.641110e-01 -1.5577328 -0.3704893 0.0007933 NOT_FRTv1_0I_64-NOT_FRTv1_0I_48 -2.499998e-10 -0.5936217 0.5936217 1.0000000 NOT_FRTv1_1IF_24-NOT_FRTv1_0I_48 -8.168711e-01 -1.4104928 -0.2232494 0.0041888 NOT_FRTv1_1IF_48-NOT_FRTv1_0I_48 -9.549236e-01 -1.5485453 -0.3613019 0.0008794 NOT_FRTv1_1IF_64-NOT_FRTv1_0I_48 -9.641110e-01 -1.5577328 -0.3704893 0.0007933 NOT_FRTv1_1IF_24-NOT_FRTv1_0I_64 -8.168711e-01 -1.4104928 -0.2232494 0.0041888 NOT_FRTv1_1IF_48-NOT_FRTv1_0I_64 -9.549236e-01 -1.5485453 -0.3613019 0.0008794 NOT_FRTv1_1IF_64-NOT_FRTv1_0I_64 -9.641110e-01 -1.5577328 -0.3704893 0.0007933 NOT_FRTv1_1IF_48-NOT_FRTv1_1IF_24 -1.380525e-01 -0.7316742 0.4555692 0.9741422 NOT_FRTv1_1IF_64-NOT_FRTv1_1IF_24 -1.472400e-01 -0.7408617 0.4463818 0.9660126 NOT_FRTv1_1IF_64-NOT_FRTv1_1IF_48 -9.187469e-03 -0.6028092 0.5844342 1.0000000 > aggregate(x= df_NOT_timecourse_norm_B3RT_gen1$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_B3RT_gen1$Sample_TimePoint), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NOT_B3RTv1_0I_24 1.0000000 2 NOT_B3RTv1_0I_48 1.0000000 3 NOT_B3RTv1_0I_64 1.0000000 4 NOT_B3RTv1_1IB_24 0.8592696 5 NOT_B3RTv1_1IB_48 0.4399164 6 NOT_B3RTv1_1IB_64 0.2426927 > aggregate(x= df_NOT_timecourse_norm_B3RT_gen1$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_B3RT_gen1$Sample_TimePoint), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 NOT_B3RTv1_0I_24 0.22428712 2 NOT_B3RTv1_0I_48 0.30939893 3 NOT_B3RTv1_0I_64 0.34925791 4 NOT_B3RTv1_1IB_24 0.08856022 5 NOT_B3RTv1_1IB_48 0.09678046 6 NOT_B3RTv1_1IB_64 0.01337292 > res.aov <- aov(Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_B3RT_gen1) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample_TimePoint 5 2.2106 0.4421 9.295 0.000165 *** Residuals 18 0.8562 0.0476 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_B3RT_gen1) $Sample_TimePoint diff lwr upr p adj NOT_B3RTv1_0I_48-NOT_B3RTv1_0I_24 -2.220446e-16 -0.4901131 0.49011311 1.0000000 NOT_B3RTv1_0I_64-NOT_B3RTv1_0I_24 -4.440892e-16 -0.4901131 0.49011311 1.0000000 NOT_B3RTv1_1IB_24-NOT_B3RTv1_0I_24 -1.407304e-01 -0.6308435 0.34938273 0.9382199 NOT_B3RTv1_1IB_48-NOT_B3RTv1_0I_24 -5.600836e-01 -1.0501967 -0.06997052 0.0199262 NOT_B3RTv1_1IB_64-NOT_B3RTv1_0I_24 -7.573073e-01 -1.2474204 -0.26719418 0.0013434 NOT_B3RTv1_0I_64-NOT_B3RTv1_0I_48 -2.220446e-16 -0.4901131 0.49011311 1.0000000 NOT_B3RTv1_1IB_24-NOT_B3RTv1_0I_48 -1.407304e-01 -0.6308435 0.34938273 0.9382199 NOT_B3RTv1_1IB_48-NOT_B3RTv1_0I_48 -5.600836e-01 -1.0501967 -0.06997052 0.0199262 NOT_B3RTv1_1IB_64-NOT_B3RTv1_0I_48 -7.573073e-01 -1.2474204 -0.26719418 0.0013434 NOT_B3RTv1_1IB_24-NOT_B3RTv1_0I_64 -1.407304e-01 -0.6308435 0.34938273 0.9382199 NOT_B3RTv1_1IB_48-NOT_B3RTv1_0I_64 -5.600836e-01 -1.0501967 -0.06997052 0.0199262 NOT_B3RTv1_1IB_64-NOT_B3RTv1_0I_64 -7.573073e-01 -1.2474204 -0.26719418 0.0013434 NOT_B3RTv1_1IB_48-NOT_B3RTv1_1IB_24 -4.193533e-01 -0.9094664 0.07075986 0.1195846 NOT_B3RTv1_1IB_64-NOT_B3RTv1_1IB_24 -6.165769e-01 -1.1066900 -0.12646380 0.0092642 NOT_B3RTv1_1IB_64-NOT_B3RTv1_1IB_48 -1.972237e-01 -0.6873368 0.29288945 0.7923105 > aggregate(x= df_NOT_timecourse_norm_FRT_gen2$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_FRT_gen2$Sample_TimePoint), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NOT_FRTv3_0I_24 1.00000000 2 NOT_FRTv3_0I_48 1.00000000 3 NOT_FRTv3_0I_64 1.00000000 4 NOT_FRTv3_1IF_24 0.35491082 5 NOT_FRTv3_1IF_48 0.18591599 6 NOT_FRTv3_1IF_64 0.07696577 > aggregate(x= df_NOT_timecourse_norm_FRT_gen2$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_FRT_gen2$Sample_TimePoint), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 NOT_FRTv3_0I_24 0.27795511 2 NOT_FRTv3_0I_48 0.17115946 3 NOT_FRTv3_0I_64 0.30149912 4 NOT_FRTv3_1IF_24 0.11050935 5 NOT_FRTv3_1IF_48 0.05898167 6 NOT_FRTv3_1IF_64 0.02155027 > res.aov <- aov(Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_FRT_gen2) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample_TimePoint 5 3.940 0.7880 22.14 4.14e-07 *** Residuals 18 0.641 0.0356 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_FRT_gen2) $Sample_TimePoint diff lwr upr p adj NOT_FRTv3_0I_48-NOT_FRTv3_0I_24 -2.220446e-16 -0.4240145 0.4240145 1.0000000 NOT_FRTv3_0I_64-NOT_FRTv3_0I_24 -2.220446e-16 -0.4240145 0.4240145 1.0000000 NOT_FRTv3_1IF_24-NOT_FRTv3_0I_24 -6.450892e-01 -1.0691037 -0.2210746 0.0015756 NOT_FRTv3_1IF_48-NOT_FRTv3_0I_24 -8.140840e-01 -1.2380986 -0.3900695 0.0001155 NOT_FRTv3_1IF_64-NOT_FRTv3_0I_24 -9.230342e-01 -1.3470488 -0.4990197 0.0000235 NOT_FRTv3_0I_64-NOT_FRTv3_0I_48 0.000000e+00 -0.4240145 0.4240145 1.0000000 NOT_FRTv3_1IF_24-NOT_FRTv3_0I_48 -6.450892e-01 -1.0691037 -0.2210746 0.0015756 NOT_FRTv3_1IF_48-NOT_FRTv3_0I_48 -8.140840e-01 -1.2380986 -0.3900695 0.0001155 NOT_FRTv3_1IF_64-NOT_FRTv3_0I_48 -9.230342e-01 -1.3470488 -0.4990197 0.0000235 NOT_FRTv3_1IF_24-NOT_FRTv3_0I_64 -6.450892e-01 -1.0691037 -0.2210746 0.0015756 NOT_FRTv3_1IF_48-NOT_FRTv3_0I_64 -8.140840e-01 -1.2380986 -0.3900695 0.0001155 NOT_FRTv3_1IF_64-NOT_FRTv3_0I_64 -9.230342e-01 -1.3470488 -0.4990197 0.0000235 NOT_FRTv3_1IF_48-NOT_FRTv3_1IF_24 -1.689948e-01 -0.5930094 0.2550197 0.7985629 NOT_FRTv3_1IF_64-NOT_FRTv3_1IF_24 -2.779450e-01 -0.7019596 0.1460695 0.3382731 NOT_FRTv3_1IF_64-NOT_FRTv3_1IF_48 -1.089502e-01 -0.5329648 0.3150643 0.9606202 > aggregate(x= df_NOT_timecourse_norm_B3RT_gen2$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_B3RT_gen2$Sample_TimePoint), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NOT_B3RTv3_0I_24 1.0000000 2 NOT_B3RTv3_0I_48 1.0000000 3 NOT_B3RTv3_0I_64 1.0000000 4 NOT_B3RTv3_1IB_24 0.2759619 5 NOT_B3RTv3_1IB_48 0.2117143 6 NOT_B3RTv3_1IB_64 0.1525589 > aggregate(x= df_NOT_timecourse_norm_B3RT_gen2$Normalised_repression, + # Specify group indicator + by = list(df_NOT_timecourse_norm_B3RT_gen2$Sample_TimePoint), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 NOT_B3RTv3_0I_24 0.09739333 2 NOT_B3RTv3_0I_48 0.04912504 3 NOT_B3RTv3_0I_64 0.24266978 4 NOT_B3RTv3_1IB_24 0.06053290 5 NOT_B3RTv3_1IB_48 0.03028872 6 NOT_B3RTv3_1IB_64 0.04210617 > res.aov <- aov(Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_B3RT_gen2) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample_TimePoint 5 3.743 0.7486 58.22 1.75e-10 *** Residuals 18 0.231 0.0129 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Normalised_repression ~ Sample_TimePoint, data = df_NOT_timecourse_norm_B3RT_gen2) $Sample_TimePoint diff lwr upr p adj NOT_B3RTv3_0I_48-NOT_B3RTv3_0I_24 -2.220446e-16 -0.2548081 0.2548081 1.0000000 NOT_B3RTv3_0I_64-NOT_B3RTv3_0I_24 0.000000e+00 -0.2548081 0.2548081 1.0000000 NOT_B3RTv3_1IB_24-NOT_B3RTv3_0I_24 -7.240381e-01 -0.9788463 -0.4692300 0.0000006 NOT_B3RTv3_1IB_48-NOT_B3RTv3_0I_24 -7.882857e-01 -1.0430938 -0.5334776 0.0000002 NOT_B3RTv3_1IB_64-NOT_B3RTv3_0I_24 -8.474411e-01 -1.1022493 -0.5926330 0.0000001 NOT_B3RTv3_0I_64-NOT_B3RTv3_0I_48 2.220446e-16 -0.2548081 0.2548081 1.0000000 NOT_B3RTv3_1IB_24-NOT_B3RTv3_0I_48 -7.240381e-01 -0.9788463 -0.4692300 0.0000006 NOT_B3RTv3_1IB_48-NOT_B3RTv3_0I_48 -7.882857e-01 -1.0430938 -0.5334776 0.0000002 NOT_B3RTv3_1IB_64-NOT_B3RTv3_0I_48 -8.474411e-01 -1.1022493 -0.5926330 0.0000001 NOT_B3RTv3_1IB_24-NOT_B3RTv3_0I_64 -7.240381e-01 -0.9788463 -0.4692300 0.0000006 NOT_B3RTv3_1IB_48-NOT_B3RTv3_0I_64 -7.882857e-01 -1.0430938 -0.5334776 0.0000002 NOT_B3RTv3_1IB_64-NOT_B3RTv3_0I_64 -8.474411e-01 -1.1022493 -0.5926330 0.0000001 NOT_B3RTv3_1IB_48-NOT_B3RTv3_1IB_24 -6.424757e-02 -0.3190557 0.1905606 0.9635958 NOT_B3RTv3_1IB_64-NOT_B3RTv3_1IB_24 -1.234030e-01 -0.3782111 0.1314051 0.6453226 NOT_B3RTv3_1IB_64-NOT_B3RTv3_1IB_48 -5.915542e-02 -0.3139635 0.1956527 0.9743338 #Fig 4c > aggregate(x= df_ANIMPLYBv3_64h$Rluc.Fluc, + # Specify group indicator + by = list(df_ANIMPLYBv3_64h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 ANIMPLYB_0I 0.63727160 2 ANIMPLYB_1IB 0.68061548 3 ANIMPLYB_1IF 4.77279609 4 ANIMPLYB_2IBF 0.91634914 5 ANIMPLYB_2IFB 0.57838141 6 GFP 0.01671668 7 TMV 6.23344688 > aggregate(x= df_ANIMPLYBv3_64h$Rluc.Fluc, + # Specify group indicator + by = list(df_ANIMPLYBv3_64h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 ANIMPLYB_0I 0.153574106 2 ANIMPLYB_1IB 0.327991935 3 ANIMPLYB_1IF 0.480381435 4 ANIMPLYB_2IBF 0.417781512 5 ANIMPLYB_2IFB 0.152810020 6 GFP 0.004433397 7 TMV 1.170825711 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_ANIMPLYBv3_64h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 6 145.33 24.222 87.82 8.96e-14 *** Residuals 21 5.79 0.276 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_ANIMPLYBv3_64h) $Sample.name diff lwr upr p adj ANIMPLYB_1IB-ANIMPLYB_0I 0.04334389 -1.1638543 1.2505420 0.9999997 ANIMPLYB_1IF-ANIMPLYB_0I 4.13552449 2.9283263 5.3427226 0.0000000 ANIMPLYB_2IBF-ANIMPLYB_0I 0.27907754 -0.9281206 1.4862757 0.9871887 ANIMPLYB_2IFB-ANIMPLYB_0I -0.05889018 -1.2660883 1.1483080 0.9999983 GFP-ANIMPLYB_0I -0.62055491 -1.8277531 0.5866432 0.6410004 TMV-ANIMPLYB_0I 5.59617528 4.3889771 6.8033734 0.0000000 ANIMPLYB_1IF-ANIMPLYB_1IB 4.09218060 2.8849825 5.2993787 0.0000000 ANIMPLYB_2IBF-ANIMPLYB_1IB 0.23573366 -0.9714645 1.4429318 0.9947302 ANIMPLYB_2IFB-ANIMPLYB_1IB -0.10223407 -1.3094322 1.1049641 0.9999545 GFP-ANIMPLYB_1IB -0.66389880 -1.8710969 0.5432993 0.5696401 TMV-ANIMPLYB_1IB 5.55283140 4.3456333 6.7600295 0.0000000 ANIMPLYB_2IBF-ANIMPLYB_1IF -3.85644695 -5.0636451 -2.6492488 0.0000000 ANIMPLYB_2IFB-ANIMPLYB_1IF -4.19441467 -5.4016128 -2.9872165 0.0000000 GFP-ANIMPLYB_1IF -4.75607940 -5.9632775 -3.5488813 0.0000000 TMV-ANIMPLYB_1IF 1.46065079 0.2534526 2.6678489 0.0114050 ANIMPLYB_2IFB-ANIMPLYB_2IBF -0.33796772 -1.5451659 0.8692304 0.9667983 GFP-ANIMPLYB_2IBF -0.89963245 -2.1068306 0.3075657 0.2379507 TMV-ANIMPLYB_2IBF 5.31709774 4.1098996 6.5242959 0.0000000 GFP-ANIMPLYB_2IFB -0.56166473 -1.7688629 0.6455334 0.7347542 TMV-ANIMPLYB_2IFB 5.65506547 4.4478673 6.8622636 0.0000000 TMV-GFP 6.21673019 5.0095320 7.4239283 0.0000000 #Fig 5a #24h > aggregate(x= df_split_AND$Rluc.Fluc, + # Specify group indicator + by = list(df_split_AND$Sample.Name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 FRT-OCS 0.3335133 2 FRT-OCS_Flp 2.8253080 3 L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS 0.3615049 4 L2_1IF2_FRT-OCS_Rluc_C1_FlpF2 0.3723251 5 L2_2I_FRT-OCS_Rluc_Flp_C1 2.3513828 6 TMV 2.6660871 > aggregate(x= df_split_AND$Rluc.Fluc, + # Specify group indicator + by = list(df_split_AND$Sample.Name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 FRT-OCS 0.02812007 2 FRT-OCS_Flp 0.42685716 3 L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS 0.04412469 4 L2_1IF2_FRT-OCS_Rluc_C1_FlpF2 0.03139749 5 L2_2I_FRT-OCS_Rluc_Flp_C1 0.58090628 6 TMV 0.16608582 > res.aov <- aov(Rluc.Fluc ~ Sample.Name, data = df_split_AND) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.Name 5 35.62 7.124 94.64 4.03e-14 *** Residuals 22 1.66 0.075 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.Name, data = df_split_AND) $Sample.Name diff lwr upr p adj FRT-OCS_Flp-FRT-OCS 2.49179474 1.9684112 3.0151783 0.0000000 L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS-FRT-OCS 0.02799159 -0.4953920 0.5513751 0.9999793 L2_1IF2_FRT-OCS_Rluc_C1_FlpF2-FRT-OCS 0.03881179 -0.4845718 0.5621953 0.9998957 L2_2I_FRT-OCS_Rluc_Flp_C1-FRT-OCS 2.01786953 1.4944860 2.5412531 0.0000000 TMV-FRT-OCS 2.33257383 1.8091903 2.8559574 0.0000000 L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS-FRT-OCS_Flp -2.46380315 -3.0681544 -1.8594519 0.0000000 L2_1IF2_FRT-OCS_Rluc_C1_FlpF2-FRT-OCS_Flp -2.45298295 -3.0573342 -1.8486317 0.0000000 L2_2I_FRT-OCS_Rluc_Flp_C1-FRT-OCS_Flp -0.47392521 -1.0782765 0.1304261 0.1848708 TMV-FRT-OCS_Flp -0.15922090 -0.7635722 0.4451304 0.9604950 L2_1IF2_FRT-OCS_Rluc_C1_FlpF2-L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS 0.01082020 -0.5935311 0.6151715 0.9999999 L2_2I_FRT-OCS_Rluc_Flp_C1-L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS 1.98987794 1.3855267 2.5942292 0.0000000 TMV-L2_1IF1_FRT-OCS_Rluc_FlpF1_C1-NLS 2.30458225 1.7002310 2.9089335 0.0000000 L2_2I_FRT-OCS_Rluc_Flp_C1-L2_1IF2_FRT-OCS_Rluc_C1_FlpF2 1.97905774 1.3747065 2.5834090 0.0000000 TMV-L2_1IF2_FRT-OCS_Rluc_C1_FlpF2 2.29376205 1.6894108 2.8981133 0.0000000 TMV-L2_2I_FRT-OCS_Rluc_Flp_C1 0.31470431 -0.2896470 0.9190556 0.5934715 #48h > aggregate(x= df_split_AND48h$rLUC.fLUC, + # Specify group indicator + by = list(df_split_AND48h$Sample), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 0I_AND 0.4785857 2 1I_Flp 3.4099262 3 1IF1_AND 0.5716657 4 1IF2_AND 0.8489757 5 2I_AND 2.2048853 6 TMV 3.2109288 > aggregate(x= df_split_AND48h$rLUC.fLUC, + # Specify group indicator + by = list(df_split_AND48h$Sample), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 0I_AND 0.1935878 2 1I_Flp 0.2655178 3 1IF1_AND 0.1271437 4 1IF2_AND 0.2062039 5 2I_AND 0.1735129 6 TMV 0.2294776 > res.aov <- aov(rLUC.fLUC ~ Sample, data = df_split_AND48h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample 5 35.62 7.124 171.4 1.59e-14 *** Residuals 18 0.75 0.042 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = rLUC.fLUC ~ Sample, data = df_split_AND48h) $Sample diff lwr upr p adj 1I_Flp-0I_AND 2.93134055 2.47315559 3.3895255 0.0000000 1IF1_AND-0I_AND 0.09308001 -0.36510495 0.5512650 0.9856765 1IF2_AND-0I_AND 0.37039000 -0.08779496 0.8285750 0.1561150 2I_AND-0I_AND 1.72629960 1.26811464 2.1844846 0.0000000 TMV-0I_AND 2.73234312 2.27415815 3.1905281 0.0000000 1IF1_AND-1I_Flp -2.83826054 -3.29644550 -2.3800756 0.0000000 1IF2_AND-1I_Flp -2.56095054 -3.01913550 -2.1027656 0.0000000 2I_AND-1I_Flp -1.20504095 -1.66322591 -0.7468560 0.0000017 TMV-1I_Flp -0.19899743 -0.65718239 0.2591875 0.7377159 1IF2_AND-1IF1_AND 0.27730999 -0.18087497 0.7354950 0.4203714 2I_AND-1IF1_AND 1.63321959 1.17503463 2.0914045 0.0000000 TMV-1IF1_AND 2.63926310 2.18107814 3.0974481 0.0000000 2I_AND-1IF2_AND 1.35590959 0.89772463 1.8140946 0.0000003 TMV-1IF2_AND 2.36195311 1.90376815 2.8201381 0.0000000 TMV-2I_AND 1.00604352 0.54785856 1.4642285 0.0000209 #Fig 5b #24h > aggregate(x= df_split_NAND_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_split_NAND_24h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NAND_0I 5.718811 2 NAND_1IF1 6.561710 3 NAND_1IF2 7.216087 4 NAND_2I 1.555042 5 NOT_FRT_1IF 1.346751 6 TMV 3.747853 > aggregate(x= df_split_NAND_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_split_NAND_24h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 NAND_0I 0.5340730 2 NAND_1IF1 1.9347196 3 NAND_1IF2 1.4079003 4 NAND_2I 0.3974237 5 NOT_FRT_1IF 0.2535412 6 TMV 0.5145354 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_split_NAND_24h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 128.69 25.739 23.77 2.41e-07 *** Residuals 18 19.49 1.083 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_split_NAND_24h) $Sample.name diff lwr upr p adj NAND_1IF1-NAND_0I 0.8428991 -1.49562731 3.1814255 0.8559249 NAND_1IF2-NAND_0I 1.4972764 -0.84124996 3.8358028 0.3621044 NAND_2I-NAND_0I -4.1637694 -6.50229578 -1.8252430 0.0002834 NOT_FRT_1IF-NAND_0I -4.3720601 -6.71058645 -2.0335337 0.0001594 TMV-NAND_0I -1.9709585 -4.30948494 0.3675679 0.1286829 NAND_1IF2-NAND_1IF1 0.6543774 -1.68414904 2.9929038 0.9442506 NAND_2I-NAND_1IF1 -5.0066685 -7.34519487 -2.6681421 0.0000292 NOT_FRT_1IF-NAND_1IF1 -5.2149591 -7.55348554 -2.8764327 0.0000170 TMV-NAND_1IF1 -2.8138576 -5.15238402 -0.4753312 0.0133535 NAND_2I-NAND_1IF2 -5.6610458 -7.99957222 -3.3225194 0.0000056 NOT_FRT_1IF-NAND_1IF2 -5.8693365 -8.20786289 -3.5308101 0.0000034 TMV-NAND_1IF2 -3.4682350 -5.80676138 -1.1297086 0.0020377 NOT_FRT_1IF-NAND_2I -0.2082907 -2.54681707 2.1302357 0.9997079 TMV-NAND_2I 2.1928108 -0.14571555 4.5313372 0.0735171 TMV-NOT_FRT_1IF 2.4011015 0.06257512 4.7396279 0.0422252 #48h > aggregate(x= df_split_NAND_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_split_NAND_48h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 NAND_0I 11.880295 2 NAND_1IF1 8.850778 3 NAND_1IF2 13.867575 4 NAND_2I 1.409341 5 NOT_FRT_1IF 0.867293 6 TMV 5.551882 > aggregate(x= df_split_NAND_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_split_NAND_48h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 NAND_0I 3.0205809 2 NAND_1IF1 2.6674847 3 NAND_1IF2 4.9529345 4 NAND_2I 0.1599969 5 NOT_FRT_1IF 0.1050707 6 TMV 1.1093252 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_split_NAND_48h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 5 581.4 116.27 16.6 3.45e-06 *** Residuals 18 126.1 7.01 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_split_NAND_48h) $Sample.name diff lwr upr p adj NAND_1IF1-NAND_0I -3.0295174 -8.9777784 2.918744 0.5975947 NAND_1IF2-NAND_0I 1.9872799 -3.9609812 7.935541 0.8900192 NAND_2I-NAND_0I -10.4709543 -16.4192154 -4.522693 0.0003233 NOT_FRT_1IF-NAND_0I -11.0130021 -16.9612632 -5.064741 0.0001791 TMV-NAND_0I -6.3284130 -12.2766741 -0.380152 0.0332965 NAND_1IF2-NAND_1IF1 5.0167973 -0.9314638 10.965058 0.1282547 NAND_2I-NAND_1IF1 -7.4414370 -13.3896980 -1.493176 0.0097088 NOT_FRT_1IF-NAND_1IF1 -7.9834847 -13.9317458 -2.035224 0.0052644 TMV-NAND_1IF1 -3.2988957 -9.2471567 2.649365 0.5117011 NAND_2I-NAND_1IF2 -12.4582342 -18.4064953 -6.509973 0.0000388 NOT_FRT_1IF-NAND_1IF2 -13.0002820 -18.9485431 -7.052021 0.0000223 TMV-NAND_1IF2 -8.3156929 -14.2639540 -2.367432 0.0036135 NOT_FRT_1IF-NAND_2I -0.5420478 -6.4903088 5.406213 0.9996735 TMV-NAND_2I 4.1425413 -1.8057198 10.090802 0.2792935 TMV-NOT_FRT_1IF 4.6845891 -1.2636720 10.632850 0.1749475 #Supp figs: #Supp Fig 1a > aggregate(x= df_ter$Rluc.Fluc, + # Specify group indicator + by = list(df_ter$Promoter), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 Act2 0.04512171 2 s35S 2.75324693 3 TCTP1 0.04469817 > aggregate(x= df_ter$Rluc.Fluc, + # Specify group indicator + by = list(df_ter$Promoter), + # Specify function (i.e. mean) + FUN = sd) Group.1 x 1 Act2 0.01062924 2 s35S 0.12116843 3 TCTP1 0.00589449 #Supp Fig 1c > aggregate(x= df_rec_pro2$Rluc.Fluc, + # Specify group indicator + by = list(df_rec_pro2$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 Act2_Flp_lacZ 2.0629399 2 FRT_OCS 0.5181803 3 NOS_Flp 2.2003789 4 s35S_Flp_lacZ 2.0380832 5 TCTP_Flp 4.4201139 6 TCTP_Flp_lacZ 4.3175488 7 TMV 3.7631733 > df_rec_pro2$on <- dfPRO[as.character(df_rec_pro2$Sample.name)] > aggregate(x= df_rec_pro2$Rluc.Fluc, + # Specify group indicator + by = list(df_rec_pro2$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 Act2_Flp_lacZ 0.21681913 2 FRT_OCS 0.06873824 3 NOS_Flp 0.32293933 4 s35S_Flp_lacZ 0.28584083 5 TCTP_Flp 0.51878575 6 TCTP_Flp_lacZ 1.13508265 7 TMV 0.71660589 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_rec_pro2) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 6 50.14 8.356 25.34 1.37e-08 *** Residuals 21 6.93 0.330 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_rec_pro2) $Sample.name diff lwr upr p adj FRT_OCS-Act2_Flp_lacZ -1.54475959 -2.8648925 -0.2246266 0.0152094 NOS_Flp-Act2_Flp_lacZ 0.13743897 -1.1826940 1.4575719 0.9998478 s35S_Flp_lacZ-Act2_Flp_lacZ -0.02485671 -1.3449897 1.2952762 1.0000000 TCTP_Flp-Act2_Flp_lacZ 2.35717398 1.0370410 3.6773069 0.0001623 TCTP_Flp_lacZ-Act2_Flp_lacZ 2.25460887 0.9344759 3.5747418 0.0002865 TMV-Act2_Flp_lacZ 1.70023332 0.3801004 3.0203663 0.0064459 NOS_Flp-FRT_OCS 1.68219855 0.3620656 3.0023315 0.0071273 s35S_Flp_lacZ-FRT_OCS 1.51990287 0.1997699 2.8400358 0.0174112 TCTP_Flp-FRT_OCS 3.90193356 2.5818006 5.2220665 0.0000001 TCTP_Flp_lacZ-FRT_OCS 3.79936846 2.4792355 5.1195014 0.0000001 TMV-FRT_OCS 3.24499291 1.9248600 4.5651259 0.0000016 s35S_Flp_lacZ-NOS_Flp -0.16229568 -1.4824286 1.1578373 0.9996021 TCTP_Flp-NOS_Flp 2.21973501 0.8996021 3.5398680 0.0003480 TCTP_Flp_lacZ-NOS_Flp 2.11716990 0.7970370 3.4373028 0.0006179 TMV-NOS_Flp 1.56279436 0.2426614 2.8829273 0.0137825 TCTP_Flp-s35S_Flp_lacZ 2.38203069 1.0618977 3.7021636 0.0001415 TCTP_Flp_lacZ-s35S_Flp_lacZ 2.27946558 0.9593326 3.5995985 0.0002495 TMV-s35S_Flp_lacZ 1.72509004 0.4049571 3.0452230 0.0056105 TCTP_Flp_lacZ-TCTP_Flp -0.10256511 -1.4226981 1.2175678 0.9999726 TMV-TCTP_Flp -0.65694065 -1.9770736 0.6631923 0.6732145 TMV-TCTP_Flp_lacZ -0.55437555 -1.8745085 0.7657574 0.8134598 #Supp Fig 2c > aggregate(x= df_alt_rec24h$Rluc.Fluc, + # Specify group indicator + by = list(df_alt_rec24h$Sample.Name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 FRT-OCS 0.4655211 2 FRT-OCS_Flp 3.3793002 3 SloxM1 0.8706237 4 SloxM1_SCre 0.9541850 5 TMV 3.7771638 6 VloxP 0.3557764 7 VloxP_VCre 1.0359619 8 vox 0.9605644 9 vox_Vika 1.0430007 > df_alt_rec24h$on <- dfalt_rec[as.character(df_alt_rec24h$Sample.Name)] > aggregate(x= df_alt_rec24h$Rluc.Fluc, + # Specify group indicator + by = list(df_alt_rec24h$Sample.Name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 FRT-OCS 0.05193278 2 FRT-OCS_Flp 0.40554204 3 SloxM1 0.25117158 4 SloxM1_SCre 0.20592871 5 TMV 0.29114322 6 VloxP 0.08204524 7 VloxP_VCre 0.17150594 8 vox 0.24287693 9 vox_Vika 0.11333919 > res.aov <- aov(Rluc.Fluc ~ Sample.Name, data = df_alt_rec24h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.Name 8 49.83 6.229 120.5 <2e-16 *** Residuals 27 1.40 0.052 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.Name, data = df_alt_rec24h) $Sample.Name diff lwr upr p adj FRT-OCS_Flp-FRT-OCS 2.913779104 2.37274702 3.45481119 0.0000000 SloxM1-FRT-OCS 0.405102571 -0.13592951 0.94613465 0.2668354 SloxM1_SCre-FRT-OCS 0.488663907 -0.05236818 1.02969599 0.1006707 TMV-FRT-OCS 3.311642691 2.77061061 3.85267477 0.0000000 VloxP-FRT-OCS -0.109744723 -0.65077681 0.43128736 0.9986417 VloxP_VCre-FRT-OCS 0.570440776 0.02940869 1.11147286 0.0329953 vox-FRT-OCS 0.495043279 -0.04598880 1.03607536 0.0927353 vox_Vika-FRT-OCS 0.577479539 0.03644746 1.11851162 0.0298096 SloxM1-FRT-OCS_Flp -2.508676533 -3.04970862 -1.96764445 0.0000000 SloxM1_SCre-FRT-OCS_Flp -2.425115197 -2.96614728 -1.88408311 0.0000000 TMV-FRT-OCS_Flp 0.397863587 -0.14316850 0.93889567 0.2874070 VloxP-FRT-OCS_Flp -3.023523827 -3.56455591 -2.48249174 0.0000000 VloxP_VCre-FRT-OCS_Flp -2.343338328 -2.88437041 -1.80230625 0.0000000 vox-FRT-OCS_Flp -2.418735824 -2.95976791 -1.87770374 0.0000000 vox_Vika-FRT-OCS_Flp -2.336299565 -2.87733165 -1.79526748 0.0000000 SloxM1_SCre-SloxM1 0.083561336 -0.45747075 0.62459342 0.9998133 TMV-SloxM1 2.906540120 2.36550804 3.44757220 0.0000000 VloxP-SloxM1 -0.514847294 -1.05587938 0.02618479 0.0714541 VloxP_VCre-SloxM1 0.165338205 -0.37569388 0.70637029 0.9794219 vox-SloxM1 0.089940708 -0.45109137 0.63097279 0.9996780 vox_Vika-SloxM1 0.172376968 -0.36865511 0.71340905 0.9735690 TMV-SloxM1_SCre 2.822978784 2.28194670 3.36401087 0.0000000 VloxP-SloxM1_SCre -0.598408630 -1.13944071 -0.05737655 0.0219495 VloxP_VCre-SloxM1_SCre 0.081776869 -0.45925521 0.62280895 0.9998411 vox-SloxM1_SCre 0.006379372 -0.53465271 0.54741146 1.0000000 vox_Vika-SloxM1_SCre 0.088815632 -0.45221645 0.62984771 0.9997065 VloxP-TMV -3.421387414 -3.96241950 -2.88035533 0.0000000 VloxP_VCre-TMV -2.741201915 -3.28223400 -2.20016983 0.0000000 vox-TMV -2.816599411 -3.35763149 -2.27556733 0.0000000 vox_Vika-TMV -2.734163151 -3.27519523 -2.19313107 0.0000000 VloxP_VCre-VloxP 0.680185499 0.13915342 1.22121758 0.0063250 vox-VloxP 0.604788002 0.06375592 1.14582008 0.0199710 vox_Vika-VloxP 0.687224262 0.14619218 1.22825634 0.0056667 vox-VloxP_VCre -0.075397496 -0.61642958 0.46563459 0.9999137 vox_Vika-VloxP_VCre 0.007038763 -0.53399332 0.54807085 1.0000000 vox_Vika-vox 0.082436260 -0.45859582 0.62346834 0.9998313 #Supp Fig 4b 24h > aggregate(x= df_NORv1_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_NORv1_24h$Sample.Name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 GFP 0.0181695 2 NOR_0I 4.7069106 3 NOR_1IB 4.5631188 4 NOR_1IF 1.2232041 5 NOR_2IBF 1.7901576 6 NOR_2IFB 1.2392747 7 TMV 3.3524561 > aggregate(x= df_NORv1_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_NORv1_24h$Sample.Name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 GFP 0.00977033 2 NOR_0I 0.61333699 3 NOR_1IB 0.28991604 4 NOR_1IF 0.17203596 5 NOR_2IBF 0.39852106 6 NOR_2IFB 0.23876959 7 TMV 0.29869733 > res.aov <- aov(Rluc.Fluc ~ Sample.Name, data = df_NORv1_24h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.Name 6 78.74 13.123 115.5 5.63e-15 *** Residuals 21 2.38 0.114 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.Name, data = df_NORv1_24h) $Sample.Name diff lwr upr p adj NOR_0I-GFP 4.68874105 3.9140994 5.4633827 0.0000000 NOR_1IB-GFP 4.54494930 3.7703076 5.3195910 0.0000000 NOR_1IF-GFP 1.20503456 0.4303929 1.9796762 0.0008833 NOR_2IBF-GFP 1.77198812 0.9973465 2.5466298 0.0000048 NOR_2IFB-GFP 1.22110521 0.4464636 1.9957469 0.0007571 TMV-GFP 3.33428659 2.5596449 4.1089282 0.0000000 NOR_1IB-NOR_0I -0.14379175 -0.9184334 0.6308499 0.9959923 NOR_1IF-NOR_0I -3.48370649 -4.2583482 -2.7090648 0.0000000 NOR_2IBF-NOR_0I -2.91675293 -3.6913946 -2.1421113 0.0000000 NOR_2IFB-NOR_0I -3.46763584 -4.2422775 -2.6929942 0.0000000 TMV-NOR_0I -1.35445446 -2.1290961 -0.5798128 0.0002127 NOR_1IF-NOR_1IB -3.33991474 -4.1145564 -2.5652731 0.0000000 NOR_2IBF-NOR_1IB -2.77296118 -3.5476028 -1.9983195 0.0000000 NOR_2IFB-NOR_1IB -3.32384409 -4.0984858 -2.5492024 0.0000000 TMV-NOR_1IB -1.21066271 -1.9853044 -0.4360211 0.0008369 NOR_2IBF-NOR_1IF 0.56695356 -0.2076881 1.3415952 0.2553202 NOR_2IFB-NOR_1IF 0.01607065 -0.7585710 0.7907123 1.0000000 TMV-NOR_1IF 2.12925203 1.3546104 2.9038937 0.0000003 NOR_2IFB-NOR_2IBF -0.55088291 -1.3255246 0.2237587 0.2841052 TMV-NOR_2IBF 1.56229847 0.7876568 2.3369401 0.0000309 TMV-NOR_2IFB 2.11318138 1.3385397 2.8878230 0.0000003 #Supp Fig 4b 48h > aggregate(x= df_NORv1_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_NORv1_48h$Sample.Name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 GFP 0.007162647 2 NOR_0I 5.919120607 3 NOR_1IB 2.784579313 4 NOR_1IF 0.568776801 5 NOR_2IBF 0.847529872 6 NOR_2IFB 0.639140519 7 TMV 3.582138858 > aggregate(x= df_NORv1_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_NORv1_48h$Sample.Name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 GFP 0.00379950 2 NOR_0I 1.14760932 3 NOR_1IB 0.45875240 4 NOR_1IF 0.09790292 5 NOR_2IBF 0.22045043 6 NOR_2IFB 0.18695487 7 TMV 0.29985749 > res.aov <- aov(Rluc.Fluc ~ Sample.Name, data = df_NORv1_48h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.Name 6 110.64 18.441 75.46 4.08e-13 *** Residuals 21 5.13 0.244 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.Name, data = df_NORv1_48h) $Sample.Name diff lwr upr p adj NOR_0I-GFP 5.91195796 4.7756691 7.0482468 0.0000000 NOR_1IB-GFP 2.77741667 1.6411278 3.9137055 0.0000017 NOR_1IF-GFP 0.56161415 -0.5746747 1.6979030 0.6797807 NOR_2IBF-GFP 0.84036723 -0.2959216 1.9766561 0.2452061 NOR_2IFB-GFP 0.63197787 -0.5043110 1.7682667 0.5572761 TMV-GFP 3.57497621 2.4386873 4.7112651 0.0000000 NOR_1IB-NOR_0I -3.13454129 -4.2708302 -1.9982524 0.0000002 NOR_1IF-NOR_0I -5.35034381 -6.4866327 -4.2140549 0.0000000 NOR_2IBF-NOR_0I -5.07159074 -6.2078796 -3.9353019 0.0000000 NOR_2IFB-NOR_0I -5.27998009 -6.4162690 -4.1436912 0.0000000 TMV-NOR_0I -2.33698175 -3.4732706 -1.2006929 0.0000234 NOR_1IF-NOR_1IB -2.21580251 -3.3520914 -1.0795136 0.0000496 NOR_2IBF-NOR_1IB -1.93704944 -3.0733383 -0.8007606 0.0002932 NOR_2IFB-NOR_1IB -2.14543879 -3.2817277 -1.0091499 0.0000773 TMV-NOR_1IB 0.79755954 -0.3387293 1.9338484 0.2976207 NOR_2IBF-NOR_1IF 0.27875307 -0.8575358 1.4150419 0.9826651 NOR_2IFB-NOR_1IF 0.07036372 -1.0659251 1.2066526 0.9999928 TMV-NOR_1IF 3.01336206 1.8770732 4.1496509 0.0000005 NOR_2IFB-NOR_2IBF -0.20838935 -1.3446782 0.9278995 0.9962470 TMV-NOR_2IBF 2.73460899 1.5983201 3.8708978 0.0000022 TMV-NOR_2IFB 2.94299834 1.8067095 4.0792872 0.0000007 #Supp Fig 5b > aggregate(x= df_NOT_timecourse_norm_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_24h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 GFP 0.01857155 2 NOT_B3RTv1_0I 3.16139943 3 NOT_B3RTv1_1IB 2.71649449 4 NOT_B3RTv3_0I 5.24221552 5 NOT_B3RTv3_1IB 1.44665150 6 NOT_FRTv1_0I 6.25962631 7 NOT_FRTv1_1IF 1.14631856 8 NOT_FRTv3_0I 5.55030525 9 NOT_FRTv3_1IF 1.96986338 10 TMV 3.07861044 > aggregate(x= df_NOT_timecourse_norm_24h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_24h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 GFP 0.004711424 2 NOT_B3RTv1_0I 0.709061163 3 NOT_B3RTv1_1IB 0.279974243 4 NOT_B3RTv3_0I 0.510556834 5 NOT_B3RTv3_1IB 0.317326488 6 NOT_FRTv1_0I 0.367877268 7 NOT_FRTv1_1IF 0.192133425 8 NOT_FRTv3_0I 1.542735703 9 NOT_FRTv3_1IF 0.613360649 10 TMV 0.520588361 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_24h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 9 152.13 16.904 40.81 2.09e-14 *** Residuals 30 12.43 0.414 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_24h) $Sample.name diff lwr upr p adj NOT_B3RTv1_0I-GFP 3.14282789 1.59044992 4.69520586 0.0000046 NOT_B3RTv1_1IB-GFP 2.69792294 1.14554497 4.25030092 0.0000663 NOT_B3RTv3_0I-GFP 5.22364397 3.67126600 6.77602194 0.0000000 NOT_B3RTv3_1IB-GFP 1.42807996 -0.12429802 2.98045793 0.0917467 NOT_FRTv1_0I-GFP 6.24105476 4.68867679 7.79343273 0.0000000 NOT_FRTv1_1IF-GFP 1.12774701 -0.42463096 2.68012498 0.3193092 NOT_FRTv3_0I-GFP 5.53173370 3.97935573 7.08411167 0.0000000 NOT_FRTv3_1IF-GFP 1.95129183 0.39891386 3.50366980 0.0056956 TMV-GFP 3.06003890 1.50766092 4.61241687 0.0000076 NOT_B3RTv1_1IB-NOT_B3RTv1_0I -0.44490494 -1.99728292 1.10747303 0.9914812 NOT_B3RTv3_0I-NOT_B3RTv1_0I 2.08081608 0.52843811 3.63319405 0.0026832 NOT_B3RTv3_1IB-NOT_B3RTv1_0I -1.71474793 -3.26712590 -0.16236996 0.0213669 NOT_FRTv1_0I-NOT_B3RTv1_0I 3.09822687 1.54584890 4.65060484 0.0000060 NOT_FRTv1_1IF-NOT_B3RTv1_0I -2.01508088 -3.56745885 -0.46270291 0.0039392 NOT_FRTv3_0I-NOT_B3RTv1_0I 2.38890581 0.83652784 3.94128379 0.0004280 NOT_FRTv3_1IF-NOT_B3RTv1_0I -1.19153606 -2.74391403 0.36084191 0.2527493 TMV-NOT_B3RTv1_0I -0.08278899 -1.63516696 1.46958898 1.0000000 NOT_B3RTv3_0I-NOT_B3RTv1_1IB 2.52572102 0.97334305 4.07809900 0.0001875 NOT_B3RTv3_1IB-NOT_B3RTv1_1IB -1.26984299 -2.82222096 0.28253498 0.1850706 NOT_FRTv1_0I-NOT_B3RTv1_1IB 3.54313182 1.99075384 5.09550979 0.0000005 NOT_FRTv1_1IF-NOT_B3RTv1_1IB -1.57017594 -3.12255391 -0.01779796 0.0456830 NOT_FRTv3_0I-NOT_B3RTv1_1IB 2.83381076 1.28143279 4.38618873 0.0000293 NOT_FRTv3_1IF-NOT_B3RTv1_1IB -0.74663111 -2.29900909 0.80574686 0.8179546 TMV-NOT_B3RTv1_1IB 0.36211595 -1.19026202 1.91449392 0.9981285 NOT_B3RTv3_1IB-NOT_B3RTv3_0I -3.79556401 -5.34794198 -2.24318604 0.0000001 NOT_FRTv1_0I-NOT_B3RTv3_0I 1.01741079 -0.53496718 2.56978876 0.4562207 NOT_FRTv1_1IF-NOT_B3RTv3_0I -4.09589696 -5.64827493 -2.54351899 0.0000000 NOT_FRTv3_0I-NOT_B3RTv3_0I 0.30808973 -1.24428824 1.86046771 0.9994718 NOT_FRTv3_1IF-NOT_B3RTv3_0I -3.27235214 -4.82473011 -1.71997417 0.0000022 TMV-NOT_B3RTv3_0I -2.16360507 -3.71598304 -0.61122710 0.0016465 NOT_FRTv1_0I-NOT_B3RTv3_1IB 4.81297480 3.26059683 6.36535278 0.0000000 NOT_FRTv1_1IF-NOT_B3RTv3_1IB -0.30033295 -1.85271092 1.25204502 0.9995695 NOT_FRTv3_0I-NOT_B3RTv3_1IB 4.10365375 2.55127577 5.65603172 0.0000000 NOT_FRTv3_1IF-NOT_B3RTv3_1IB 0.52321187 -1.02916610 2.07558984 0.9745824 TMV-NOT_B3RTv3_1IB 1.63195894 0.07958097 3.18433691 0.0331971 NOT_FRTv1_1IF-NOT_FRTv1_0I -5.11330775 -6.66568572 -3.56092978 0.0000000 NOT_FRTv3_0I-NOT_FRTv1_0I -0.70932106 -2.26169903 0.84305691 0.8569126 NOT_FRTv3_1IF-NOT_FRTv1_0I -4.28976293 -5.84214090 -2.73738496 0.0000000 TMV-NOT_FRTv1_0I -3.18101586 -4.73339384 -1.62863789 0.0000037 NOT_FRTv3_0I-NOT_FRTv1_1IF 4.40398669 2.85160872 5.95636467 0.0000000 NOT_FRTv3_1IF-NOT_FRTv1_1IF 0.82354482 -0.72883315 2.37592279 0.7243648 TMV-NOT_FRTv1_1IF 1.93229189 0.37991392 3.48466986 0.0063514 NOT_FRTv3_1IF-NOT_FRTv3_0I -3.58044187 -5.13281985 -2.02806390 0.0000004 TMV-NOT_FRTv3_0I -2.47169481 -4.02407278 -0.91931683 0.0002598 TMV-NOT_FRTv3_1IF 1.10874707 -0.44363090 2.66112504 0.3410532 #Supp Fig 5c > aggregate(x= df_NOT_timecourse_norm_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_48h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 GFP 0.0202150 2 NOT_B3RTv1_0I 5.0462758 3 NOT_B3RTv1_1IB 2.2199393 4 NOT_B3RTv3_0I 7.8876490 5 NOT_B3RTv3_1IB 1.6699280 6 NOT_FRTv1_0I 13.5853023 7 NOT_FRTv1_1IF 0.6123768 8 NOT_FRTv3_0I 7.4007832 9 NOT_FRTv3_1IF 1.3759239 10 TMV 4.5906654 > aggregate(x= df_NOT_timecourse_norm_48h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_48h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 GFP 0.01440019 2 NOT_B3RTv1_0I 1.56131234 3 NOT_B3RTv1_1IB 0.48838089 4 NOT_B3RTv3_0I 0.38748103 5 NOT_B3RTv3_1IB 0.23890676 6 NOT_FRTv1_0I 8.12819094 7 NOT_FRTv1_1IF 0.03464418 8 NOT_FRTv3_0I 1.26671410 9 NOT_FRTv3_1IF 0.43651052 10 TMV 0.60853330 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_48h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 9 643.4 71.49 9.734 1.13e-06 *** Residuals 29 213.0 7.34 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_48h) $Sample.name diff lwr upr p adj NOT_B3RTv1_0I-GFP 5.0260608 -1.5276198 11.57974146 0.2521017 NOT_B3RTv1_1IB-GFP 2.1997243 -4.3539563 8.75340498 0.9746729 NOT_B3RTv3_0I-GFP 7.8674340 1.3137533 14.42111462 0.0095142 NOT_B3RTv3_1IB-GFP 1.6497129 -4.9039677 8.20339359 0.9965775 NOT_FRTv1_0I-GFP 13.5650873 7.0114066 20.11876790 0.0000035 NOT_FRTv1_1IF-GFP 0.5921618 -5.9615188 7.14584247 0.9999993 NOT_FRTv3_0I-GFP 7.3805682 0.8268876 13.93424888 0.0179488 NOT_FRTv3_1IF-GFP 1.3557089 -5.1979717 7.90938955 0.9992418 TMV-GFP 4.5704504 -2.5083337 11.64923458 0.4738014 NOT_B3RTv1_1IB-NOT_B3RTv1_0I -2.8263365 -9.3800171 3.72734416 0.8911895 NOT_B3RTv3_0I-NOT_B3RTv1_0I 2.8413732 -3.7123075 9.39505381 0.8881779 NOT_B3RTv3_1IB-NOT_B3RTv1_0I -3.3763479 -9.9300285 3.17733278 0.7521278 NOT_FRTv1_0I-NOT_B3RTv1_0I 8.5390264 1.9853458 15.09270709 0.0038576 NOT_FRTv1_1IF-NOT_B3RTv1_0I -4.4338990 -10.9875796 2.11978166 0.4103713 NOT_FRTv3_0I-NOT_B3RTv1_0I 2.3545074 -4.1991732 8.90818807 0.9612269 NOT_FRTv3_1IF-NOT_B3RTv1_0I -3.6703519 -10.2240326 2.88332874 0.6594200 TMV-NOT_B3RTv1_0I -0.4556104 -7.5343945 6.62317377 1.0000000 NOT_B3RTv3_0I-NOT_B3RTv1_1IB 5.6677096 -0.8859710 12.22139029 0.1349054 NOT_B3RTv3_1IB-NOT_B3RTv1_1IB -0.5500114 -7.1036920 6.00366926 0.9999996 NOT_FRTv1_0I-NOT_B3RTv1_1IB 11.3653629 4.8116823 17.91904357 0.0000743 NOT_FRTv1_1IF-NOT_B3RTv1_1IB -1.6075625 -8.1612431 4.94611814 0.9971788 NOT_FRTv3_0I-NOT_B3RTv1_1IB 5.1808439 -1.3728367 11.73452455 0.2186570 NOT_FRTv3_1IF-NOT_B3RTv1_1IB -0.8440154 -7.3976961 5.70966522 0.9999850 TMV-NOT_B3RTv1_1IB 2.3707261 -4.7080580 9.44951025 0.9750298 NOT_B3RTv3_1IB-NOT_B3RTv3_0I -6.2177210 -12.7714017 0.33595962 0.0739512 NOT_FRTv1_0I-NOT_B3RTv3_0I 5.6976533 -0.8560274 12.25133393 0.1307468 NOT_FRTv1_1IF-NOT_B3RTv3_0I -7.2752721 -13.8289528 -0.72159150 0.0205342 NOT_FRTv3_0I-NOT_B3RTv3_0I -0.4868657 -7.0405464 6.06681491 0.9999999 NOT_FRTv3_1IF-NOT_B3RTv3_0I -6.5117251 -13.0654057 0.04195558 0.0525512 TMV-NOT_B3RTv3_0I -3.2969835 -10.3757677 3.78180061 0.8409046 NOT_FRTv1_0I-NOT_B3RTv3_1IB 11.9153743 5.3616937 18.46905495 0.0000344 NOT_FRTv1_1IF-NOT_B3RTv3_1IB -1.0575511 -7.6112318 5.49612953 0.9998992 NOT_FRTv3_0I-NOT_B3RTv3_1IB 5.7308553 -0.8228254 12.28453594 0.1262598 NOT_FRTv3_1IF-NOT_B3RTv3_1IB -0.2940040 -6.8476847 6.25967661 1.0000000 TMV-NOT_B3RTv3_1IB 2.9207375 -4.1580467 9.99952164 0.9138636 NOT_FRTv1_1IF-NOT_FRTv1_0I -12.9729254 -19.5266061 -6.41924478 0.0000079 NOT_FRTv3_0I-NOT_FRTv1_0I -6.1845190 -12.7381997 0.36916163 0.0767966 NOT_FRTv3_1IF-NOT_FRTv1_0I -12.2093783 -18.7630590 -5.65569770 0.0000228 TMV-NOT_FRTv1_0I -8.9946368 -16.0734210 -1.91585267 0.0051413 NOT_FRTv3_0I-NOT_FRTv1_1IF 6.7884064 0.2347258 13.34208706 0.0376864 NOT_FRTv3_1IF-NOT_FRTv1_1IF 0.7635471 -5.7901336 7.31722773 0.9999937 TMV-NOT_FRTv1_1IF 3.9782886 -3.1004955 11.05707276 0.6552123 NOT_FRTv3_1IF-NOT_FRTv3_0I -6.0248593 -12.5785400 0.52882132 0.0918549 TMV-NOT_FRTv3_0I -2.8101178 -9.8889019 4.26866635 0.9303714 TMV-NOT_FRTv3_1IF 3.2147415 -3.8640426 10.29352568 0.8590165 #Supp Fig 5d > aggregate(x= df_NOT_timecourse_norm_64h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_64h$Sample.name), + # Specify function (i.e. mean) + FUN = mean) Group.1 x 1 GFP 0.02832968 2 NOT_B3RTv1_0I 6.68118388 3 NOT_B3RTv1_1IB 1.62147462 4 NOT_B3RTv3_0I 9.64427894 5 NOT_B3RTv3_1IB 1.47132020 6 NOT_FRTv1_0I 14.09717257 7 NOT_FRTv1_1IF 0.50593278 8 NOT_FRTv3_0I 10.34396026 9 NOT_FRTv3_1IF 0.79613089 10 TMV 5.18030917 > aggregate(x= df_NOT_timecourse_norm_64h$Rluc.Fluc, + # Specify group indicator + by = list(df_NOT_timecourse_norm_64h$Sample.name), + # Specify function (i.e. SD) + FUN = sd) Group.1 x 1 GFP 0.01508686 2 NOT_B3RTv1_0I 2.33345634 3 NOT_B3RTv1_1IB 0.08934694 4 NOT_B3RTv3_0I 2.34037506 5 NOT_B3RTv3_1IB 0.40608365 6 NOT_FRTv1_0I 3.34320283 7 NOT_FRTv1_1IF 0.11321501 8 NOT_FRTv3_0I 3.11869489 9 NOT_FRTv3_1IF 0.22291513 10 TMV 1.14140247 > res.aov <- aov(Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_64h) > summary(res.aov) Df Sum Sq Mean Sq F value Pr(>F) Sample.name 9 888.7 98.75 29.6 1.54e-12 *** Residuals 30 100.1 3.34 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > TukeyHSD(res.aov) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Rluc.Fluc ~ Sample.name, data = df_NOT_timecourse_norm_64h) $Sample.name diff lwr upr p adj NOT_B3RTv1_0I-GFP 6.6528542 2.24700662 11.0587018 0.0005603 NOT_B3RTv1_1IB-GFP 1.5931449 -2.81270263 5.9989925 0.9605135 NOT_B3RTv3_0I-GFP 9.6159493 5.21010169 14.0217968 0.0000011 NOT_B3RTv3_1IB-GFP 1.4429905 -2.96285706 5.8488381 0.9788899 NOT_FRTv1_0I-GFP 14.0688429 9.66299532 18.4746905 0.0000000 NOT_FRTv1_1IF-GFP 0.4776031 -3.92824447 4.8834507 0.9999967 NOT_FRTv3_0I-GFP 10.3156306 5.90978300 14.7214781 0.0000003 NOT_FRTv3_1IF-GFP 0.7678012 -3.63804636 5.1736488 0.9998159 TMV-GFP 5.1519795 0.74613191 9.5578271 0.0123049 NOT_B3RTv1_1IB-NOT_B3RTv1_0I -5.0597093 -9.46555682 -0.6538617 0.0147359 NOT_B3RTv3_0I-NOT_B3RTv1_0I 2.9630951 -1.44275251 7.3689426 0.4210371 NOT_B3RTv3_1IB-NOT_B3RTv1_0I -5.2098637 -9.61571125 -0.8040161 0.0109800 NOT_FRTv1_0I-NOT_B3RTv1_0I 7.4159887 3.01014112 11.8218363 0.0001107 NOT_FRTv1_1IF-NOT_B3RTv1_0I -6.1752511 -10.58109867 -1.7694035 0.0015314 NOT_FRTv3_0I-NOT_B3RTv1_0I 3.6627764 -0.74307119 8.0686240 0.1697055 NOT_FRTv3_1IF-NOT_B3RTv1_0I -5.8850530 -10.29090056 -1.4792054 0.0027997 TMV-NOT_B3RTv1_0I -1.5008747 -5.90672228 2.9049729 0.9727868 NOT_B3RTv3_0I-NOT_B3RTv1_1IB 8.0228043 3.61695675 12.4286519 0.0000305 NOT_B3RTv3_1IB-NOT_B3RTv1_1IB -0.1501544 -4.55600200 4.2556931 1.0000000 NOT_FRTv1_0I-NOT_B3RTv1_1IB 12.4756979 8.06985037 16.8815455 0.0000000 NOT_FRTv1_1IF-NOT_B3RTv1_1IB -1.1155418 -5.52138941 3.2903057 0.9965274 NOT_FRTv3_0I-NOT_B3RTv1_1IB 8.7224856 4.31663806 13.1283332 0.0000070 NOT_FRTv3_1IF-NOT_B3RTv1_1IB -0.8253437 -5.23119131 3.5805038 0.9996681 TMV-NOT_B3RTv1_1IB 3.5588345 -0.84701303 7.9646821 0.1975750 NOT_B3RTv3_1IB-NOT_B3RTv3_0I -8.1729587 -12.57880632 -3.7671112 0.0000222 NOT_FRTv1_0I-NOT_B3RTv3_0I 4.4528936 0.04704606 8.8587412 0.0459681 NOT_FRTv1_1IF-NOT_B3RTv3_0I -9.1383462 -13.54419373 -4.7324986 0.0000029 NOT_FRTv3_0I-NOT_B3RTv3_0I 0.6996813 -3.70616626 5.1055289 0.9999147 NOT_FRTv3_1IF-NOT_B3RTv3_0I -8.8481481 -13.25399562 -4.4423005 0.0000054 TMV-NOT_B3RTv3_0I -4.4639698 -8.86981735 -0.0581222 0.0450628 NOT_FRTv1_0I-NOT_B3RTv3_1IB 12.6258524 8.22000480 17.0316999 0.0000000 NOT_FRTv1_1IF-NOT_B3RTv3_1IB -0.9653874 -5.37123499 3.4404602 0.9988456 NOT_FRTv3_0I-NOT_B3RTv3_1IB 8.8726401 4.46679249 13.2784876 0.0000051 NOT_FRTv3_1IF-NOT_B3RTv3_1IB -0.6751893 -5.08103688 3.7306583 0.9999367 TMV-NOT_B3RTv3_1IB 3.7089890 -0.69685860 8.1148365 0.1583450 NOT_FRTv1_1IF-NOT_FRTv1_0I -13.5912398 -17.99708736 -9.1853922 0.0000000 NOT_FRTv3_0I-NOT_FRTv1_0I -3.7532123 -8.15905988 0.6526353 0.1480473 NOT_FRTv3_1IF-NOT_FRTv1_0I -13.3010417 -17.70688925 -8.8951941 0.0000000 TMV-NOT_FRTv1_0I -8.9168634 -13.32271097 -4.5110158 0.0000047 NOT_FRTv3_0I-NOT_FRTv1_1IF 9.8380275 5.43217990 14.2438750 0.0000007 NOT_FRTv3_1IF-NOT_FRTv1_1IF 0.2901981 -4.11564946 4.6960457 1.0000000 TMV-NOT_FRTv1_1IF 4.6743764 0.26852881 9.0802240 0.0306762 NOT_FRTv3_1IF-NOT_FRTv3_0I -9.5478294 -13.95367694 -5.1419818 0.0000013 TMV-NOT_FRTv3_0I -5.1636511 -9.56949866 -0.7578035 0.0120260 TMV-NOT_FRTv3_1IF 4.3841783 -0.02166930 8.7900258 0.0519622