library(readxl)
ia <- read_xlsx("D:/2.RESEARCH/0. Projects/MP38_Kha/IIA_for_R.xlsx")##a.PvMP38 and gst 4 #Subset of a.PVMP38 and gst 4
attach(ia)
library(magrittr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:magrittr':
##
## extractia.v38 = ia %>% filter(ab_no==1, ab_name=="v38"|ab_name=="gst")
ia.v38 = filter(ia,ab_no==1, ab_name=="v38"|ab_name=="gst")#Anova: a.PvMP38 and gst 4
anova.v38 = aov(iia ~ para_ab_conc, data = ia.v38)
summary(anova.v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 8 4676 584.5 172.8 6.19e-09 ***
## Residuals 9 30 3.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis:a.PvMP38 and gst 4
tukey.anova.v38= TukeyHSD(anova.v38)
options(digits= 4)
tukey.anova.v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v38)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.38_1.33-ko.38_0.66 7.020 -0.2554 14.295 0.0604
## ko.38_2-ko.38_0.66 13.995 6.7196 21.270 0.0006
## ko.38_4-ko.38_0.66 31.325 24.0496 38.600 0.0000
## wt.38_0.66-ko.38_0.66 9.975 2.6996 17.250 0.0074
## wt.38_1.33-ko.38_0.66 18.365 11.0896 25.640 0.0001
## wt.38_2-ko.38_0.66 38.170 30.8946 45.445 0.0000
## wt.38_4-ko.38_0.66 53.795 46.5196 61.070 0.0000
## wt.gst_4-ko.38_0.66 14.095 6.8196 21.370 0.0006
## ko.38_2-ko.38_1.33 6.975 -0.3004 14.250 0.0624
## ko.38_4-ko.38_1.33 24.305 17.0296 31.580 0.0000
## wt.38_0.66-ko.38_1.33 2.955 -4.3204 10.230 0.7822
## wt.38_1.33-ko.38_1.33 11.345 4.0696 18.620 0.0030
## wt.38_2-ko.38_1.33 31.150 23.8746 38.425 0.0000
## wt.38_4-ko.38_1.33 46.775 39.4996 54.050 0.0000
## wt.gst_4-ko.38_1.33 7.075 -0.2004 14.350 0.0580
## ko.38_4-ko.38_2 17.330 10.0546 24.605 0.0001
## wt.38_0.66-ko.38_2 -4.020 -11.2954 3.255 0.4802
## wt.38_1.33-ko.38_2 4.370 -2.9054 11.645 0.3908
## wt.38_2-ko.38_2 24.175 16.8996 31.450 0.0000
## wt.38_4-ko.38_2 39.800 32.5246 47.075 0.0000
## wt.gst_4-ko.38_2 0.100 -7.1754 7.375 1.0000
## wt.38_0.66-ko.38_4 -21.350 -28.6254 -14.075 0.0000
## wt.38_1.33-ko.38_4 -12.960 -20.2354 -5.685 0.0011
## wt.38_2-ko.38_4 6.845 -0.4304 14.120 0.0687
## wt.38_4-ko.38_4 22.470 15.1946 29.745 0.0000
## wt.gst_4-ko.38_4 -17.230 -24.5054 -9.955 0.0001
## wt.38_1.33-wt.38_0.66 8.390 1.1146 15.665 0.0222
## wt.38_2-wt.38_0.66 28.195 20.9196 35.470 0.0000
## wt.38_4-wt.38_0.66 43.820 36.5446 51.095 0.0000
## wt.gst_4-wt.38_0.66 4.120 -3.1554 11.395 0.4536
## wt.38_2-wt.38_1.33 19.805 12.5296 27.080 0.0000
## wt.38_4-wt.38_1.33 35.430 28.1546 42.705 0.0000
## wt.gst_4-wt.38_1.33 -4.270 -11.5454 3.005 0.4152
## wt.38_4-wt.38_2 15.625 8.3496 22.900 0.0003
## wt.gst_4-wt.38_2 -24.075 -31.3504 -16.800 0.0000
## wt.gst_4-wt.38_4 -39.700 -46.9754 -32.425 0.0000#Tukey multiple comparisons of means
#95% family-wise confidence level
#Fit: aov(formula = IIA ~ Conc., data = ia.v38.wt)
#$Conc.##v12 and gst 4 #subset_v12 and gst 4
ia.v12 = ia %>% filter(ab_no==1, ab_name=="v12"|ab_name=="gst")
ia.v12 = filter(ia,ab_no==1, ab_name=="v12"|ab_name=="gst")#Anova_v12 and gst 4
anova.v12 = aov(iia ~ para_ab_conc, data = ia.v12)
summary(anova.v12)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 8 3603 450 237 1.5e-09 ***
## Residuals 9 17 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v12 and gst 4
tukey.anova.v12= TukeyHSD(anova.v12)
options(digits= 4)
tukey.anova.v12## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12_1.33-ko.12_0.66 10.895 5.4382 16.3518 0.0005
## ko.12_2-ko.12_0.66 23.630 18.1732 29.0868 0.0000
## ko.12_4-ko.12_0.66 36.125 30.6682 41.5818 0.0000
## wt.12_0.66-ko.12_0.66 -1.590 -7.0468 3.8668 0.9489
## wt.12_1.33-ko.12_0.66 10.180 4.7232 15.6368 0.0008
## wt.12_2-ko.12_0.66 16.525 11.0682 21.9818 0.0000
## wt.12_4-ko.12_0.66 40.405 34.9482 45.8618 0.0000
## wt.gst_4-ko.12_0.66 5.140 -0.3168 10.5968 0.0683
## ko.12_2-ko.12_1.33 12.735 7.2782 18.1918 0.0001
## ko.12_4-ko.12_1.33 25.230 19.7732 30.6868 0.0000
## wt.12_0.66-ko.12_1.33 -12.485 -17.9418 -7.0282 0.0002
## wt.12_1.33-ko.12_1.33 -0.715 -6.1718 4.7418 0.9997
## wt.12_2-ko.12_1.33 5.630 0.1732 11.0868 0.0422
## wt.12_4-ko.12_1.33 29.510 24.0532 34.9668 0.0000
## wt.gst_4-ko.12_1.33 -5.755 -11.2118 -0.2982 0.0373
## ko.12_4-ko.12_2 12.495 7.0382 17.9518 0.0002
## wt.12_0.66-ko.12_2 -25.220 -30.6768 -19.7632 0.0000
## wt.12_1.33-ko.12_2 -13.450 -18.9068 -7.9932 0.0001
## wt.12_2-ko.12_2 -7.105 -12.5618 -1.6482 0.0104
## wt.12_4-ko.12_2 16.775 11.3182 22.2318 0.0000
## wt.gst_4-ko.12_2 -18.490 -23.9468 -13.0332 0.0000
## wt.12_0.66-ko.12_4 -37.715 -43.1718 -32.2582 0.0000
## wt.12_1.33-ko.12_4 -25.945 -31.4018 -20.4882 0.0000
## wt.12_2-ko.12_4 -19.600 -25.0568 -14.1432 0.0000
## wt.12_4-ko.12_4 4.280 -1.1768 9.7368 0.1583
## wt.gst_4-ko.12_4 -30.985 -36.4418 -25.5282 0.0000
## wt.12_1.33-wt.12_0.66 11.770 6.3132 17.2268 0.0003
## wt.12_2-wt.12_0.66 18.115 12.6582 23.5718 0.0000
## wt.12_4-wt.12_0.66 41.995 36.5382 47.4518 0.0000
## wt.gst_4-wt.12_0.66 6.730 1.2732 12.1868 0.0147
## wt.12_2-wt.12_1.33 6.345 0.8882 11.8018 0.0211
## wt.12_4-wt.12_1.33 30.225 24.7682 35.6818 0.0000
## wt.gst_4-wt.12_1.33 -5.040 -10.4968 0.4168 0.0754
## wt.12_4-wt.12_2 23.880 18.4232 29.3368 0.0000
## wt.gst_4-wt.12_2 -11.385 -16.8418 -5.9282 0.0003
## wt.gst_4-wt.12_4 -35.265 -40.7218 -29.8082 0.0000##v41 and gst 4 #subset_v41 and gst 4
ia.v41 = ia %>% filter(ab_no==1, ab_name=="v41"|ab_name=="gst")
ia.v12 = filter(ia,ab_no==1, ab_name=="v41"|ab_name=="gst")#Anova_v41 and gst 4
anova.v41 = aov(iia ~ para_ab_conc, data = ia.v41)
summary(anova.v41)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 8 6081 760 150 1.2e-08 ***
## Residuals 9 46 5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v41 and gst 4
tukey.anova.v41= TukeyHSD(anova.v41)
options(digits= 4)
tukey.anova.v41## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v41)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.41_1.33-ko.41_0.66 6.760 -2.1581 15.67812 0.1816
## ko.41_2-ko.41_0.66 37.905 28.9869 46.82312 0.0000
## ko.41_4-ko.41_0.66 39.885 30.9669 48.80312 0.0000
## wt.41_0.66-ko.41_0.66 -3.265 -12.1831 5.65312 0.8537
## wt.41_1.33-ko.41_0.66 5.255 -3.6631 14.17312 0.4110
## wt.41_2-ko.41_0.66 31.030 22.1119 39.94812 0.0000
## wt.41_4-ko.41_0.66 45.080 36.1619 53.99812 0.0000
## wt.gst_4-ko.41_0.66 3.870 -5.0481 12.78812 0.7269
## ko.41_2-ko.41_1.33 31.145 22.2269 40.06312 0.0000
## ko.41_4-ko.41_1.33 33.125 24.2069 42.04312 0.0000
## wt.41_0.66-ko.41_1.33 -10.025 -18.9431 -1.10688 0.0259
## wt.41_1.33-ko.41_1.33 -1.505 -10.4231 7.41312 0.9981
## wt.41_2-ko.41_1.33 24.270 15.3519 33.18812 0.0000
## wt.41_4-ko.41_1.33 38.320 29.4019 47.23812 0.0000
## wt.gst_4-ko.41_1.33 -2.890 -11.8081 6.02812 0.9142
## ko.41_4-ko.41_2 1.980 -6.9381 10.89812 0.9889
## wt.41_0.66-ko.41_2 -41.170 -50.0881 -32.25188 0.0000
## wt.41_1.33-ko.41_2 -32.650 -41.5681 -23.73188 0.0000
## wt.41_2-ko.41_2 -6.875 -15.7931 2.04312 0.1698
## wt.41_4-ko.41_2 7.175 -1.7431 16.09312 0.1424
## wt.gst_4-ko.41_2 -34.035 -42.9531 -25.11688 0.0000
## wt.41_0.66-ko.41_4 -43.150 -52.0681 -34.23188 0.0000
## wt.41_1.33-ko.41_4 -34.630 -43.5481 -25.71188 0.0000
## wt.41_2-ko.41_4 -8.855 -17.7731 0.06312 0.0519
## wt.41_4-ko.41_4 5.195 -3.7231 14.11312 0.4232
## wt.gst_4-ko.41_4 -36.015 -44.9331 -27.09688 0.0000
## wt.41_1.33-wt.41_0.66 8.520 -0.3981 17.43812 0.0635
## wt.41_2-wt.41_0.66 34.295 25.3769 43.21312 0.0000
## wt.41_4-wt.41_0.66 48.345 39.4269 57.26312 0.0000
## wt.gst_4-wt.41_0.66 7.135 -1.7831 16.05312 0.1458
## wt.41_2-wt.41_1.33 25.775 16.8569 34.69312 0.0000
## wt.41_4-wt.41_1.33 39.825 30.9069 48.74312 0.0000
## wt.gst_4-wt.41_1.33 -1.385 -10.3031 7.53312 0.9989
## wt.41_4-wt.41_2 14.050 5.1319 22.96812 0.0028
## wt.gst_4-wt.41_2 -27.160 -36.0781 -18.24188 0.0000
## wt.gst_4-wt.41_4 -41.210 -50.1281 -32.29188 0.0000##v38+v12 at 4 mg/ml_Wide type #Subset of v38+v12 at 4 mg/ml_Wide type
ia.v12_v38 = ia %>% filter(parasite=="WT",conc==4, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")
ia.v12_v38 = filter(ia,parasite=="WT",conc==4, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")#Anova_v38+v12 at 4 mg/ml_Wide type
anova.v12_v38 = aov(iia ~ para_ab_conc, data = ia.v12_v38)
summary(anova.v12_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 379 189.6 241 0.00049 ***
## Residuals 3 2 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v12 at 4 mg/ml_Wide type
tukey.anova.v12_v38= TukeyHSD(anova.v12_v38)
options(digits= 4)
tukey.anova.v12_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.12_4-wt.12_38_4 -18.640 -22.3492 -14.931 0.0005
## wt.38_4-wt.12_38_4 -14.205 -17.9142 -10.496 0.0011
## wt.38_4-wt.12_4 4.435 0.7258 8.144 0.0312##v38+v41 at 4 mg/ml_Wide type #Subset of v38+v41 at 4 mg/ml_Wide type
ia.v41_v38 = ia %>% filter(parasite=="WT",conc==4, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")
ia.v41_v38 = filter(ia,parasite=="WT",conc==4, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")#Anova_v38+v41 at 4 mg/ml_Wide type
anova.v41_v38 = aov(iia ~ para_ab_conc, data = ia.v41_v38)
summary(anova.v41_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 161.1 80.6 9.94 0.047 *
## Residuals 3 24.3 8.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v41 at 4 mg/ml_Wide type
tukey.anova.v41_v38= TukeyHSD(anova.v41_v38)
options(digits= 4)
tukey.anova.v41_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v41_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.41_38_4-wt.38_4 11.67 -0.2251 23.565 0.0525
## wt.41_4-wt.38_4 1.51 -10.3851 13.405 0.8629
## wt.41_4-wt.41_38_4 -10.16 -22.0551 1.735 0.0745##v12+v41 at 4 mg/ml_Wide type #Subset of v12+v41 at 4 mg/ml_Wide type
ia.v12_v41 = ia %>% filter(parasite=="WT",conc==4, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")
ia.v12_v41 = filter(ia,parasite=="WT",conc==4, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")#Anova_v41+v12 at 4 mg/ml_Wide type
anova.v12_v41 = aov(iia ~ para_ab_conc, data = ia.v12_v41)
summary(anova.v12_v41)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 323 161.3 25.8 0.013 *
## Residuals 3 19 6.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v41+v12 at 4 mg/ml
tukey.anova.v12_v41= TukeyHSD(anova.v12_v41)
options(digits= 4)
tukey.anova.v12_v41## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v41)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.12_41_4-wt.12_4 17.650 7.206 28.094 0.0119
## wt.41_4-wt.12_4 5.945 -4.499 16.389 0.1862
## wt.41_4-wt.12_41_4 -11.705 -22.149 -1.261 0.0371##v38+v12 at 4 mg/ml_kock out #Subset of v38+v12 at 4 mg/ml_kock out
ia.v12_v38 = ia %>% filter(parasite=="KO",conc==4, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")
ia.v12_v38 = filter(ia,parasite=="KO",conc==4, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")#Anova_v38+v12 at 4 mg/ml_kock out
anova.v12_v38 = aov(iia ~ para_ab_conc, data = ia.v12_v38)
summary(anova.v12_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 590 295.1 110 0.0016 **
## Residuals 3 8 2.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v12 at 4 mg/ml_kock out
tukey.anova.v12_v38= TukeyHSD(anova.v12_v38)
options(digits= 4)
tukey.anova.v12_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12_4-ko.12_38_4 -10.47 -17.32 -3.611 0.0159
## ko.38_4-ko.12_38_4 -24.22 -31.07 -17.366 0.0014
## ko.38_4-ko.12_4 -13.75 -20.61 -6.901 0.0073##v38+v41 at 4 mg/ml_kock out #Subset of v38+v41 at 4 mg/ml_kock out
ia.v41_v38 = ia %>% filter(parasite=="KO",conc==4, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")
ia.v41_v38 = filter(ia,parasite=="KO",conc==4, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")#Anova_v38+v41 at 4 mg/ml_kock out
anova.v41_v38 = aov(iia ~ para_ab_conc, data = ia.v41_v38)
summary(anova.v41_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 706 353 93.4 0.002 **
## Residuals 3 11 4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v41 at 4 mg/ml_kock out
tukey.anova.v41_v38= TukeyHSD(anova.v41_v38)
options(digits= 4)
tukey.anova.v41_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v41_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.41_38_4-ko.38_4 25.670 17.55 33.794 0.0020
## ko.41_4-ko.38_4 18.785 10.66 26.909 0.0048
## ko.41_4-ko.41_38_4 -6.885 -15.01 1.239 0.0760##v12+v41 at 4 mg/ml_kock out #Subset of v12+v41 at 4 mg/ml_kock out
ia.v12_v41 = ia %>% filter(parasite=="KO",conc==4, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")
ia.v12_v41 = filter(ia,parasite=="KO",conc==4, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")#Anova_v41+v12 at 4 mg/ml_kock out
anova.v12_v41 = aov(iia ~ para_ab_conc, data = ia.v12_v41)
summary(anova.v12_v41)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 305.2 152.6 34.5 0.0085 **
## Residuals 3 13.3 4.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v41+v12 at 4 mg/ml_kock out
tukey.anova.v12_v41= TukeyHSD(anova.v12_v41)
options(digits= 4)
tukey.anova.v12_v41## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v41)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12_41_4-ko.12_4 17.00 8.213 25.797 0.0081
## ko.41_4-ko.12_4 5.03 -3.762 13.822 0.1843
## ko.41_4-ko.12_41_4 -11.98 -20.767 -3.183 0.0218##v38+v12 at 2 mg/ml_Wide type #Subset of v38+v12 at 2 mg/ml_Wide type
ia.v12_v38 = ia %>% filter(parasite=="WT",conc==2, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")
ia.v12_v38 = filter(ia,parasite=="WT",conc==2, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")#Anova_v38+v12 at 2 mg/ml_Wide type
anova.v12_v38 = aov(iia ~ para_ab_conc, data = ia.v12_v38)
summary(anova.v12_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 694 347 193 0.00068 ***
## Residuals 3 5 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v12 at 2 mg/ml_Wide type
tukey.anova.v12_v38= TukeyHSD(anova.v12_v38)
options(digits= 4)
tukey.anova.v12_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.12_38_4-wt.12_2 26.33 20.724 31.936 0.0006
## wt.38_2-wt.12_2 12.69 7.084 18.296 0.0051
## wt.38_2-wt.12_38_4 -13.64 -19.246 -8.034 0.0042###v38+v41 at 2 mg/ml_Wide type #Subset of v38+v41 at 2 mg/ml_Wide type
ia.v41_v38 = ia %>% filter(parasite=="WT",conc==2, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")
ia.v41_v38 = filter(ia,parasite=="WT",conc==2, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")#Anova_v38+v41 at 2 mg/ml_Wide type
anova.v41_v38 = aov(iia ~ para_ab_conc, data = ia.v41_v38)
summary(anova.v41_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 81.5 40.7 4.33 0.13
## Residuals 3 28.2 9.4#Post-hoc analysis_v38+v41 at 2 mg/ml_Wide type
tukey.anova.v41_v38= TukeyHSD(anova.v41_v38)
options(digits= 4)
tukey.anova.v41_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v41_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.41_2-wt.38_2 3.085 -9.737 15.91 0.6232
## wt.41_38_4-wt.38_2 8.890 -3.932 21.71 0.1220
## wt.41_38_4-wt.41_2 5.805 -7.017 18.63 0.2851##v12+v41 at 2 mg/ml_Wide type #Subset of v12+v41 at 2 mg/ml_Wide type
ia.v12_v41 = ia %>% filter(parasite=="WT",conc==2, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")
ia.v12_v41 = filter(ia,parasite=="WT",conc==2, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")#Anova_v41+v12 at 2 mg/ml_Wide type
anova.v12_v41 = aov(iia ~ para_ab_conc, data = ia.v12_v41)
summary(anova.v12_v41)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 1073 537 723 9.4e-05 ***
## Residuals 3 2 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v41+v12 at 2 mg/ml_Wide type
tukey.anova.v12_v41= TukeyHSD(anova.v12_v41)
options(digits= 4)
tukey.anova.v12_v41## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v41)
##
## $para_ab_conc
## diff lwr upr p adj
## wt.12_41_4-wt.12_2 32.75 29.15 36.35 0e+00
## wt.41_2-wt.12_2 15.77 12.17 19.38 8e-04
## wt.41_2-wt.12_41_4 -16.98 -20.58 -13.37 6e-04##v38+v12 at 2 mg/ml_knock out #Subset of v38+v12 at 2 mg/ml_knock out
ia.v12_v38 = ia %>% filter(parasite=="KO",conc==2, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")
ia.v12_v38 = filter(ia,parasite=="KO",conc==2, ab_name=="v38"|ab_name=="v12"|ab_name=="v12_v38")#Anova_v38+v12 at 2 mg/ml_knock out
anova.v12_v38 = aov(iia ~ para_ab_conc, data = ia.v12_v38)
summary(anova.v12_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 523 261.5 41.1 0.0066 **
## Residuals 3 19 6.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v12 at 2 mg/ml_knock out
tukey.anova.v12_v38= TukeyHSD(anova.v12_v38)
options(digits= 4)
tukey.anova.v12_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12_38_4-ko.12_2 2.24 -8.305 12.785 0.6833
## ko.38_2-ko.12_2 -18.59 -29.135 -8.045 0.0106
## ko.38_2-ko.12_38_4 -20.83 -31.375 -10.285 0.0076###v38+v41 at 2 mg/ml_knock out #Subset of v38+v41 at 2 mg/ml_knock out
ia.v41_v38 = ia %>% filter(parasite=="KO",conc==2, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")
ia.v41_v38 = filter(ia,parasite=="KO",conc==2, ab_name=="v38"|ab_name=="v41"|ab_name=="v41_v38")#Anova_v38+v41 at 2 mg/ml_knock out
anova.v41_v38 = aov(iia ~ para_ab_conc, data = ia.v41_v38)
summary(anova.v41_v38)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 1323 662 118 0.0014 **
## Residuals 3 17 6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v38+v41 at 2 mg/ml_knock out
tukey.anova.v41_v38= TukeyHSD(anova.v41_v38)
options(digits= 4)
tukey.anova.v41_v38## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v41_v38)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.41_2-ko.38_2 34.135 24.22 44.052 0.0015
## ko.41_38_4-ko.38_2 27.960 18.04 37.877 0.0027
## ko.41_38_4-ko.41_2 -6.175 -16.09 3.742 0.1545##v12+v41 at 2 mg/ml_knock out #Subset of v12+v41 at 2 mg/ml_knock out
ia.v12_v41 = ia %>% filter(parasite=="KO",conc==2, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")
ia.v12_v41 = filter(ia,parasite=="KO",conc==2, ab_name=="v41"|ab_name=="v12"|ab_name=="v12_v41")#Anova_v41+v12 at 2 mg/ml_knock out
anova.v12_v41 = aov(iia ~ para_ab_conc, data = ia.v12_v41)
summary(anova.v12_v41)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 2 468 234.0 268 0.00042 ***
## Residuals 3 3 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_v41+v12 at 2 mg/ml_knock out
tukey.anova.v12_v41= TukeyHSD(anova.v12_v41)
options(digits= 4)
tukey.anova.v12_v41## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.v12_v41)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12_41_4-ko.12_2 20.800 16.894 24.706 0.0004
## ko.41_2-ko.12_2 15.545 11.639 19.451 0.0010
## ko.41_2-ko.12_41_4 -5.255 -9.161 -1.349 0.0226###combine 3 Abs_Wt and Ko #Subset 3 Abs
ia.combine = ia %>% filter(ab_no==3 | ab_no==1 & conc==4)
ia.combine = filter(ia,ab_no==3 | ab_no==1 & conc==4)#Anova_combine 3 Abs
anova.3 = aov(iia ~ para_ab_conc, data = ia.combine)
summary(anova.3)## Df Sum Sq Mean Sq F value Pr(>F)
## para_ab_conc 18 6628 368 65.7 2.3e-13 ***
## Residuals 19 106 6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Post-hoc analysis_combine 3 Abs
tukey.anova.3= TukeyHSD(anova.3)
options(digits= 4)
tukey.anova.3## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = iia ~ para_ab_conc, data = ia.combine)
##
## $para_ab_conc
## diff lwr upr p adj
## ko.12.41.38_0.66-ko.12.41.38_0.33 1.135 -8.40663 10.67663 1.0000
## ko.12.41.38_1.33-ko.12.41.38_0.33 13.630 4.08837 23.17163 0.0015
## ko.12.41.GST_0.33-ko.12.41.38_0.33 -0.180 -9.72163 9.36163 1.0000
## ko.12.41.GST_0.66-ko.12.41.38_0.33 17.680 8.13837 27.22163 0.0001
## ko.12.41.GST_1.33-ko.12.41.38_0.33 20.730 11.18837 30.27163 0.0000
## ko.12_4-ko.12.41.38_0.33 12.205 2.66337 21.74663 0.0052
## ko.38_4-ko.12.41.38_0.33 -1.550 -11.09163 7.99163 1.0000
## ko.41_4-ko.12.41.38_0.33 17.235 7.69337 26.77663 0.0001
## wt.12.41.38_0.33-ko.12.41.38_0.33 14.030 4.48837 23.57163 0.0011
## wt.12.41.38_0.66-ko.12.41.38_0.33 27.130 17.58837 36.67163 0.0000
## wt.12.41.38_1.33-ko.12.41.38_0.33 43.320 33.77837 52.86163 0.0000
## wt.12.41.GST_0.33-ko.12.41.38_0.33 5.665 -3.87663 15.20663 0.6406
## wt.12.41.GST_0.66-ko.12.41.38_0.33 11.490 1.94837 21.03163 0.0096
## wt.12.41.GST_1.33-ko.12.41.38_0.33 25.985 16.44337 35.52663 0.0000
## wt.12_4-ko.12.41.38_0.33 16.485 6.94337 26.02663 0.0001
## wt.38_4-ko.12.41.38_0.33 20.920 11.37837 30.46163 0.0000
## wt.41_4-ko.12.41.38_0.33 22.430 12.88837 31.97163 0.0000
## wt.gst_4-ko.12.41.38_0.33 -18.780 -28.32163 -9.23837 0.0000
## ko.12.41.38_1.33-ko.12.41.38_0.66 12.495 2.95337 22.03663 0.0040
## ko.12.41.GST_0.33-ko.12.41.38_0.66 -1.315 -10.85663 8.22663 1.0000
## ko.12.41.GST_0.66-ko.12.41.38_0.66 16.545 7.00337 26.08663 0.0001
## ko.12.41.GST_1.33-ko.12.41.38_0.66 19.595 10.05337 29.13663 0.0000
## ko.12_4-ko.12.41.38_0.66 11.070 1.52837 20.61163 0.0138
## ko.38_4-ko.12.41.38_0.66 -2.685 -12.22663 6.85663 0.9992
## ko.41_4-ko.12.41.38_0.66 16.100 6.55837 25.64163 0.0002
## wt.12.41.38_0.33-ko.12.41.38_0.66 12.895 3.35337 22.43663 0.0028
## wt.12.41.38_0.66-ko.12.41.38_0.66 25.995 16.45337 35.53663 0.0000
## wt.12.41.38_1.33-ko.12.41.38_0.66 42.185 32.64337 51.72663 0.0000
## wt.12.41.GST_0.33-ko.12.41.38_0.66 4.530 -5.01163 14.07163 0.8885
## wt.12.41.GST_0.66-ko.12.41.38_0.66 10.355 0.81337 19.89663 0.0254
## wt.12.41.GST_1.33-ko.12.41.38_0.66 24.850 15.30837 34.39163 0.0000
## wt.12_4-ko.12.41.38_0.66 15.350 5.80837 24.89163 0.0004
## wt.38_4-ko.12.41.38_0.66 19.785 10.24337 29.32663 0.0000
## wt.41_4-ko.12.41.38_0.66 21.295 11.75337 30.83663 0.0000
## wt.gst_4-ko.12.41.38_0.66 -19.915 -29.45663 -10.37337 0.0000
## ko.12.41.GST_0.33-ko.12.41.38_1.33 -13.810 -23.35163 -4.26837 0.0013
## ko.12.41.GST_0.66-ko.12.41.38_1.33 4.050 -5.49163 13.59163 0.9498
## ko.12.41.GST_1.33-ko.12.41.38_1.33 7.100 -2.44163 16.64163 0.3042
## ko.12_4-ko.12.41.38_1.33 -1.425 -10.96663 8.11663 1.0000
## ko.38_4-ko.12.41.38_1.33 -15.180 -24.72163 -5.63837 0.0004
## ko.41_4-ko.12.41.38_1.33 3.605 -5.93663 13.14663 0.9812
## wt.12.41.38_0.33-ko.12.41.38_1.33 0.400 -9.14163 9.94163 1.0000
## wt.12.41.38_0.66-ko.12.41.38_1.33 13.500 3.95837 23.04163 0.0017
## wt.12.41.38_1.33-ko.12.41.38_1.33 29.690 20.14837 39.23163 0.0000
## wt.12.41.GST_0.33-ko.12.41.38_1.33 -7.965 -17.50663 1.57663 0.1697
## wt.12.41.GST_0.66-ko.12.41.38_1.33 -2.140 -11.68163 7.40163 1.0000
## wt.12.41.GST_1.33-ko.12.41.38_1.33 12.355 2.81337 21.89663 0.0045
## wt.12_4-ko.12.41.38_1.33 2.855 -6.68663 12.39663 0.9983
## wt.38_4-ko.12.41.38_1.33 7.290 -2.25163 16.83163 0.2695
## wt.41_4-ko.12.41.38_1.33 8.800 -0.74163 18.34163 0.0906
## wt.gst_4-ko.12.41.38_1.33 -32.410 -41.95163 -22.86837 0.0000
## ko.12.41.GST_0.66-ko.12.41.GST_0.33 17.860 8.31837 27.40163 0.0000
## ko.12.41.GST_1.33-ko.12.41.GST_0.33 20.910 11.36837 30.45163 0.0000
## ko.12_4-ko.12.41.GST_0.33 12.385 2.84337 21.92663 0.0044
## ko.38_4-ko.12.41.GST_0.33 -1.370 -10.91163 8.17163 1.0000
## ko.41_4-ko.12.41.GST_0.33 17.415 7.87337 26.95663 0.0001
## wt.12.41.38_0.33-ko.12.41.GST_0.33 14.210 4.66837 23.75163 0.0009
## wt.12.41.38_0.66-ko.12.41.GST_0.33 27.310 17.76837 36.85163 0.0000
## wt.12.41.38_1.33-ko.12.41.GST_0.33 43.500 33.95837 53.04163 0.0000
## wt.12.41.GST_0.33-ko.12.41.GST_0.33 5.845 -3.69663 15.38663 0.5946
## wt.12.41.GST_0.66-ko.12.41.GST_0.33 11.670 2.12837 21.21163 0.0082
## wt.12.41.GST_1.33-ko.12.41.GST_0.33 26.165 16.62337 35.70663 0.0000
## wt.12_4-ko.12.41.GST_0.33 16.665 7.12337 26.20663 0.0001
## wt.38_4-ko.12.41.GST_0.33 21.100 11.55837 30.64163 0.0000
## wt.41_4-ko.12.41.GST_0.33 22.610 13.06837 32.15163 0.0000
## wt.gst_4-ko.12.41.GST_0.33 -18.600 -28.14163 -9.05837 0.0000
## ko.12.41.GST_1.33-ko.12.41.GST_0.66 3.050 -6.49163 12.59163 0.9964
## ko.12_4-ko.12.41.GST_0.66 -5.475 -15.01663 4.06663 0.6886
## ko.38_4-ko.12.41.GST_0.66 -19.230 -28.77163 -9.68837 0.0000
## ko.41_4-ko.12.41.GST_0.66 -0.445 -9.98663 9.09663 1.0000
## wt.12.41.38_0.33-ko.12.41.GST_0.66 -3.650 -13.19163 5.89163 0.9789
## wt.12.41.38_0.66-ko.12.41.GST_0.66 9.450 -0.09163 18.99163 0.0539
## wt.12.41.38_1.33-ko.12.41.GST_0.66 25.640 16.09837 35.18163 0.0000
## wt.12.41.GST_0.33-ko.12.41.GST_0.66 -12.015 -21.55663 -2.47337 0.0061
## wt.12.41.GST_0.66-ko.12.41.GST_0.66 -6.190 -15.73163 3.35163 0.5070
## wt.12.41.GST_1.33-ko.12.41.GST_0.66 8.305 -1.23663 17.84663 0.1323
## wt.12_4-ko.12.41.GST_0.66 -1.195 -10.73663 8.34663 1.0000
## wt.38_4-ko.12.41.GST_0.66 3.240 -6.30163 12.78163 0.9933
## wt.41_4-ko.12.41.GST_0.66 4.750 -4.79163 14.29163 0.8503
## wt.gst_4-ko.12.41.GST_0.66 -36.460 -46.00163 -26.91837 0.0000
## ko.12_4-ko.12.41.GST_1.33 -8.525 -18.06663 1.01663 0.1120
## ko.38_4-ko.12.41.GST_1.33 -22.280 -31.82163 -12.73837 0.0000
## ko.41_4-ko.12.41.GST_1.33 -3.495 -13.03663 6.04663 0.9858
## wt.12.41.38_0.33-ko.12.41.GST_1.33 -6.700 -16.24163 2.84163 0.3865
## wt.12.41.38_0.66-ko.12.41.GST_1.33 6.400 -3.14163 15.94163 0.4557
## wt.12.41.38_1.33-ko.12.41.GST_1.33 22.590 13.04837 32.13163 0.0000
## wt.12.41.GST_0.33-ko.12.41.GST_1.33 -15.065 -24.60663 -5.52337 0.0004
## wt.12.41.GST_0.66-ko.12.41.GST_1.33 -9.240 -18.78163 0.30163 0.0639
## wt.12.41.GST_1.33-ko.12.41.GST_1.33 5.255 -4.28663 14.79663 0.7421
## wt.12_4-ko.12.41.GST_1.33 -4.245 -13.78663 5.29663 0.9286
## wt.38_4-ko.12.41.GST_1.33 0.190 -9.35163 9.73163 1.0000
## wt.41_4-ko.12.41.GST_1.33 1.700 -7.84163 11.24163 1.0000
## wt.gst_4-ko.12.41.GST_1.33 -39.510 -49.05163 -29.96837 0.0000
## ko.38_4-ko.12_4 -13.755 -23.29663 -4.21337 0.0014
## ko.41_4-ko.12_4 5.030 -4.51163 14.57163 0.7933
## wt.12.41.38_0.33-ko.12_4 1.825 -7.71663 11.36663 1.0000
## wt.12.41.38_0.66-ko.12_4 14.925 5.38337 24.46663 0.0005
## wt.12.41.38_1.33-ko.12_4 31.115 21.57337 40.65663 0.0000
## wt.12.41.GST_0.33-ko.12_4 -6.540 -16.08163 3.00163 0.4227
## wt.12.41.GST_0.66-ko.12_4 -0.715 -10.25663 8.82663 1.0000
## wt.12.41.GST_1.33-ko.12_4 13.780 4.23837 23.32163 0.0013
## wt.12_4-ko.12_4 4.280 -5.26163 13.82163 0.9243
## wt.38_4-ko.12_4 8.715 -0.82663 18.25663 0.0968
## wt.41_4-ko.12_4 10.225 0.68337 19.76663 0.0283
## wt.gst_4-ko.12_4 -30.985 -40.52663 -21.44337 0.0000
## ko.41_4-ko.38_4 18.785 9.24337 28.32663 0.0000
## wt.12.41.38_0.33-ko.38_4 15.580 6.03837 25.12163 0.0003
## wt.12.41.38_0.66-ko.38_4 28.680 19.13837 38.22163 0.0000
## wt.12.41.38_1.33-ko.38_4 44.870 35.32837 54.41163 0.0000
## wt.12.41.GST_0.33-ko.38_4 7.215 -2.32663 16.75663 0.2828
## wt.12.41.GST_0.66-ko.38_4 13.040 3.49837 22.58163 0.0025
## wt.12.41.GST_1.33-ko.38_4 27.535 17.99337 37.07663 0.0000
## wt.12_4-ko.38_4 18.035 8.49337 27.57663 0.0000
## wt.38_4-ko.38_4 22.470 12.92837 32.01163 0.0000
## wt.41_4-ko.38_4 23.980 14.43837 33.52163 0.0000
## wt.gst_4-ko.38_4 -17.230 -26.77163 -7.68837 0.0001
## wt.12.41.38_0.33-ko.41_4 -3.205 -12.74663 6.33663 0.9940
## wt.12.41.38_0.66-ko.41_4 9.895 0.35337 19.43663 0.0373
## wt.12.41.38_1.33-ko.41_4 26.085 16.54337 35.62663 0.0000
## wt.12.41.GST_0.33-ko.41_4 -11.570 -21.11163 -2.02837 0.0090
## wt.12.41.GST_0.66-ko.41_4 -5.745 -15.28663 3.79663 0.6202
## wt.12.41.GST_1.33-ko.41_4 8.750 -0.79163 18.29163 0.0942
## wt.12_4-ko.41_4 -0.750 -10.29163 8.79163 1.0000
## wt.38_4-ko.41_4 3.685 -5.85663 13.22663 0.9771
## wt.41_4-ko.41_4 5.195 -4.34663 14.73663 0.7561
## wt.gst_4-ko.41_4 -36.015 -45.55663 -26.47337 0.0000
## wt.12.41.38_0.66-wt.12.41.38_0.33 13.100 3.55837 22.64163 0.0024
## wt.12.41.38_1.33-wt.12.41.38_0.33 29.290 19.74837 38.83163 0.0000
## wt.12.41.GST_0.33-wt.12.41.38_0.33 -8.365 -17.90663 1.17663 0.1265
## wt.12.41.GST_0.66-wt.12.41.38_0.33 -2.540 -12.08163 7.00163 0.9996
## wt.12.41.GST_1.33-wt.12.41.38_0.33 11.955 2.41337 21.49663 0.0064
## wt.12_4-wt.12.41.38_0.33 2.455 -7.08663 11.99663 0.9997
## wt.38_4-wt.12.41.38_0.33 6.890 -2.65163 16.43163 0.3459
## wt.41_4-wt.12.41.38_0.33 8.400 -1.14163 17.94163 0.1232
## wt.gst_4-wt.12.41.38_0.33 -32.810 -42.35163 -23.26837 0.0000
## wt.12.41.38_1.33-wt.12.41.38_0.66 16.190 6.64837 25.73163 0.0002
## wt.12.41.GST_0.33-wt.12.41.38_0.66 -21.465 -31.00663 -11.92337 0.0000
## wt.12.41.GST_0.66-wt.12.41.38_0.66 -15.640 -25.18163 -6.09837 0.0003
## wt.12.41.GST_1.33-wt.12.41.38_0.66 -1.145 -10.68663 8.39663 1.0000
## wt.12_4-wt.12.41.38_0.66 -10.645 -20.18663 -1.10337 0.0198
## wt.38_4-wt.12.41.38_0.66 -6.210 -15.75163 3.33163 0.5021
## wt.41_4-wt.12.41.38_0.66 -4.700 -14.24163 4.84163 0.8595
## wt.gst_4-wt.12.41.38_0.66 -45.910 -55.45163 -36.36837 0.0000
## wt.12.41.GST_0.33-wt.12.41.38_1.33 -37.655 -47.19663 -28.11337 0.0000
## wt.12.41.GST_0.66-wt.12.41.38_1.33 -31.830 -41.37163 -22.28837 0.0000
## wt.12.41.GST_1.33-wt.12.41.38_1.33 -17.335 -26.87663 -7.79337 0.0001
## wt.12_4-wt.12.41.38_1.33 -26.835 -36.37663 -17.29337 0.0000
## wt.38_4-wt.12.41.38_1.33 -22.400 -31.94163 -12.85837 0.0000
## wt.41_4-wt.12.41.38_1.33 -20.890 -30.43163 -11.34837 0.0000
## wt.gst_4-wt.12.41.38_1.33 -62.100 -71.64163 -52.55837 0.0000
## wt.12.41.GST_0.66-wt.12.41.GST_0.33 5.825 -3.71663 15.36663 0.5997
## wt.12.41.GST_1.33-wt.12.41.GST_0.33 20.320 10.77837 29.86163 0.0000
## wt.12_4-wt.12.41.GST_0.33 10.820 1.27837 20.36163 0.0171
## wt.38_4-wt.12.41.GST_0.33 15.255 5.71337 24.79663 0.0004
## wt.41_4-wt.12.41.GST_0.33 16.765 7.22337 26.30663 0.0001
## wt.gst_4-wt.12.41.GST_0.33 -24.445 -33.98663 -14.90337 0.0000
## wt.12.41.GST_1.33-wt.12.41.GST_0.66 14.495 4.95337 24.03663 0.0007
## wt.12_4-wt.12.41.GST_0.66 4.995 -4.54663 14.53663 0.8009
## wt.38_4-wt.12.41.GST_0.66 9.430 -0.11163 18.97163 0.0548
## wt.41_4-wt.12.41.GST_0.66 10.940 1.39837 20.48163 0.0154
## wt.gst_4-wt.12.41.GST_0.66 -30.270 -39.81163 -20.72837 0.0000
## wt.12_4-wt.12.41.GST_1.33 -9.500 -19.04163 0.04163 0.0517
## wt.38_4-wt.12.41.GST_1.33 -5.065 -14.60663 4.47663 0.7856
## wt.41_4-wt.12.41.GST_1.33 -3.555 -13.09663 5.98663 0.9834
## wt.gst_4-wt.12.41.GST_1.33 -44.765 -54.30663 -35.22337 0.0000
## wt.38_4-wt.12_4 4.435 -5.10663 13.97663 0.9031
## wt.41_4-wt.12_4 5.945 -3.59663 15.48663 0.5690
## wt.gst_4-wt.12_4 -35.265 -44.80663 -25.72337 0.0000
## wt.41_4-wt.38_4 1.510 -8.03163 11.05163 1.0000
## wt.gst_4-wt.38_4 -39.700 -49.24163 -30.15837 0.0000
## wt.gst_4-wt.41_4 -41.210 -50.75163 -31.66837 0.0000