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outputfigurerescueIII_stats.txt

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  1. [1] "speed"
    2. 

  2. Component 1 :
    3. 

  3.             Df Sum Sq Mean Sq F value    Pr(>F)    
    4. 

  4. data$group   3 763.19 254.396  25.949 4.404e-12 ***
    5. 

  5. Residuals   87 852.92   9.804                      
    6. 

  6. ---
    7. 

  7. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    8. 

  8. 

  9.   Tukey multiple comparisons of means
    10. 

  10.     95% family-wise confidence level
    11. 

  11. 

  12. Fit: aov(formula = data$median_speed ~ data$group)
    13. 

  13. 

  14. $`data$group`
    15. 

  15.                                          diff        lwr       upr     p adj
    16. 

  16. Control_actin2-Control_actin1      -6.6750135 -9.0935098 -4.256517 0.0000000
    17. 

  17. Control_actin3 -Control_actin1     -4.6679652 -7.1137900 -2.222140 0.0000174
    18. 

  18. Experimental_actin-Control_actin1  -0.1541438 -2.5726401  2.264353 0.9983325
    19. 

  19. Control_actin3 -Control_actin2      2.0070483 -0.4387766  4.452873 0.1458660
    20. 

  20. Experimental_actin-Control_actin2   6.5208697  4.1023734  8.939366 0.0000000
    21. 

  21. Experimental_actin-Control_actin3   4.5138214  2.0679966  6.959646 0.0000335
    22. 

  22. 

  23. [1] "angledev"
    24. 

  24. 

  25. 	Pairwise comparisons using Wilcoxon rank sum exact test 
    26. 

  26. 

  27. data:  data$stripe_deviation and data$group 
    28. 

  28. 

  29.                    Control_actin1 Control_actin2 Control_actin3 
    30. 

  30. Control_actin2     5.8e-12        -              -              
    31. 

  31. Control_actin3     0.00016        9.5e-05        -              
    32. 

  32. Experimental_actin 0.01064        4.3e-05        1.00000        
    33. 

  33. 

  34. P value adjustment method: bonferroni 
    35. 

  35. [1] "speed"
    36. 

  36. Component 1 :
    37. 

  37.             Df Sum Sq Mean Sq F value    Pr(>F)    
    38. 

  38. data$group   3 502.60  167.53  13.119 5.454e-07 ***
    39. 

  39. Residuals   76 970.54   12.77                      
    40. 

  40. ---
    41. 

  41. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    42. 

  42. 

  43.   Tukey multiple comparisons of means
    44. 

  44.     95% family-wise confidence level
    45. 

  45. 

  46. Fit: aov(formula = data$median_speed ~ data$group)
    47. 

  47. 

  48. $`data$group`
    49. 

  49.                                       diff        lwr        upr     p adj
    50. 

  50. Control2_nsyb-Control1_nsyb     -3.1700277 -6.1384564 -0.2015991 0.0317212
    51. 

  51. Control3_nsyb-Control1_nsyb     -0.9424625 -3.9108912  2.0259661 0.8381294
    52. 

  52. Experimental_nsyb-Control1_nsyb  3.7711940  0.8027653  6.7396226 0.0070397
    53. 

  53. Control3_nsyb-Control2_nsyb      2.2275652 -0.7408635  5.1959938 0.2079812
    54. 

  54. Experimental_nsyb-Control2_nsyb  6.9412217  3.9727930  9.9096503 0.0000002
    55. 

  55. Experimental_nsyb-Control3_nsyb  4.7136565  1.7452278  7.6820851 0.0004552
    56. 

  56. 

  57. [1] "angledev"
    58. 

  58. 

  59. 	Pairwise comparisons using Wilcoxon rank sum exact test 
    60. 

  60. 

  61. data:  data$stripe_deviation and data$group 
    62. 

  62. 

  63.                   Control1_nsyb Control2_nsyb Control3_nsyb
    64. 

  64. Control2_nsyb     8.0e-07       -             -            
    65. 

  65. Control3_nsyb     0.31          1.4e-05       -            
    66. 

  66. Experimental_nsyb 0.16          4.5e-06       1.00         
    67. 

  67. 

  68. P value adjustment method: bonferroni 
    69. 

  69. [1] "speed"
    70. 

  70. Component 1 :
    71. 

  71.              Df  Sum Sq Mean Sq F value    Pr(>F)    
    72. 

  72. data$group    3  430.67 143.558  9.6774 7.149e-06 ***
    73. 

  73. Residuals   148 2195.48  14.834                      
    74. 

  74. ---
    75. 

  75. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    76. 

  76. 

  77.   Tukey multiple comparisons of means
    78. 

  78.     95% family-wise confidence level
    79. 

  79. 

  80. Fit: aov(formula = data$median_speed ~ data$group)
    81. 

  81. 

  82. $`data$group`
    83. 

  83.                                       diff         lwr        upr     p adj
    84. 

  84. control2_tdc1-control1_tdc1     -4.7811273 -7.09754415 -2.4647104 0.0000018
    85. 

  85. Control3_tdc1-control1_tdc1     -2.5504128 -4.91024689 -0.1905787 0.0285960
    86. 

  86. Experimental_tdc1-control1_tdc1 -2.9170876 -5.23350450 -0.6006707 0.0071914
    87. 

  87. Control3_tdc1-control2_tdc1      2.2307145 -0.05206843  4.5134974 0.0580936
    88. 

  88. Experimental_tdc1-control2_tdc1  1.8640397 -0.37383166  4.1019110 0.1381025
    89. 

  89. Experimental_tdc1-Control3_tdc1 -0.3666748 -2.64945772  1.9161081 0.9754320
    90. 

  90. 

  91. [1] "angledev"
    92. 

  92. 

  93. 	Pairwise comparisons using Wilcoxon rank sum exact test 
    94. 

  94. 

  95. data:  data$stripe_deviation and data$group 
    96. 

  96. 

  97.                   control1_tdc1 control2_tdc1 Control3_tdc1
    98. 

  98. control2_tdc1     9.0e-14       -             -            
    99. 

  99. Control3_tdc1     0.0029        2.7e-09       -            
    100. 

  100. Experimental_tdc1 1.0000        3.0e-14       0.0123       
    101. 

  101. 

  102. P value adjustment method: bonferroni 
    103. 

  103. [1] "speed"
    104. 

  104. Component 1 :
    105. 

  105.             Df Sum Sq Mean Sq F value    Pr(>F)    
    106. 

  106. data$group   3 151.26  50.421  6.4778 0.0006382 ***
    107. 

  107. Residuals   68 529.29   7.784                      
    108. 

  108. ---
    109. 

  109. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    110. 

  110. 

  111.   Tukey multiple comparisons of means
    112. 

  112.     95% family-wise confidence level
    113. 

  113. 

  114. Fit: aov(formula = data$median_speed ~ data$group)
    115. 

  115. 

  116. $`data$group`
    117. 

  117.                                       diff        lwr      upr     p adj
    118. 

  118. control2_tdc2-control1_tdc2      0.1060279 -2.2479530 2.460009 0.9993971
    119. 

  119. control3_tdc2-control1_tdc2     -0.6010208 -3.3104377 2.108396 0.9365084
    120. 

  120. Experimental_tdc2-control1_tdc2  3.0460583  0.7195427 5.372574 0.0052574
    121. 

  121. control3_tdc2-control2_tdc2     -0.7070487 -3.3901173 1.976020 0.8990273
    122. 

  122. Experimental_tdc2-control2_tdc2  2.9400304  0.6442534 5.235807 0.0066048
    123. 

  123. Experimental_tdc2-control3_tdc2  3.6470791  0.9880743 6.306084 0.0031623
    124. 

  124. 

  125. [1] "angledev"
    126. 

  126. 

  127. 	Pairwise comparisons using Wilcoxon rank sum exact test 
    128. 

  128. 

  129. data:  data$stripe_deviation and data$group 
    130. 

  130. 

  131.                   control1_tdc2 control2_tdc2 control3_tdc2
    132. 

  132. control2_tdc2     3.3e-09       -             -            
    133. 

  133. control3_tdc2     0.00109       0.00036       -            
    134. 

  134. Experimental_tdc2 1.7e-06       6.5e-07       1.00000      
    135. 

  135. 

  136. P value adjustment method: bonferroni 
    137. 

  137. [1] "speed"
    138. 

  138. Component 1 :
    139. 

  139.             Df Sum Sq Mean Sq F value   Pr(>F)    
    140. 

  140. data$group   3  662.4 220.801  13.405 3.59e-07 ***
    141. 

  141. Residuals   80 1317.7  16.471                     
    142. 

  142. ---
    143. 

  143. Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    144. 

  144. 

  145.   Tukey multiple comparisons of means
    146. 

  146.     95% family-wise confidence level
    147. 

  147. 

  148. Fit: aov(formula = data$median_speed ~ data$group)
    149. 

  149. 

  150. $`data$group`
    151. 

  151.                                           diff       lwr       upr     p adj
    152. 

  152. control2_np7088-control1_np7088     -6.3940148 -9.680326 -3.107704 0.0000129
    153. 

  153. control3_np7088-control1_np7088     -5.9551877 -9.241499 -2.668877 0.0000505
    154. 

  154. Experimental_np7088-control1_np7088 -1.2810092 -4.567320  2.005302 0.7366318
    155. 

  155. control3_np7088-control2_np7088      0.4388272 -2.847484  3.725138 0.9851304
    156. 

  156. Experimental_np7088-control2_np7088  5.1130056  1.826695  8.399316 0.0005978
    157. 

  157. Experimental_np7088-control3_np7088  4.6741784  1.387868  7.960489 0.0019721
    158. 

  158. 

  159. [1] "angledev"
    160. 

  160. 

  161. 	Pairwise comparisons using Wilcoxon rank sum exact test 
    162. 

  162. 

  163. data:  data$stripe_deviation and data$group 
    164. 

  164. 

  165.                     control1_np7088 control2_np7088 control3_np7088
    166. 

  166. control2_np7088     8.9e-11         -               -              
    167. 

  167. control3_np7088     0.4688          0.0015          -              
    168. 

  168. Experimental_np7088 1.0000          1.8e-05         1.0000         
    169. 

  169. 

  170. P value adjustment method: bonferroni 
    171.