LID SPN TRAP

1448115_at

Trmt2a TRM2 tRNA methyltransferase 2 homolog A (S. cerevisiae)

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.027 0.870 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.79
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.000137
Time: 19:56:05 Log-Likelihood: -101.01
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 30.7216 85.459 0.359 0.723 -148.146 209.590
C(dose)[T.1] 84.0129 132.263 0.635 0.533 -192.817 360.843
expression 3.6191 13.134 0.276 0.786 -23.870 31.108
expression:C(dose)[T.1] -4.7031 20.080 -0.234 0.817 -46.731 37.325
Omnibus: 0.345 Durbin-Watson: 1.989
Prob(Omnibus): 0.841 Jarque-Bera (JB): 0.499
Skew: 0.057 Prob(JB): 0.779
Kurtosis: 2.288 Cond. No. 249.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Sun, 09 Nov 2025 Prob (F-statistic): 2.80e-05
Time: 19:56:05 Log-Likelihood: -101.05
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 43.7788 63.223 0.692 0.497 -88.102 175.659
C(dose)[T.1] 53.1080 8.872 5.986 0.000 34.601 71.615
expression 1.6071 9.697 0.166 0.870 -18.621 21.835
Omnibus: 0.307 Durbin-Watson: 1.903
Prob(Omnibus): 0.858 Jarque-Bera (JB): 0.476
Skew: 0.054 Prob(JB): 0.788
Kurtosis: 2.304 Cond. No. 97.4

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Sun, 09 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 19:56:05 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.022
Model: OLS Adj. R-squared: -0.025
Method: Least Squares F-statistic: 0.4652
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.503
Time: 19:56:06 Log-Likelihood: -112.85
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.8578 102.671 0.096 0.924 -203.659 223.374
expression 10.6529 15.618 0.682 0.503 -21.827 43.133
Omnibus: 3.032 Durbin-Watson: 2.428
Prob(Omnibus): 0.220 Jarque-Bera (JB): 1.526
Skew: 0.297 Prob(JB): 0.466
Kurtosis: 1.887 Cond. No. 96.7

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.249 0.627 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.320
Method: Least Squares F-statistic: 3.197
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0663
Time: 19:56:06 Log-Likelihood: -70.598
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.2139 356.655 0.138 0.893 -735.779 834.207
C(dose)[T.1] 191.6918 414.421 0.463 0.653 -720.443 1103.827
expression 2.7812 54.429 0.051 0.960 -117.016 122.578
expression:C(dose)[T.1] -21.8789 63.335 -0.345 0.736 -161.279 117.521
Omnibus: 3.029 Durbin-Watson: 0.792
Prob(Omnibus): 0.220 Jarque-Bera (JB): 2.075
Skew: -0.894 Prob(JB): 0.354
Kurtosis: 2.648 Cond. No. 507.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 5.111
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0248
Time: 19:56:06 Log-Likelihood: -70.679
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 155.0344 175.827 0.882 0.395 -228.060 538.129
C(dose)[T.1] 48.6422 15.618 3.114 0.009 14.613 82.671
expression -13.3768 26.791 -0.499 0.627 -71.750 44.996
Omnibus: 3.603 Durbin-Watson: 0.828
Prob(Omnibus): 0.165 Jarque-Bera (JB): 2.415
Skew: -0.974 Prob(JB): 0.299
Kurtosis: 2.740 Cond. No. 152.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.00629
Time: 19:56:06 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.023
Model: OLS Adj. R-squared: -0.052
Method: Least Squares F-statistic: 0.3127
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.586
Time: 19:56:06 Log-Likelihood: -75.122
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 219.6817 225.578 0.974 0.348 -267.649 707.012
expression -19.3067 34.526 -0.559 0.586 -93.897 55.283
Omnibus: 0.218 Durbin-Watson: 1.730
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.407
Skew: -0.065 Prob(JB): 0.816
Kurtosis: 2.204 Cond. No. 150.