LID SPN TRAP

1432332_a_at

Nudt19 nudix (nucleoside diphosphate linked moiety X)-type motif 19

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
0.004 0.953 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.685
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 13.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.20e-05
Time: 03:41:56 Log-Likelihood: -99.811
No. Observations: 23 AIC: 207.6
Df Residuals: 19 BIC: 212.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -19.0480 121.887 -0.156 0.877 -274.161 236.065
C(dose)[T.1] 540.0596 329.690 1.638 0.118 -149.990 1230.110
expression 9.7337 16.176 0.602 0.554 -24.124 43.591
expression:C(dose)[T.1] -64.8058 43.884 -1.477 0.156 -156.657 27.045
Omnibus: 1.843 Durbin-Watson: 1.929
Prob(Omnibus): 0.398 Jarque-Bera (JB): 1.197
Skew: 0.272 Prob(JB): 0.550
Kurtosis: 2.024 Cond. No. 678.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 03:41:56 Log-Likelihood: -101.06
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 47.2241 116.622 0.405 0.690 -196.046 290.494
C(dose)[T.1] 53.3541 8.774 6.081 0.000 35.053 71.656
expression 0.9280 15.475 0.060 0.953 -31.352 33.208
Omnibus: 0.344 Durbin-Watson: 1.900
Prob(Omnibus): 0.842 Jarque-Bera (JB): 0.497
Skew: 0.053 Prob(JB): 0.780
Kurtosis: 2.287 Cond. No. 204.

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: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 03:41:56 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.006889
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.935
Time: 03:41:56 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 95.6132 191.656 0.499 0.623 -302.958 494.184
expression -2.1146 25.477 -0.083 0.935 -55.098 50.868
Omnibus: 3.312 Durbin-Watson: 2.462
Prob(Omnibus): 0.191 Jarque-Bera (JB): 1.556
Skew: 0.280 Prob(JB): 0.459
Kurtosis: 1.855 Cond. No. 203.

CP101

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

F-statistic p-value df difference
0.002 0.962 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.519
Model: OLS Adj. R-squared: 0.388
Method: Least Squares F-statistic: 3.955
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0388
Time: 03:41:56 Log-Likelihood: -69.812
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -331.1119 410.465 -0.807 0.437 -1234.539 572.315
C(dose)[T.1] 708.8546 521.274 1.360 0.201 -438.462 1856.171
expression 53.3345 54.910 0.971 0.352 -67.521 174.190
expression:C(dose)[T.1] -87.8068 69.373 -1.266 0.232 -240.496 64.882
Omnibus: 4.874 Durbin-Watson: 1.203
Prob(Omnibus): 0.087 Jarque-Bera (JB): 2.500
Skew: -0.964 Prob(JB): 0.287
Kurtosis: 3.530 Cond. No. 734.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.887
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 03:41:56 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 79.9533 257.232 0.311 0.761 -480.508 640.415
C(dose)[T.1] 49.3678 16.126 3.061 0.010 14.233 84.503
expression -1.6761 34.390 -0.049 0.962 -76.605 73.252
Omnibus: 2.704 Durbin-Watson: 0.808
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.844
Skew: -0.840 Prob(JB): 0.398
Kurtosis: 2.637 Cond. No. 252.

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: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 03:41:56 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: 0.018
Model: OLS Adj. R-squared: -0.057
Method: Least Squares F-statistic: 0.2444
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.629
Time: 03:41:56 Log-Likelihood: -75.160
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -66.4560 324.071 -0.205 0.841 -766.568 633.656
expression 21.2731 43.034 0.494 0.629 -71.696 114.242
Omnibus: 0.978 Durbin-Watson: 1.584
Prob(Omnibus): 0.613 Jarque-Bera (JB): 0.733
Skew: 0.159 Prob(JB): 0.693
Kurtosis: 1.965 Cond. No. 247.