LID SPN TRAP

1436790_a_at

Sox11 SRY-box containing gene 11

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
0.078 0.783 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.605
Method: Least Squares F-statistic: 12.22
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000110
Time: 05:26:15 Log-Likelihood: -100.74
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -0.3736 134.561 -0.003 0.998 -282.012 281.265
C(dose)[T.1] 162.5808 162.078 1.003 0.328 -176.652 501.814
expression 9.0189 22.211 0.406 0.689 -37.470 55.507
expression:C(dose)[T.1] -18.1223 26.807 -0.676 0.507 -74.229 37.985
Omnibus: 0.140 Durbin-Watson: 1.836
Prob(Omnibus): 0.932 Jarque-Bera (JB): 0.358
Skew: -0.058 Prob(JB): 0.836
Kurtosis: 2.400 Cond. No. 320.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.73e-05
Time: 05:26:15 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 74.9215 74.477 1.006 0.326 -80.436 230.279
C(dose)[T.1] 53.1751 8.772 6.062 0.000 34.877 71.473
expression -3.4226 12.266 -0.279 0.783 -29.008 22.163
Omnibus: 0.355 Durbin-Watson: 1.869
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.504
Skew: 0.057 Prob(JB): 0.777
Kurtosis: 2.284 Cond. No. 106.

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: 05:26:15 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: 0.008
Model: OLS Adj. R-squared: -0.039
Method: Least Squares F-statistic: 0.1720
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.683
Time: 05:26:15 Log-Likelihood: -113.01
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.0250 121.514 1.070 0.297 -122.676 382.726
expression -8.3439 20.119 -0.415 0.683 -50.183 33.495
Omnibus: 3.884 Durbin-Watson: 2.486
Prob(Omnibus): 0.143 Jarque-Bera (JB): 1.621
Skew: 0.252 Prob(JB): 0.445
Kurtosis: 1.801 Cond. No. 105.

CP101

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

F-statistic p-value df difference
0.002 0.961 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.572
Method: Least Squares F-statistic: 7.248
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00592
Time: 05:26:15 Log-Likelihood: -67.119
No. Observations: 15 AIC: 142.2
Df Residuals: 11 BIC: 145.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 440.2119 192.130 2.291 0.043 17.336 863.088
C(dose)[T.1] -671.7513 272.038 -2.469 0.031 -1270.503 -73.000
expression -63.6302 32.756 -1.943 0.078 -135.725 8.464
expression:C(dose)[T.1] 116.3644 43.841 2.654 0.022 19.870 212.859
Omnibus: 1.106 Durbin-Watson: 0.981
Prob(Omnibus): 0.575 Jarque-Bera (JB): 0.867
Skew: -0.323 Prob(JB): 0.648
Kurtosis: 2.015 Cond. No. 373.

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: 05:26:15 Log-Likelihood: -70.831
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 59.6582 156.834 0.380 0.710 -282.053 401.369
C(dose)[T.1] 48.2101 25.335 1.903 0.081 -6.990 103.410
expression 1.3263 26.698 0.050 0.961 -56.843 59.496
Omnibus: 2.701 Durbin-Watson: 0.801
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.892
Skew: -0.844 Prob(JB): 0.388
Kurtosis: 2.583 Cond. No. 130.

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: 05:26:15 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: 0.283
Model: OLS Adj. R-squared: 0.227
Method: Least Squares F-statistic: 5.121
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0414
Time: 05:26:15 Log-Likelihood: -72.809
No. Observations: 15 AIC: 149.6
Df Residuals: 13 BIC: 151.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -163.6649 114.044 -1.435 0.175 -410.042 82.713
expression 41.1388 18.180 2.263 0.041 1.864 80.414
Omnibus: 2.951 Durbin-Watson: 1.055
Prob(Omnibus): 0.229 Jarque-Bera (JB): 1.544
Skew: -0.785 Prob(JB): 0.462
Kurtosis: 3.082 Cond. No. 85.2