LID SPN TRAP

1441350_at

Fgf3 fibroblast growth factor 3

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
0.294 0.594 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 15.74
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.20e-05
Time: 22:45:57 Log-Likelihood: -98.747
No. Observations: 23 AIC: 205.5
Df Residuals: 19 BIC: 210.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -77.8248 125.681 -0.619 0.543 -340.879 185.229
C(dose)[T.1] 395.2017 172.878 2.286 0.034 33.363 757.041
expression 19.5496 18.590 1.052 0.306 -19.361 58.460
expression:C(dose)[T.1] -50.1453 25.386 -1.975 0.063 -103.279 2.989
Omnibus: 0.147 Durbin-Watson: 2.427
Prob(Omnibus): 0.929 Jarque-Bera (JB): 0.341
Skew: 0.128 Prob(JB): 0.843
Kurtosis: 2.461 Cond. No. 390.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.91
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.45e-05
Time: 22:45:57 Log-Likelihood: -100.90
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.7937 91.691 1.132 0.271 -87.470 295.057
C(dose)[T.1] 54.1041 8.820 6.134 0.000 35.705 72.503
expression -7.3419 13.547 -0.542 0.594 -35.600 20.917
Omnibus: 0.564 Durbin-Watson: 1.859
Prob(Omnibus): 0.754 Jarque-Bera (JB): 0.646
Skew: 0.194 Prob(JB): 0.724
Kurtosis: 2.277 Cond. No. 147.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:45:57 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: 0.003
Model: OLS Adj. R-squared: -0.044
Method: Least Squares F-statistic: 0.07314
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.789
Time: 22:45:57 Log-Likelihood: -113.06
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 38.9615 150.875 0.258 0.799 -274.801 352.724
expression 5.9902 22.150 0.270 0.789 -40.074 52.054
Omnibus: 2.730 Durbin-Watson: 2.486
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.528
Skew: 0.339 Prob(JB): 0.466
Kurtosis: 1.934 Cond. No. 146.

CP101

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

F-statistic p-value df difference
2.664 0.129 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.648
Model: OLS Adj. R-squared: 0.552
Method: Least Squares F-statistic: 6.751
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00758
Time: 22:45:57 Log-Likelihood: -67.469
No. Observations: 15 AIC: 142.9
Df Residuals: 11 BIC: 145.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 47.1392 125.248 0.376 0.714 -228.529 322.808
C(dose)[T.1] -285.4846 189.972 -1.503 0.161 -703.610 132.641
expression 2.8521 17.555 0.162 0.874 -35.785 41.489
expression:C(dose)[T.1] 46.7018 26.537 1.760 0.106 -11.705 105.109
Omnibus: 12.292 Durbin-Watson: 1.315
Prob(Omnibus): 0.002 Jarque-Bera (JB): 8.400
Skew: -1.609 Prob(JB): 0.0150
Kurtosis: 4.756 Cond. No. 273.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.549
Model: OLS Adj. R-squared: 0.474
Method: Least Squares F-statistic: 7.301
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00842
Time: 22:45:57 Log-Likelihood: -69.329
No. Observations: 15 AIC: 144.7
Df Residuals: 12 BIC: 146.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -98.2465 102.036 -0.963 0.355 -320.565 124.071
C(dose)[T.1] 48.0434 14.256 3.370 0.006 16.983 79.104
expression 23.2892 14.269 1.632 0.129 -7.800 54.378
Omnibus: 3.431 Durbin-Watson: 0.851
Prob(Omnibus): 0.180 Jarque-Bera (JB): 1.661
Skew: -0.802 Prob(JB): 0.436
Kurtosis: 3.295 Cond. No. 105.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:45:57 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: 0.122
Model: OLS Adj. R-squared: 0.054
Method: Least Squares F-statistic: 1.806
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.202
Time: 22:45:57 Log-Likelihood: -74.324
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -89.6372 136.729 -0.656 0.524 -385.022 205.747
expression 25.6721 19.103 1.344 0.202 -15.597 66.941
Omnibus: 2.577 Durbin-Watson: 1.788
Prob(Omnibus): 0.276 Jarque-Bera (JB): 1.200
Skew: -0.291 Prob(JB): 0.549
Kurtosis: 1.742 Cond. No. 105.