LID SPN TRAP

1441914_x_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
0.059 0.810 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 13.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.84e-05
Time: 03:39:30 Log-Likelihood: -100.15
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -12.3366 84.084 -0.147 0.885 -188.326 163.652
C(dose)[T.1] 189.2953 110.630 1.711 0.103 -42.256 420.847
expression 11.0124 13.880 0.793 0.437 -18.038 40.063
expression:C(dose)[T.1] -22.0307 17.942 -1.228 0.234 -59.583 15.522
Omnibus: 0.181 Durbin-Watson: 1.937
Prob(Omnibus): 0.914 Jarque-Bera (JB): 0.041
Skew: -0.075 Prob(JB): 0.980
Kurtosis: 2.858 Cond. No. 220.

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: 18.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.75e-05
Time: 03:39:30 Log-Likelihood: -101.03
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 67.3326 54.157 1.243 0.228 -45.637 180.302
C(dose)[T.1] 53.8955 9.051 5.954 0.000 35.015 72.776
expression -2.1719 8.906 -0.244 0.810 -20.750 16.406
Omnibus: 0.309 Durbin-Watson: 1.866
Prob(Omnibus): 0.857 Jarque-Bera (JB): 0.477
Skew: 0.050 Prob(JB): 0.788
Kurtosis: 2.302 Cond. No. 78.9

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: 03:39:31 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: 0.030
Model: OLS Adj. R-squared: -0.016
Method: Least Squares F-statistic: 0.6447
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.431
Time: 03:39:31 Log-Likelihood: -112.76
No. Observations: 23 AIC: 229.5
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 10.3956 86.625 0.120 0.906 -169.751 190.542
expression 11.2432 14.002 0.803 0.431 -17.876 40.362
Omnibus: 1.845 Durbin-Watson: 2.427
Prob(Omnibus): 0.398 Jarque-Bera (JB): 1.483
Skew: 0.462 Prob(JB): 0.476
Kurtosis: 2.167 Cond. No. 77.3

CP101

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

F-statistic p-value df difference
4.272 0.061 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.615
Model: OLS Adj. R-squared: 0.510
Method: Least Squares F-statistic: 5.860
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0121
Time: 03:39:31 Log-Likelihood: -68.139
No. Observations: 15 AIC: 144.3
Df Residuals: 11 BIC: 147.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -20.9181 114.529 -0.183 0.858 -272.994 231.158
C(dose)[T.1] -66.4953 152.403 -0.436 0.671 -401.932 268.941
expression 13.3427 17.230 0.774 0.455 -24.581 51.267
expression:C(dose)[T.1] 18.2048 23.162 0.786 0.448 -32.774 69.184
Omnibus: 1.850 Durbin-Watson: 1.206
Prob(Omnibus): 0.397 Jarque-Bera (JB): 1.377
Skew: -0.692 Prob(JB): 0.502
Kurtosis: 2.464 Cond. No. 203.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.593
Model: OLS Adj. R-squared: 0.526
Method: Least Squares F-statistic: 8.760
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00451
Time: 03:39:31 Log-Likelihood: -68.549
No. Observations: 15 AIC: 143.1
Df Residuals: 12 BIC: 145.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -87.6266 75.665 -1.158 0.269 -252.486 77.233
C(dose)[T.1] 52.7954 13.628 3.874 0.002 23.102 82.489
expression 23.4175 11.330 2.067 0.061 -1.268 48.103
Omnibus: 1.816 Durbin-Watson: 1.070
Prob(Omnibus): 0.403 Jarque-Bera (JB): 1.380
Skew: -0.684 Prob(JB): 0.502
Kurtosis: 2.421 Cond. No. 75.6

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: 03:39:31 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: 0.085
Model: OLS Adj. R-squared: 0.015
Method: Least Squares F-statistic: 1.209
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.291
Time: 03:39:31 Log-Likelihood: -74.633
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -22.7973 106.359 -0.214 0.834 -252.573 206.978
expression 17.8097 16.196 1.100 0.291 -17.181 52.800
Omnibus: 0.460 Durbin-Watson: 2.105
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.523
Skew: 0.008 Prob(JB): 0.770
Kurtosis: 2.085 Cond. No. 73.5