LID SPN TRAP

1421365_at

Fst follistatin

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
0.005 0.943 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000112
Time: 03:41:09 Log-Likelihood: -100.76
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 104.1870 98.879 1.054 0.305 -102.769 311.143
C(dose)[T.1] -53.8272 150.787 -0.357 0.725 -369.429 261.774
expression -8.1105 16.015 -0.506 0.618 -41.630 25.409
expression:C(dose)[T.1] 17.9099 25.216 0.710 0.486 -34.867 70.687
Omnibus: 0.825 Durbin-Watson: 1.722
Prob(Omnibus): 0.662 Jarque-Bera (JB): 0.721
Skew: 0.070 Prob(JB): 0.697
Kurtosis: 2.144 Cond. No. 261.

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: 03:41:09 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 59.6681 75.521 0.790 0.439 -97.867 217.203
C(dose)[T.1] 53.0478 9.633 5.507 0.000 32.953 73.143
expression -0.8860 12.216 -0.073 0.943 -26.368 24.596
Omnibus: 0.311 Durbin-Watson: 1.889
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.478
Skew: 0.049 Prob(JB): 0.787
Kurtosis: 2.301 Cond. No. 107.

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: 03:41:09 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: 0.117
Model: OLS Adj. R-squared: 0.075
Method: Least Squares F-statistic: 2.788
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.110
Time: 03:41:09 Log-Likelihood: -111.67
No. Observations: 23 AIC: 227.3
Df Residuals: 21 BIC: 229.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 252.3366 103.605 2.436 0.024 36.879 467.794
expression -28.7410 17.213 -1.670 0.110 -64.538 7.056
Omnibus: 3.199 Durbin-Watson: 2.288
Prob(Omnibus): 0.202 Jarque-Bera (JB): 1.491
Skew: 0.250 Prob(JB): 0.475
Kurtosis: 1.857 Cond. No. 94.5

CP101

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

F-statistic p-value df difference
0.477 0.503 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.646
Model: OLS Adj. R-squared: 0.549
Method: Least Squares F-statistic: 6.682
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00784
Time: 03:41:09 Log-Likelihood: -67.518
No. Observations: 15 AIC: 143.0
Df Residuals: 11 BIC: 145.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -57.4794 116.827 -0.492 0.632 -314.615 199.656
C(dose)[T.1] 474.6616 178.693 2.656 0.022 81.360 867.963
expression 19.7782 18.436 1.073 0.306 -20.799 60.355
expression:C(dose)[T.1] -61.1177 26.158 -2.337 0.039 -118.690 -3.545
Omnibus: 1.203 Durbin-Watson: 0.914
Prob(Omnibus): 0.548 Jarque-Bera (JB): 0.772
Skew: 0.082 Prob(JB): 0.680
Kurtosis: 1.901 Cond. No. 253.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.382
Method: Least Squares F-statistic: 5.318
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0222
Time: 03:41:09 Log-Likelihood: -70.540
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 134.2553 97.388 1.379 0.193 -77.935 346.445
C(dose)[T.1] 59.3023 21.266 2.789 0.016 12.967 105.637
expression -10.5815 15.317 -0.691 0.503 -43.954 22.791
Omnibus: 2.313 Durbin-Watson: 0.990
Prob(Omnibus): 0.315 Jarque-Bera (JB): 1.621
Skew: -0.776 Prob(JB): 0.445
Kurtosis: 2.572 Cond. No. 89.8

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: 03:41:09 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: 0.126
Model: OLS Adj. R-squared: 0.059
Method: Least Squares F-statistic: 1.880
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.194
Time: 03:41:09 Log-Likelihood: -74.287
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.6341 94.064 -0.368 0.719 -237.846 168.578
expression 18.7992 13.712 1.371 0.194 -10.824 48.423
Omnibus: 0.694 Durbin-Watson: 1.036
Prob(Omnibus): 0.707 Jarque-Bera (JB): 0.620
Skew: 0.091 Prob(JB): 0.733
Kurtosis: 2.021 Cond. No. 69.4