LID SPN TRAP

1438967_x_at

Amhr2 anti-Mullerian hormone type 2 receptor

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
3.965 0.060 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.749
Model: OLS Adj. R-squared: 0.709
Method: Least Squares F-statistic: 18.89
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.32e-06
Time: 05:27:32 Log-Likelihood: -97.213
No. Observations: 23 AIC: 202.4
Df Residuals: 19 BIC: 207.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 21.5268 39.331 0.547 0.591 -60.794 103.848
C(dose)[T.1] -84.0120 76.135 -1.103 0.284 -243.363 75.339
expression 4.2813 5.106 0.838 0.412 -6.406 14.968
expression:C(dose)[T.1] 17.1653 9.654 1.778 0.091 -3.041 37.371
Omnibus: 1.380 Durbin-Watson: 1.335
Prob(Omnibus): 0.502 Jarque-Bera (JB): 1.013
Skew: 0.234 Prob(JB): 0.603
Kurtosis: 2.085 Cond. No. 189.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.707
Model: OLS Adj. R-squared: 0.678
Method: Least Squares F-statistic: 24.14
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.64e-06
Time: 05:27:32 Log-Likelihood: -98.983
No. Observations: 23 AIC: 204.0
Df Residuals: 20 BIC: 207.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -15.1282 35.259 -0.429 0.672 -88.677 58.421
C(dose)[T.1] 50.6617 8.123 6.236 0.000 33.716 67.607
expression 9.0831 4.562 1.991 0.060 -0.432 18.598
Omnibus: 5.876 Durbin-Watson: 1.483
Prob(Omnibus): 0.053 Jarque-Bera (JB): 1.789
Skew: 0.147 Prob(JB): 0.409
Kurtosis: 1.666 Cond. No. 70.2

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: 05:27:32 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: 0.138
Model: OLS Adj. R-squared: 0.096
Method: Least Squares F-statistic: 3.350
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0815
Time: 05:27:32 Log-Likelihood: -111.40
No. Observations: 23 AIC: 226.8
Df Residuals: 21 BIC: 229.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -27.4788 58.953 -0.466 0.646 -150.079 95.121
expression 13.7883 7.534 1.830 0.081 -1.879 29.456
Omnibus: 3.291 Durbin-Watson: 2.528
Prob(Omnibus): 0.193 Jarque-Bera (JB): 1.335
Skew: 0.013 Prob(JB): 0.513
Kurtosis: 1.820 Cond. No. 69.9

CP101

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

F-statistic p-value df difference
2.168 0.167 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.605
Model: OLS Adj. R-squared: 0.497
Method: Least Squares F-statistic: 5.616
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0139
Time: 05:27:32 Log-Likelihood: -68.333
No. Observations: 15 AIC: 144.7
Df Residuals: 11 BIC: 147.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 59.9504 54.457 1.101 0.294 -59.908 179.809
C(dose)[T.1] -92.9402 90.686 -1.025 0.327 -292.538 106.657
expression 1.3818 9.885 0.140 0.891 -20.376 23.139
expression:C(dose)[T.1] 20.3433 14.377 1.415 0.185 -11.299 51.986
Omnibus: 2.410 Durbin-Watson: 0.643
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.676
Skew: -0.793 Prob(JB): 0.433
Kurtosis: 2.588 Cond. No. 111.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.533
Model: OLS Adj. R-squared: 0.455
Method: Least Squares F-statistic: 6.851
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0104
Time: 05:27:32 Log-Likelihood: -69.588
No. Observations: 15 AIC: 145.2
Df Residuals: 12 BIC: 147.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 7.8960 41.797 0.189 0.853 -83.171 98.963
C(dose)[T.1] 32.9751 18.200 1.812 0.095 -6.678 72.629
expression 11.0000 7.471 1.472 0.167 -5.279 27.279
Omnibus: 4.140 Durbin-Watson: 0.909
Prob(Omnibus): 0.126 Jarque-Bera (JB): 1.950
Skew: -0.588 Prob(JB): 0.377
Kurtosis: 1.682 Cond. No. 38.5

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: 05:27:32 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: 0.405
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 8.863
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0107
Time: 05:27:32 Log-Likelihood: -71.401
No. Observations: 15 AIC: 146.8
Df Residuals: 13 BIC: 148.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.3157 40.727 -0.622 0.545 -113.302 62.671
expression 19.1951 6.448 2.977 0.011 5.266 33.125
Omnibus: 1.333 Durbin-Watson: 1.289
Prob(Omnibus): 0.513 Jarque-Bera (JB): 1.107
Skew: -0.522 Prob(JB): 0.575
Kurtosis: 2.176 Cond. No. 33.6