LID SPN TRAP

1422705_at

Pmepa1 prostate transmembrane protein, androgen induced 1

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
0.148 0.704 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000128
Time: 04:33:21 Log-Likelihood: -100.93
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -96.7849 316.296 -0.306 0.763 -758.801 565.231
C(dose)[T.1] 207.8596 539.425 0.385 0.704 -921.170 1336.889
expression 15.5098 32.483 0.477 0.638 -52.479 83.498
expression:C(dose)[T.1] -15.8578 53.947 -0.294 0.772 -128.771 97.055
Omnibus: 0.207 Durbin-Watson: 1.833
Prob(Omnibus): 0.902 Jarque-Bera (JB): 0.408
Skew: 0.100 Prob(JB): 0.816
Kurtosis: 2.379 Cond. No. 1.48e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.63e-05
Time: 04:33:21 Log-Likelihood: -100.98
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 -40.8124 246.722 -0.165 0.870 -555.465 473.840
C(dose)[T.1] 49.3485 13.548 3.643 0.002 21.089 77.608
expression 9.7604 25.335 0.385 0.704 -43.088 62.609
Omnibus: 0.130 Durbin-Watson: 1.857
Prob(Omnibus): 0.937 Jarque-Bera (JB): 0.352
Skew: 0.020 Prob(JB): 0.839
Kurtosis: 2.395 Cond. No. 569.

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: 04:33:21 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: 0.421
Model: OLS Adj. R-squared: 0.393
Method: Least Squares F-statistic: 15.24
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000817
Time: 04:33:21 Log-Likelihood: -106.83
No. Observations: 23 AIC: 217.7
Df Residuals: 21 BIC: 219.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -717.6056 204.313 -3.512 0.002 -1142.498 -292.713
expression 80.2882 20.566 3.904 0.001 37.518 123.058
Omnibus: 3.210 Durbin-Watson: 1.750
Prob(Omnibus): 0.201 Jarque-Bera (JB): 1.330
Skew: -0.060 Prob(JB): 0.514
Kurtosis: 1.828 Cond. No. 373.

CP101

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

F-statistic p-value df difference
8.251 0.014 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.674
Model: OLS Adj. R-squared: 0.585
Method: Least Squares F-statistic: 7.578
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00505
Time: 04:33:21 Log-Likelihood: -66.895
No. Observations: 15 AIC: 141.8
Df Residuals: 11 BIC: 144.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -542.1162 356.855 -1.519 0.157 -1327.548 243.316
C(dose)[T.1] 86.9643 444.225 0.196 0.848 -890.768 1064.697
expression 63.8475 37.367 1.709 0.116 -18.396 146.091
expression:C(dose)[T.1] -6.2985 45.875 -0.137 0.893 -107.269 94.672
Omnibus: 3.044 Durbin-Watson: 1.190
Prob(Omnibus): 0.218 Jarque-Bera (JB): 1.460
Skew: -0.756 Prob(JB): 0.482
Kurtosis: 3.223 Cond. No. 994.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.37
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00121
Time: 04:33:21 Log-Likelihood: -66.908
No. Observations: 15 AIC: 139.8
Df Residuals: 12 BIC: 141.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -502.2221 198.507 -2.530 0.026 -934.733 -69.712
C(dose)[T.1] 26.0099 14.559 1.787 0.099 -5.710 57.730
expression 59.6688 20.772 2.873 0.014 14.410 104.928
Omnibus: 3.606 Durbin-Watson: 1.155
Prob(Omnibus): 0.165 Jarque-Bera (JB): 1.715
Skew: -0.808 Prob(JB): 0.424
Kurtosis: 3.367 Cond. No. 325.

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: 04:33:21 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: 0.586
Model: OLS Adj. R-squared: 0.555
Method: Least Squares F-statistic: 18.44
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000873
Time: 04:33:21 Log-Likelihood: -68.677
No. Observations: 15 AIC: 141.4
Df Residuals: 13 BIC: 142.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -689.0501 182.400 -3.778 0.002 -1083.101 -294.999
expression 80.2446 18.688 4.294 0.001 39.872 120.617
Omnibus: 0.956 Durbin-Watson: 1.400
Prob(Omnibus): 0.620 Jarque-Bera (JB): 0.586
Skew: -0.460 Prob(JB): 0.746
Kurtosis: 2.699 Cond. No. 275.