LID SPN TRAP

1441814_s_at

Rpain RPA interacting protein

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
0.515 0.481 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 13.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.56e-05
Time: 03:50:42 Log-Likelihood: -100.10
No. Observations: 23 AIC: 208.2
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -222.8875 219.685 -1.015 0.323 -682.693 236.918
C(dose)[T.1] 388.4268 310.517 1.251 0.226 -261.493 1038.347
expression 31.3903 24.877 1.262 0.222 -20.679 83.459
expression:C(dose)[T.1] -38.1509 35.677 -1.069 0.298 -112.823 36.521
Omnibus: 0.463 Durbin-Watson: 2.104
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.587
Skew: -0.198 Prob(JB): 0.746
Kurtosis: 2.325 Cond. No. 819.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 19.23
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.20e-05
Time: 03:50:42 Log-Likelihood: -100.77
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -59.1376 158.084 -0.374 0.712 -388.895 270.620
C(dose)[T.1] 56.5369 9.740 5.805 0.000 36.220 76.854
expression 12.8402 17.895 0.718 0.481 -24.489 50.169
Omnibus: 0.631 Durbin-Watson: 1.959
Prob(Omnibus): 0.729 Jarque-Bera (JB): 0.643
Skew: -0.077 Prob(JB): 0.725
Kurtosis: 2.195 Cond. No. 323.

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: 03:50:42 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: 0.081
Model: OLS Adj. R-squared: 0.038
Method: Least Squares F-statistic: 1.863
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.187
Time: 03:50:42 Log-Likelihood: -112.13
No. Observations: 23 AIC: 228.3
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 382.0738 221.640 1.724 0.099 -78.851 842.999
expression -34.7207 25.439 -1.365 0.187 -87.625 18.183
Omnibus: 2.279 Durbin-Watson: 2.302
Prob(Omnibus): 0.320 Jarque-Bera (JB): 1.249
Skew: 0.218 Prob(JB): 0.535
Kurtosis: 1.945 Cond. No. 283.

CP101

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

F-statistic p-value df difference
2.156 0.168 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.533
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 4.181
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0334
Time: 03:50:42 Log-Likelihood: -69.593
No. Observations: 15 AIC: 147.2
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -306.2145 330.302 -0.927 0.374 -1033.204 420.775
C(dose)[T.1] 44.1983 561.468 0.079 0.939 -1191.585 1279.981
expression 39.8570 35.214 1.132 0.282 -37.648 117.363
expression:C(dose)[T.1] 1.4246 60.738 0.023 0.982 -132.260 135.109
Omnibus: 1.634 Durbin-Watson: 0.855
Prob(Omnibus): 0.442 Jarque-Bera (JB): 1.213
Skew: -0.648 Prob(JB): 0.545
Kurtosis: 2.487 Cond. No. 864.

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: 6.840
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0104
Time: 03:50:42 Log-Likelihood: -69.594
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 -310.7036 257.746 -1.205 0.251 -872.285 250.878
C(dose)[T.1] 57.3621 15.522 3.696 0.003 23.542 91.182
expression 40.3359 27.471 1.468 0.168 -19.518 100.190
Omnibus: 1.656 Durbin-Watson: 0.851
Prob(Omnibus): 0.437 Jarque-Bera (JB): 1.226
Skew: -0.652 Prob(JB): 0.542
Kurtosis: 2.490 Cond. No. 335.

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: 03:50:42 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.01210
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.914
Time: 03:50:42 Log-Likelihood: -75.293
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 56.9423 334.036 0.170 0.867 -664.699 778.583
expression 3.9631 36.031 0.110 0.914 -73.876 81.802
Omnibus: 0.892 Durbin-Watson: 1.641
Prob(Omnibus): 0.640 Jarque-Bera (JB): 0.683
Skew: 0.082 Prob(JB): 0.711
Kurtosis: 1.967 Cond. No. 308.