LID SPN TRAP

1419070_at

Cys1 cystin 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
2.522 0.128 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.641
Method: Least Squares F-statistic: 14.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.54e-05
Time: 03:38:18 Log-Likelihood: -99.644
No. Observations: 23 AIC: 207.3
Df Residuals: 19 BIC: 211.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 88.5139 189.260 0.468 0.645 -307.612 484.640
C(dose)[T.1] 100.1058 196.282 0.510 0.616 -310.717 510.928
expression -4.4342 24.451 -0.181 0.858 -55.611 46.743
expression:C(dose)[T.1] -7.5944 25.625 -0.296 0.770 -61.228 46.039
Omnibus: 0.429 Durbin-Watson: 2.046
Prob(Omnibus): 0.807 Jarque-Bera (JB): 0.555
Skew: 0.128 Prob(JB): 0.758
Kurtosis: 2.283 Cond. No. 524.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.688
Model: OLS Adj. R-squared: 0.657
Method: Least Squares F-statistic: 22.09
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.64e-06
Time: 03:38:18 Log-Likelihood: -99.697
No. Observations: 23 AIC: 205.4
Df Residuals: 20 BIC: 208.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.0104 55.582 2.555 0.019 26.068 257.953
C(dose)[T.1] 42.0281 10.909 3.853 0.001 19.272 64.784
expression -11.3489 7.146 -1.588 0.128 -26.256 3.558
Omnibus: 0.535 Durbin-Watson: 2.089
Prob(Omnibus): 0.765 Jarque-Bera (JB): 0.608
Skew: 0.116 Prob(JB): 0.738
Kurtosis: 2.238 Cond. No. 101.

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: 03:38:18 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: 0.457
Model: OLS Adj. R-squared: 0.431
Method: Least Squares F-statistic: 17.68
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000398
Time: 03:38:18 Log-Likelihood: -106.08
No. Observations: 23 AIC: 216.2
Df Residuals: 21 BIC: 218.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 292.5854 50.904 5.748 0.000 186.724 398.447
expression -29.3206 6.973 -4.205 0.000 -43.822 -14.819
Omnibus: 0.706 Durbin-Watson: 2.274
Prob(Omnibus): 0.703 Jarque-Bera (JB): 0.704
Skew: 0.356 Prob(JB): 0.703
Kurtosis: 2.522 Cond. No. 71.2

CP101

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

F-statistic p-value df difference
0.243 0.631 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.317
Method: Least Squares F-statistic: 3.166
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0678
Time: 03:38:18 Log-Likelihood: -70.632
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 64.0836 360.786 0.178 0.862 -730.000 858.168
C(dose)[T.1] 165.7917 416.150 0.398 0.698 -750.148 1081.732
expression 0.4382 47.235 0.009 0.993 -103.524 104.401
expression:C(dose)[T.1] -14.7811 54.028 -0.274 0.789 -133.697 104.135
Omnibus: 2.267 Durbin-Watson: 0.890
Prob(Omnibus): 0.322 Jarque-Bera (JB): 1.714
Skew: -0.702 Prob(JB): 0.424
Kurtosis: 2.120 Cond. No. 609.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 5.105
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0249
Time: 03:38:18 Log-Likelihood: -70.683
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 150.3290 168.557 0.892 0.390 -216.926 517.584
C(dose)[T.1] 52.0400 16.616 3.132 0.009 15.837 88.243
expression -10.8593 22.029 -0.493 0.631 -58.857 37.138
Omnibus: 2.096 Durbin-Watson: 0.797
Prob(Omnibus): 0.351 Jarque-Bera (JB): 1.573
Skew: -0.647 Prob(JB): 0.455
Kurtosis: 2.083 Cond. No. 172.

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: 03:38:18 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: 0.018
Model: OLS Adj. R-squared: -0.057
Method: Least Squares F-statistic: 0.2394
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.633
Time: 03:38:18 Log-Likelihood: -75.163
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -8.1103 208.254 -0.039 0.970 -458.017 441.796
expression 13.0925 26.758 0.489 0.633 -44.715 70.900
Omnibus: 0.291 Durbin-Watson: 1.594
Prob(Omnibus): 0.864 Jarque-Bera (JB): 0.449
Skew: -0.091 Prob(JB): 0.799
Kurtosis: 2.172 Cond. No. 164.