LID SPN TRAP

1416776_at

Crym crystallin, mu

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
0.051 0.824 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 13.43
Date: Sun, 10 Nov 2024 Prob (F-statistic): 6.15e-05
Time: 14:23:29 Log-Likelihood: -100.02
No. Observations: 23 AIC: 208.0
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -178.9679 221.724 -0.807 0.430 -643.041 285.105
C(dose)[T.1] 490.1603 329.480 1.488 0.153 -199.450 1179.771
expression 21.4057 20.347 1.052 0.306 -21.181 63.992
expression:C(dose)[T.1] -40.5159 30.606 -1.324 0.201 -104.574 23.542
Omnibus: 1.170 Durbin-Watson: 1.580
Prob(Omnibus): 0.557 Jarque-Bera (JB): 0.994
Skew: -0.468 Prob(JB): 0.608
Kurtosis: 2.598 Cond. No. 1.05e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Sun, 10 Nov 2024 Prob (F-statistic): 2.76e-05
Time: 14:23:29 Log-Likelihood: -101.03
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 16.0982 168.765 0.095 0.925 -335.939 368.135
C(dose)[T.1] 54.1655 9.495 5.705 0.000 34.359 73.972
expression 3.4985 15.483 0.226 0.824 -28.798 35.795
Omnibus: 0.464 Durbin-Watson: 1.825
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.562
Skew: 0.059 Prob(JB): 0.755
Kurtosis: 2.243 Cond. No. 420.

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: Sun, 10 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 14:23:29 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: 0.080
Model: OLS Adj. R-squared: 0.037
Method: Least Squares F-statistic: 1.835
Date: Sun, 10 Nov 2024 Prob (F-statistic): 0.190
Time: 14:23:29 Log-Likelihood: -112.14
No. Observations: 23 AIC: 228.3
Df Residuals: 21 BIC: 230.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 409.6174 243.632 1.681 0.108 -97.043 916.277
expression -30.6031 22.591 -1.355 0.190 -77.584 16.378
Omnibus: 5.961 Durbin-Watson: 2.549
Prob(Omnibus): 0.051 Jarque-Bera (JB): 1.751
Skew: 0.078 Prob(JB): 0.417
Kurtosis: 1.657 Cond. No. 383.

CP101

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

F-statistic p-value df difference
0.332 0.575 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.555
Model: OLS Adj. R-squared: 0.433
Method: Least Squares F-statistic: 4.569
Date: Sun, 10 Nov 2024 Prob (F-statistic): 0.0260
Time: 14:23:29 Log-Likelihood: -69.231
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 641.0639 384.670 1.667 0.124 -205.590 1487.717
C(dose)[T.1] -774.1775 553.412 -1.399 0.189 -1992.229 443.874
expression -58.7627 39.390 -1.492 0.164 -145.459 27.934
expression:C(dose)[T.1] 83.3301 55.519 1.501 0.162 -38.867 205.527
Omnibus: 1.187 Durbin-Watson: 1.462
Prob(Omnibus): 0.552 Jarque-Bera (JB): 0.679
Skew: -0.507 Prob(JB): 0.712
Kurtosis: 2.760 Cond. No. 1.01e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.464
Model: OLS Adj. R-squared: 0.374
Method: Least Squares F-statistic: 5.186
Date: Sun, 10 Nov 2024 Prob (F-statistic): 0.0238
Time: 14:23:29 Log-Likelihood: -70.628
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 231.6010 285.000 0.813 0.432 -389.361 852.563
C(dose)[T.1] 55.9833 19.485 2.873 0.014 13.530 98.437
expression -16.8177 29.172 -0.576 0.575 -80.378 46.743
Omnibus: 3.960 Durbin-Watson: 0.876
Prob(Omnibus): 0.138 Jarque-Bera (JB): 2.461
Skew: -0.992 Prob(JB): 0.292
Kurtosis: 2.940 Cond. No. 372.

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: Sun, 10 Nov 2024 Prob (F-statistic): 0.00629
Time: 14:23:30 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: 0.095
Model: OLS Adj. R-squared: 0.025
Method: Least Squares F-statistic: 1.359
Date: Sun, 10 Nov 2024 Prob (F-statistic): 0.265
Time: 14:23:30 Log-Likelihood: -74.554
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -243.8015 289.655 -0.842 0.415 -869.562 381.959
expression 33.8242 29.016 1.166 0.265 -28.860 96.509
Omnibus: 0.159 Durbin-Watson: 1.316
Prob(Omnibus): 0.924 Jarque-Bera (JB): 0.188
Skew: -0.178 Prob(JB): 0.910
Kurtosis: 2.584 Cond. No. 302.