LID SPN TRAP

1460371_at

Hspa12b heat shock protein 12B

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
0.003 0.956 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.81
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.000136
Time: 11:39:37 Log-Likelihood: -101.00
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 47.1291 68.528 0.688 0.500 -96.301 190.560
C(dose)[T.1] 98.0098 145.303 0.675 0.508 -206.112 402.132
expression 1.0290 9.920 0.104 0.918 -19.734 21.792
expression:C(dose)[T.1] -6.1726 20.123 -0.307 0.762 -48.291 35.946
Omnibus: 0.092 Durbin-Watson: 1.867
Prob(Omnibus): 0.955 Jarque-Bera (JB): 0.318
Skew: 0.013 Prob(JB): 0.853
Kurtosis: 2.425 Cond. No. 277.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Tue, 03 Dec 2024 Prob (F-statistic): 2.83e-05
Time: 11:39:37 Log-Likelihood: -101.06
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 57.4487 58.333 0.985 0.336 -64.232 179.130
C(dose)[T.1] 53.5393 9.487 5.644 0.000 33.750 73.328
expression -0.4710 8.433 -0.056 0.956 -18.062 17.120
Omnibus: 0.271 Durbin-Watson: 1.886
Prob(Omnibus): 0.873 Jarque-Bera (JB): 0.453
Skew: 0.057 Prob(JB): 0.797
Kurtosis: 2.322 Cond. No. 96.9

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: Tue, 03 Dec 2024 Prob (F-statistic): 3.51e-06
Time: 11:39:37 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: 0.090
Model: OLS Adj. R-squared: 0.047
Method: Least Squares F-statistic: 2.085
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.164
Time: 11:39:37 Log-Likelihood: -112.02
No. Observations: 23 AIC: 228.0
Df Residuals: 21 BIC: 230.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -45.5863 87.056 -0.524 0.606 -226.629 135.457
expression 17.6860 12.249 1.444 0.164 -7.787 43.159
Omnibus: 3.920 Durbin-Watson: 2.679
Prob(Omnibus): 0.141 Jarque-Bera (JB): 1.760
Skew: 0.331 Prob(JB): 0.415
Kurtosis: 1.818 Cond. No. 91.7

CP101

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

F-statistic p-value df difference
0.348 0.566 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.481
Model: OLS Adj. R-squared: 0.340
Method: Least Squares F-statistic: 3.401
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0571
Time: 11:39:37 Log-Likelihood: -70.378
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -77.9481 183.726 -0.424 0.680 -482.326 326.430
C(dose)[T.1] 173.7660 202.828 0.857 0.410 -272.656 620.188
expression 20.2427 25.531 0.793 0.445 -35.951 76.436
expression:C(dose)[T.1] -17.1178 28.561 -0.599 0.561 -79.979 45.744
Omnibus: 1.697 Durbin-Watson: 1.172
Prob(Omnibus): 0.428 Jarque-Bera (JB): 1.208
Skew: -0.658 Prob(JB): 0.547
Kurtosis: 2.551 Cond. No. 272.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.464
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 5.200
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0236
Time: 11:39:37 Log-Likelihood: -70.619
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 20.2888 80.758 0.251 0.806 -155.669 196.246
C(dose)[T.1] 52.6311 16.574 3.175 0.008 16.519 88.743
expression 6.5639 11.134 0.590 0.566 -17.695 30.822
Omnibus: 1.323 Durbin-Watson: 1.073
Prob(Omnibus): 0.516 Jarque-Bera (JB): 0.837
Skew: -0.557 Prob(JB): 0.658
Kurtosis: 2.688 Cond. No. 74.4

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: Tue, 03 Dec 2024 Prob (F-statistic): 0.00629
Time: 11:39:37 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: 0.014
Model: OLS Adj. R-squared: -0.062
Method: Least Squares F-statistic: 0.1863
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.673
Time: 11:39:38 Log-Likelihood: -75.193
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 134.1422 94.315 1.422 0.178 -69.613 337.897
expression -5.8638 13.585 -0.432 0.673 -35.213 23.485
Omnibus: 0.524 Durbin-Watson: 1.528
Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.551
Skew: -0.052 Prob(JB): 0.759
Kurtosis: 2.067 Cond. No. 66.2