LID SPN TRAP

1460287_at

Timp2 tissue inhibitor of metalloproteinase 2

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
0.106 0.748 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.600
Method: Least Squares F-statistic: 11.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000124
Time: 04:14:59 Log-Likelihood: -100.89
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.2658 282.862 0.217 0.831 -530.771 653.302
C(dose)[T.1] -153.0416 464.925 -0.329 0.746 -1126.142 820.058
expression -0.6902 27.656 -0.025 0.980 -58.574 57.194
expression:C(dose)[T.1] 19.2809 44.146 0.437 0.667 -73.117 111.679
Omnibus: 0.397 Durbin-Watson: 1.914
Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.532
Skew: 0.093 Prob(JB): 0.767
Kurtosis: 2.279 Cond. No. 1.36e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.69e-05
Time: 04:14:59 Log-Likelihood: -101.00
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 -16.1101 216.004 -0.075 0.941 -466.686 434.466
C(dose)[T.1] 49.9266 13.645 3.659 0.002 21.465 78.389
expression 6.8767 21.116 0.326 0.748 -37.170 50.923
Omnibus: 0.377 Durbin-Watson: 2.005
Prob(Omnibus): 0.828 Jarque-Bera (JB): 0.515
Skew: 0.047 Prob(JB): 0.773
Kurtosis: 2.273 Cond. No. 523.

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: 04:14:59 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: 0.417
Model: OLS Adj. R-squared: 0.389
Method: Least Squares F-statistic: 15.03
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000871
Time: 04:14:59 Log-Likelihood: -106.90
No. Observations: 23 AIC: 217.8
Df Residuals: 21 BIC: 220.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -612.6816 178.663 -3.429 0.003 -984.231 -241.132
expression 66.1774 17.068 3.877 0.001 30.683 101.672
Omnibus: 1.026 Durbin-Watson: 2.611
Prob(Omnibus): 0.599 Jarque-Bera (JB): 0.785
Skew: 0.004 Prob(JB): 0.675
Kurtosis: 2.095 Cond. No. 343.

CP101

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

F-statistic p-value df difference
2.616 0.132 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.556
Model: OLS Adj. R-squared: 0.435
Method: Least Squares F-statistic: 4.586
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0257
Time: 04:14:59 Log-Likelihood: -69.216
No. Observations: 15 AIC: 146.4
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -386.5476 311.249 -1.242 0.240 -1071.603 298.508
C(dose)[T.1] 245.0538 475.294 0.516 0.616 -801.062 1291.169
expression 45.5971 31.243 1.459 0.172 -23.168 114.362
expression:C(dose)[T.1] -20.9514 46.384 -0.452 0.660 -123.043 81.140
Omnibus: 1.793 Durbin-Watson: 1.058
Prob(Omnibus): 0.408 Jarque-Bera (JB): 1.420
Skew: -0.648 Prob(JB): 0.492
Kurtosis: 2.231 Cond. No. 869.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.547
Model: OLS Adj. R-squared: 0.472
Method: Least Squares F-statistic: 7.258
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00859
Time: 04:14:59 Log-Likelihood: -69.354
No. Observations: 15 AIC: 144.7
Df Residuals: 12 BIC: 146.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -291.9090 222.403 -1.313 0.214 -776.484 192.666
C(dose)[T.1] 30.5392 18.342 1.665 0.122 -9.425 70.504
expression 36.0917 22.314 1.617 0.132 -12.525 84.709
Omnibus: 1.973 Durbin-Watson: 0.955
Prob(Omnibus): 0.373 Jarque-Bera (JB): 1.444
Skew: -0.586 Prob(JB): 0.486
Kurtosis: 2.031 Cond. No. 324.

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: 04:14:59 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: 0.443
Model: OLS Adj. R-squared: 0.400
Method: Least Squares F-statistic: 10.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00677
Time: 04:14:59 Log-Likelihood: -70.913
No. Observations: 15 AIC: 145.8
Df Residuals: 13 BIC: 147.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -514.6670 189.381 -2.718 0.018 -923.799 -105.535
expression 59.4543 18.494 3.215 0.007 19.501 99.408
Omnibus: 2.509 Durbin-Watson: 1.651
Prob(Omnibus): 0.285 Jarque-Bera (JB): 1.331
Skew: 0.411 Prob(JB): 0.514
Kurtosis: 1.794 Cond. No. 258.