LID SPN TRAP

1423721_at

Tpm1 tropomyosin 1, alpha

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
2.227 0.151 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.686
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 13.81
Date: Thu, 03 Apr 2025 Prob (F-statistic): 5.13e-05
Time: 22:50:41 Log-Likelihood: -99.796
No. Observations: 23 AIC: 207.6
Df Residuals: 19 BIC: 212.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 545.1736 399.008 1.366 0.188 -289.959 1380.306
C(dose)[T.1] 369.0747 1044.727 0.353 0.728 -1817.564 2555.714
expression -45.3055 36.816 -1.231 0.233 -122.362 31.751
expression:C(dose)[T.1] -28.0247 95.175 -0.294 0.772 -227.228 171.179
Omnibus: 2.618 Durbin-Watson: 1.974
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.337
Skew: 0.226 Prob(JB): 0.512
Kurtosis: 1.909 Cond. No. 3.11e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.653
Method: Least Squares F-statistic: 21.67
Date: Thu, 03 Apr 2025 Prob (F-statistic): 9.86e-06
Time: 22:50:42 Log-Likelihood: -99.848
No. Observations: 23 AIC: 205.7
Df Residuals: 20 BIC: 209.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 590.6163 359.454 1.643 0.116 -159.192 1340.425
C(dose)[T.1] 61.4648 9.943 6.182 0.000 40.724 82.205
expression -49.4989 33.166 -1.492 0.151 -118.681 19.683
Omnibus: 1.746 Durbin-Watson: 1.996
Prob(Omnibus): 0.418 Jarque-Bera (JB): 1.078
Skew: 0.183 Prob(JB): 0.583
Kurtosis: 2.005 Cond. No. 953.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:50:42 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: 0.037
Method: Least Squares F-statistic: 1.847
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.189
Time: 22:50:42 Log-Likelihood: -112.14
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 -605.7083 504.344 -1.201 0.243 -1654.549 443.133
expression 62.7949 46.201 1.359 0.189 -33.285 158.875
Omnibus: 3.233 Durbin-Watson: 2.364
Prob(Omnibus): 0.199 Jarque-Bera (JB): 1.336
Skew: 0.066 Prob(JB): 0.513
Kurtosis: 1.827 Cond. No. 802.

CP101

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

F-statistic p-value df difference
0.017 0.899 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.301
Method: Least Squares F-statistic: 3.014
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0761
Time: 22:50:42 Log-Likelihood: -70.801
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 209.8814 675.981 0.310 0.762 -1277.943 1697.706
C(dose)[T.1] -132.5325 1017.973 -0.130 0.899 -2373.076 2108.010
expression -13.1198 62.248 -0.211 0.837 -150.126 123.886
expression:C(dose)[T.1] 16.7531 93.975 0.178 0.862 -190.084 223.590
Omnibus: 2.354 Durbin-Watson: 0.859
Prob(Omnibus): 0.308 Jarque-Bera (JB): 1.683
Skew: -0.786 Prob(JB): 0.431
Kurtosis: 2.532 Cond. No. 1.76e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.900
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0278
Time: 22:50:42 Log-Likelihood: -70.823
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.0703 485.619 0.268 0.793 -928.003 1188.143
C(dose)[T.1] 48.9203 15.874 3.082 0.010 14.335 83.506
expression -5.7693 44.713 -0.129 0.899 -103.190 91.651
Omnibus: 2.677 Durbin-Watson: 0.826
Prob(Omnibus): 0.262 Jarque-Bera (JB): 1.797
Skew: -0.831 Prob(JB): 0.407
Kurtosis: 2.666 Cond. No. 677.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:50:42 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: 0.014
Model: OLS Adj. R-squared: -0.062
Method: Least Squares F-statistic: 0.1826
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.676
Time: 22:50:42 Log-Likelihood: -75.195
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 357.3987 617.238 0.579 0.572 -976.063 1690.860
expression -24.3468 56.973 -0.427 0.676 -147.431 98.737
Omnibus: 0.952 Durbin-Watson: 1.575
Prob(Omnibus): 0.621 Jarque-Bera (JB): 0.701
Skew: 0.079 Prob(JB): 0.704
Kurtosis: 1.953 Cond. No. 668.