LID SPN TRAP

1460740_at

Cltb clathrin, light polypeptide (Lcb)

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
0.008 0.931 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.78
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000138
Time: 22:58:21 Log-Likelihood: -101.02
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 -7.3527 257.806 -0.029 0.978 -546.947 532.242
C(dose)[T.1] 149.9446 381.230 0.393 0.698 -647.979 947.868
expression 6.7661 28.327 0.239 0.814 -52.523 66.055
expression:C(dose)[T.1] -10.4500 40.907 -0.255 0.801 -96.070 75.170
Omnibus: 0.112 Durbin-Watson: 1.876
Prob(Omnibus): 0.946 Jarque-Bera (JB): 0.336
Skew: 0.021 Prob(JB): 0.845
Kurtosis: 2.410 Cond. No. 1.03e+03

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: 18.51
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.82e-05
Time: 22:58:21 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 38.2388 181.643 0.211 0.835 -340.662 417.140
C(dose)[T.1] 52.6085 12.062 4.362 0.000 27.448 77.769
expression 1.7552 19.953 0.088 0.931 -39.866 43.377
Omnibus: 0.353 Durbin-Watson: 1.900
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.505
Skew: 0.083 Prob(JB): 0.777
Kurtosis: 2.293 Cond. No. 391.

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: 22:58:22 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: 0.316
Model: OLS Adj. R-squared: 0.283
Method: Least Squares F-statistic: 9.680
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00528
Time: 22:58:22 Log-Likelihood: -108.75
No. Observations: 23 AIC: 221.5
Df Residuals: 21 BIC: 223.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -492.2071 183.916 -2.676 0.014 -874.682 -109.732
expression 61.5175 19.772 3.111 0.005 20.399 102.636
Omnibus: 1.851 Durbin-Watson: 2.413
Prob(Omnibus): 0.396 Jarque-Bera (JB): 1.605
Skew: 0.583 Prob(JB): 0.448
Kurtosis: 2.440 Cond. No. 290.

CP101

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

F-statistic p-value df difference
1.309 0.275 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.544
Model: OLS Adj. R-squared: 0.419
Method: Least Squares F-statistic: 4.366
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0296
Time: 22:58:22 Log-Likelihood: -69.419
No. Observations: 15 AIC: 146.8
Df Residuals: 11 BIC: 149.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 145.8211 372.095 0.392 0.703 -673.155 964.797
C(dose)[T.1] -408.1686 450.239 -0.907 0.384 -1399.137 582.800
expression -9.5671 45.391 -0.211 0.837 -109.473 90.339
expression:C(dose)[T.1] 53.3677 54.013 0.988 0.344 -65.514 172.249
Omnibus: 0.972 Durbin-Watson: 0.742
Prob(Omnibus): 0.615 Jarque-Bera (JB): 0.823
Skew: -0.329 Prob(JB): 0.663
Kurtosis: 2.060 Cond. No. 755.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.503
Model: OLS Adj. R-squared: 0.420
Method: Least Squares F-statistic: 6.072
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0151
Time: 22:58:22 Log-Likelihood: -70.056
No. Observations: 15 AIC: 146.1
Df Residuals: 12 BIC: 148.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -163.0135 201.682 -0.808 0.435 -602.440 276.413
C(dose)[T.1] 36.3074 18.715 1.940 0.076 -4.468 77.083
expression 28.1235 24.577 1.144 0.275 -25.426 81.673
Omnibus: 2.504 Durbin-Watson: 0.651
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.916
Skew: -0.790 Prob(JB): 0.384
Kurtosis: 2.246 Cond. No. 233.

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: 22:58:22 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: 0.347
Model: OLS Adj. R-squared: 0.297
Method: Least Squares F-statistic: 6.912
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0208
Time: 22:58:22 Log-Likelihood: -72.102
No. Observations: 15 AIC: 148.2
Df Residuals: 13 BIC: 149.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -385.8158 182.565 -2.113 0.054 -780.224 8.592
expression 56.8217 21.613 2.629 0.021 10.129 103.514
Omnibus: 2.193 Durbin-Watson: 1.089
Prob(Omnibus): 0.334 Jarque-Bera (JB): 0.568
Skew: -0.366 Prob(JB): 0.753
Kurtosis: 3.611 Cond. No. 190.