LID SPN TRAP

1453063_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
0.552 0.466 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.715
Model: OLS Adj. R-squared: 0.671
Method: Least Squares F-statistic: 15.92
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.03e-05
Time: 22:50:42 Log-Likelihood: -98.652
No. Observations: 23 AIC: 205.3
Df Residuals: 19 BIC: 209.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1415.5566 1104.221 -1.282 0.215 -3726.718 895.605
C(dose)[T.1] 2517.9473 1261.499 1.996 0.060 -122.400 5158.295
expression 120.5578 90.573 1.331 0.199 -69.013 310.129
expression:C(dose)[T.1] -201.2388 103.201 -1.950 0.066 -417.240 14.763
Omnibus: 1.014 Durbin-Watson: 1.753
Prob(Omnibus): 0.602 Jarque-Bera (JB): 0.693
Skew: 0.413 Prob(JB): 0.707
Kurtosis: 2.799 Cond. No. 5.63e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 19.28
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.16e-05
Time: 22:50:43 Log-Likelihood: -100.75
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 474.1543 565.170 0.839 0.411 -704.770 1653.079
C(dose)[T.1] 58.1327 10.793 5.386 0.000 35.619 80.647
expression -34.4462 46.356 -0.743 0.466 -131.142 62.250
Omnibus: 1.577 Durbin-Watson: 1.861
Prob(Omnibus): 0.454 Jarque-Bera (JB): 1.027
Skew: 0.180 Prob(JB): 0.598
Kurtosis: 2.030 Cond. No. 1.62e+03

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: 22:50:43 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: 0.163
Model: OLS Adj. R-squared: 0.123
Method: Least Squares F-statistic: 4.093
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0560
Time: 22:50:43 Log-Likelihood: -111.06
No. Observations: 23 AIC: 226.1
Df Residuals: 21 BIC: 228.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1328.0097 695.825 -1.909 0.070 -2775.057 119.038
expression 114.8419 56.763 2.023 0.056 -3.202 232.886
Omnibus: 2.632 Durbin-Watson: 2.130
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.867
Skew: 0.515 Prob(JB): 0.393
Kurtosis: 2.057 Cond. No. 1.30e+03

CP101

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

F-statistic p-value df difference
5.693 0.034 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.627
Model: OLS Adj. R-squared: 0.526
Method: Least Squares F-statistic: 6.173
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0102
Time: 22:50:43 Log-Likelihood: -67.897
No. Observations: 15 AIC: 143.8
Df Residuals: 11 BIC: 146.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -827.7664 687.321 -1.204 0.254 -2340.549 685.016
C(dose)[T.1] -125.2056 890.235 -0.141 0.891 -2084.599 1834.188
expression 76.0588 58.391 1.303 0.219 -52.459 204.577
expression:C(dose)[T.1] 14.3446 75.470 0.190 0.853 -151.763 180.452
Omnibus: 1.142 Durbin-Watson: 1.261
Prob(Omnibus): 0.565 Jarque-Bera (JB): 0.865
Skew: -0.303 Prob(JB): 0.649
Kurtosis: 1.991 Cond. No. 2.18e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.626
Model: OLS Adj. R-squared: 0.564
Method: Least Squares F-statistic: 10.05
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00273
Time: 22:50:43 Log-Likelihood: -67.921
No. Observations: 15 AIC: 141.8
Df Residuals: 12 BIC: 144.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -928.8316 417.662 -2.224 0.046 -1838.840 -18.823
C(dose)[T.1] 43.9813 13.145 3.346 0.006 15.340 72.623
expression 84.6456 35.477 2.386 0.034 7.348 161.943
Omnibus: 1.602 Durbin-Watson: 1.308
Prob(Omnibus): 0.449 Jarque-Bera (JB): 0.985
Skew: -0.287 Prob(JB): 0.611
Kurtosis: 1.884 Cond. No. 769.

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: 22:50:43 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: 0.277
Model: OLS Adj. R-squared: 0.222
Method: Least Squares F-statistic: 4.990
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0437
Time: 22:50:43 Log-Likelihood: -72.864
No. Observations: 15 AIC: 149.7
Df Residuals: 13 BIC: 151.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1138.3138 551.576 -2.064 0.060 -2329.922 53.295
expression 104.3818 46.728 2.234 0.044 3.433 205.331
Omnibus: 1.090 Durbin-Watson: 2.197
Prob(Omnibus): 0.580 Jarque-Bera (JB): 0.886
Skew: 0.356 Prob(JB): 0.642
Kurtosis: 2.046 Cond. No. 759.