LID SPN TRAP

1455628_at

Epb4.1l4b erythrocyte protein band 4.1-like 4b

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
1.360 0.257 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.711
Model: OLS Adj. R-squared: 0.665
Method: Least Squares F-statistic: 15.57
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.36e-05
Time: 22:47:56 Log-Likelihood: -98.834
No. Observations: 23 AIC: 205.7
Df Residuals: 19 BIC: 210.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -97.2911 90.256 -1.078 0.295 -286.200 91.618
C(dose)[T.1] 503.5625 283.605 1.776 0.092 -90.030 1097.155
expression 20.4335 12.149 1.682 0.109 -4.996 45.863
expression:C(dose)[T.1] -58.3675 36.231 -1.611 0.124 -134.200 17.465
Omnibus: 0.936 Durbin-Watson: 1.481
Prob(Omnibus): 0.626 Jarque-Bera (JB): 0.914
Skew: -0.396 Prob(JB): 0.633
Kurtosis: 2.427 Cond. No. 624.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 20.43
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.47e-05
Time: 22:47:56 Log-Likelihood: -100.31
No. Observations: 23 AIC: 206.6
Df Residuals: 20 BIC: 210.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -48.6288 88.378 -0.550 0.588 -232.983 135.725
C(dose)[T.1] 46.9483 10.101 4.648 0.000 25.878 68.018
expression 13.8702 11.894 1.166 0.257 -10.940 38.680
Omnibus: 0.217 Durbin-Watson: 1.841
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.418
Skew: -0.051 Prob(JB): 0.812
Kurtosis: 2.348 Cond. No. 163.

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: 22:47:56 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: 0.284
Method: Least Squares F-statistic: 9.722
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00520
Time: 22:47:56 Log-Likelihood: -108.73
No. Observations: 23 AIC: 221.5
Df Residuals: 21 BIC: 223.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -255.0858 107.542 -2.372 0.027 -478.731 -31.440
expression 43.8536 14.064 3.118 0.005 14.605 73.102
Omnibus: 1.791 Durbin-Watson: 2.239
Prob(Omnibus): 0.408 Jarque-Bera (JB): 1.515
Skew: 0.584 Prob(JB): 0.469
Kurtosis: 2.537 Cond. No. 140.

CP101

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

F-statistic p-value df difference
0.306 0.590 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.464
Model: OLS Adj. R-squared: 0.318
Method: Least Squares F-statistic: 3.179
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0672
Time: 22:47:57 Log-Likelihood: -70.618
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 42.9134 215.145 0.199 0.846 -430.618 516.445
C(dose)[T.1] -1.1889 254.335 -0.005 0.996 -560.977 558.599
expression 3.5050 30.713 0.114 0.911 -64.095 71.105
expression:C(dose)[T.1] 7.0873 36.178 0.196 0.848 -72.539 86.714
Omnibus: 2.908 Durbin-Watson: 0.896
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.811
Skew: -0.846 Prob(JB): 0.404
Kurtosis: 2.815 Cond. No. 332.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 5.162
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0241
Time: 22:47:57 Log-Likelihood: -70.644
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 7.1862 109.469 0.066 0.949 -231.327 245.699
C(dose)[T.1] 48.5340 15.589 3.113 0.009 14.569 82.499
expression 8.6131 15.567 0.553 0.590 -25.304 42.530
Omnibus: 2.741 Durbin-Watson: 0.914
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.775
Skew: -0.832 Prob(JB): 0.412
Kurtosis: 2.734 Cond. No. 102.

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: 22:47:57 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: 0.028
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.3785
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.549
Time: 22:47:57 Log-Likelihood: -75.085
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.8837 141.411 0.049 0.962 -298.617 312.384
expression 12.3353 20.050 0.615 0.549 -30.979 55.650
Omnibus: 0.489 Durbin-Watson: 1.718
Prob(Omnibus): 0.783 Jarque-Bera (JB): 0.536
Skew: 0.028 Prob(JB): 0.765
Kurtosis: 2.076 Cond. No. 102.