LID SPN TRAP

1451431_a_at

Dbndd2 dysbindin (dystrobrevin binding protein 1) domain containing 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
0.027 0.872 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.603
Method: Least Squares F-statistic: 12.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000114
Time: 04:06:40 Log-Likelihood: -100.79
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -232.9476 633.779 -0.368 0.717 -1559.462 1093.567
C(dose)[T.1] 556.8284 763.500 0.729 0.475 -1041.195 2154.852
expression 26.8486 59.255 0.453 0.656 -97.173 150.870
expression:C(dose)[T.1] -47.6287 71.994 -0.662 0.516 -198.313 103.056
Omnibus: 0.105 Durbin-Watson: 1.796
Prob(Omnibus): 0.949 Jarque-Bera (JB): 0.327
Skew: -0.045 Prob(JB): 0.849
Kurtosis: 2.422 Cond. No. 2.57e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 04:06:40 Log-Likelihood: -101.05
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 112.1320 354.903 0.316 0.755 -628.182 852.446
C(dose)[T.1] 51.7956 12.884 4.020 0.001 24.921 78.670
expression -5.4158 33.178 -0.163 0.872 -74.624 63.792
Omnibus: 0.229 Durbin-Watson: 1.931
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.426
Skew: 0.078 Prob(JB): 0.808
Kurtosis: 2.352 Cond. No. 865.

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: 04:06:40 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: 0.366
Model: OLS Adj. R-squared: 0.336
Method: Least Squares F-statistic: 12.14
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00221
Time: 04:06:40 Log-Likelihood: -107.86
No. Observations: 23 AIC: 219.7
Df Residuals: 21 BIC: 222.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1169.2708 312.785 3.738 0.001 518.798 1819.743
expression -103.1849 29.617 -3.484 0.002 -164.777 -41.593
Omnibus: 3.822 Durbin-Watson: 2.641
Prob(Omnibus): 0.148 Jarque-Bera (JB): 2.121
Skew: 0.682 Prob(JB): 0.346
Kurtosis: 3.593 Cond. No. 580.

CP101

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

F-statistic p-value df difference
4.055 0.067 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.600
Model: OLS Adj. R-squared: 0.490
Method: Least Squares F-statistic: 5.492
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0149
Time: 04:06:40 Log-Likelihood: -68.435
No. Observations: 15 AIC: 144.9
Df Residuals: 11 BIC: 147.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -139.7968 1543.502 -0.091 0.929 -3537.021 3257.428
C(dose)[T.1] -875.6244 1641.038 -0.534 0.604 -4487.524 2736.275
expression 18.7194 139.427 0.134 0.896 -288.157 325.596
expression:C(dose)[T.1] 83.8330 148.284 0.565 0.583 -242.538 410.204
Omnibus: 0.494 Durbin-Watson: 1.086
Prob(Omnibus): 0.781 Jarque-Bera (JB): 0.519
Skew: -0.345 Prob(JB): 0.771
Kurtosis: 2.405 Cond. No. 4.11e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.588
Model: OLS Adj. R-squared: 0.519
Method: Least Squares F-statistic: 8.563
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00489
Time: 04:06:40 Log-Likelihood: -68.649
No. Observations: 15 AIC: 143.3
Df Residuals: 12 BIC: 145.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -960.2820 510.428 -1.881 0.084 -2072.408 151.844
C(dose)[T.1] 52.1094 13.684 3.808 0.002 22.294 81.924
expression 92.8367 46.100 2.014 0.067 -7.607 193.280
Omnibus: 0.808 Durbin-Watson: 1.032
Prob(Omnibus): 0.668 Jarque-Bera (JB): 0.723
Skew: -0.445 Prob(JB): 0.697
Kurtosis: 2.396 Cond. No. 839.

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: 04:06:40 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: 0.090
Model: OLS Adj. R-squared: 0.020
Method: Least Squares F-statistic: 1.288
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.277
Time: 04:06:40 Log-Likelihood: -74.592
No. Observations: 15 AIC: 153.2
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -727.3780 723.527 -1.005 0.333 -2290.463 835.707
expression 74.2801 65.452 1.135 0.277 -67.120 215.680
Omnibus: 0.086 Durbin-Watson: 1.758
Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.067
Skew: -0.009 Prob(JB): 0.967
Kurtosis: 2.674 Cond. No. 832.