LID SPN TRAP

1435917_at

Ociad2 OCIA 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
0.162 0.691 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.91
Date: Tue, 28 Jan 2025 Prob (F-statistic): 7.86e-05
Time: 19:02:34 Log-Likelihood: -100.32
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -79.8823 160.051 -0.499 0.623 -414.873 255.109
C(dose)[T.1] 431.7373 363.925 1.186 0.250 -329.967 1193.442
expression 14.5099 17.307 0.838 0.412 -21.714 50.733
expression:C(dose)[T.1] -39.6763 37.850 -1.048 0.308 -118.898 39.545
Omnibus: 2.396 Durbin-Watson: 1.774
Prob(Omnibus): 0.302 Jarque-Bera (JB): 1.155
Skew: -0.003 Prob(JB): 0.561
Kurtosis: 1.902 Cond. No. 937.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.73
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.61e-05
Time: 19:02:34 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -3.2240 142.718 -0.023 0.982 -300.929 294.481
C(dose)[T.1] 50.4386 11.317 4.457 0.000 26.831 74.046
expression 6.2147 15.430 0.403 0.691 -25.971 38.400
Omnibus: 0.469 Durbin-Watson: 1.910
Prob(Omnibus): 0.791 Jarque-Bera (JB): 0.563
Skew: -0.043 Prob(JB): 0.755
Kurtosis: 2.239 Cond. No. 314.

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 19:02:34 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: 0.306
Model: OLS Adj. R-squared: 0.273
Method: Least Squares F-statistic: 9.266
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.00617
Time: 19:02:34 Log-Likelihood: -108.90
No. Observations: 23 AIC: 221.8
Df Residuals: 21 BIC: 224.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -392.9539 155.399 -2.529 0.020 -716.124 -69.784
expression 49.9420 16.407 3.044 0.006 15.822 84.062
Omnibus: 3.077 Durbin-Watson: 2.383
Prob(Omnibus): 0.215 Jarque-Bera (JB): 1.527
Skew: 0.291 Prob(JB): 0.466
Kurtosis: 1.880 Cond. No. 248.

CP101

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

F-statistic p-value df difference
0.925 0.355 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.489
Model: OLS Adj. R-squared: 0.349
Method: Least Squares F-statistic: 3.505
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0530
Time: 19:02:34 Log-Likelihood: -70.268
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -189.2873 327.699 -0.578 0.575 -910.548 531.974
C(dose)[T.1] 100.0690 529.138 0.189 0.853 -1064.556 1264.694
expression 28.7012 36.614 0.784 0.450 -51.887 109.289
expression:C(dose)[T.1] -6.1285 58.436 -0.105 0.918 -134.745 122.488
Omnibus: 2.865 Durbin-Watson: 0.867
Prob(Omnibus): 0.239 Jarque-Bera (JB): 2.140
Skew: -0.876 Prob(JB): 0.343
Kurtosis: 2.402 Cond. No. 778.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.488
Model: OLS Adj. R-squared: 0.403
Method: Least Squares F-statistic: 5.724
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0180
Time: 19:02:34 Log-Likelihood: -70.276
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -167.7668 244.744 -0.685 0.506 -701.018 365.485
C(dose)[T.1] 44.6025 15.900 2.805 0.016 9.960 79.245
expression 26.2951 27.335 0.962 0.355 -33.262 85.852
Omnibus: 2.829 Durbin-Watson: 0.841
Prob(Omnibus): 0.243 Jarque-Bera (JB): 2.135
Skew: -0.865 Prob(JB): 0.344
Kurtosis: 2.351 Cond. No. 297.

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 19:02:34 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: 0.153
Model: OLS Adj. R-squared: 0.087
Method: Least Squares F-statistic: 2.342
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.150
Time: 19:02:35 Log-Likelihood: -74.058
No. Observations: 15 AIC: 152.1
Df Residuals: 13 BIC: 153.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -352.1236 291.461 -1.208 0.249 -981.786 277.539
expression 49.3261 32.233 1.530 0.150 -20.309 118.961
Omnibus: 0.987 Durbin-Watson: 1.712
Prob(Omnibus): 0.610 Jarque-Bera (JB): 0.738
Skew: 0.165 Prob(JB): 0.691
Kurtosis: 1.965 Cond. No. 285.