LID SPN TRAP

1452479_at

Copg2as2 coatomer protein complex, subunit gamma 2, antisense 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
0.766 0.392 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.704
Model: OLS Adj. R-squared: 0.658
Method: Least Squares F-statistic: 15.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.91e-05
Time: 05:12:13 Log-Likelihood: -99.095
No. Observations: 23 AIC: 206.2
Df Residuals: 19 BIC: 210.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.7937 102.161 0.517 0.611 -161.032 266.619
C(dose)[T.1] 399.2129 209.616 1.904 0.072 -39.519 837.944
expression 0.1804 13.006 0.014 0.989 -27.041 27.401
expression:C(dose)[T.1] -43.6498 26.497 -1.647 0.116 -99.109 11.810
Omnibus: 0.187 Durbin-Watson: 1.826
Prob(Omnibus): 0.911 Jarque-Bera (JB): 0.280
Skew: -0.183 Prob(JB): 0.869
Kurtosis: 2.603 Cond. No. 478.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.59
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.95e-05
Time: 05:12:13 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 135.2669 92.790 1.458 0.160 -58.289 328.823
C(dose)[T.1] 54.1774 8.660 6.256 0.000 36.113 72.242
expression -10.3353 11.807 -0.875 0.392 -34.964 14.293
Omnibus: 0.280 Durbin-Watson: 1.989
Prob(Omnibus): 0.869 Jarque-Bera (JB): 0.460
Skew: 0.062 Prob(JB): 0.795
Kurtosis: 2.318 Cond. No. 173.

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: 05:12:13 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: 0.001
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.01190
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.914
Time: 05:12:13 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 96.6488 155.369 0.622 0.541 -226.459 419.756
expression -2.1482 19.691 -0.109 0.914 -43.098 38.802
Omnibus: 3.649 Durbin-Watson: 2.500
Prob(Omnibus): 0.161 Jarque-Bera (JB): 1.616
Skew: 0.277 Prob(JB): 0.446
Kurtosis: 1.825 Cond. No. 173.

CP101

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

F-statistic p-value df difference
4.551 0.054 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.606
Model: OLS Adj. R-squared: 0.498
Method: Least Squares F-statistic: 5.629
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0138
Time: 05:12:13 Log-Likelihood: -68.323
No. Observations: 15 AIC: 144.6
Df Residuals: 11 BIC: 147.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 367.3136 181.550 2.023 0.068 -32.275 766.902
C(dose)[T.1] -55.0161 237.631 -0.232 0.821 -578.039 468.007
expression -34.8343 21.056 -1.654 0.126 -81.177 11.509
expression:C(dose)[T.1] 10.7584 28.247 0.381 0.711 -51.412 72.929
Omnibus: 2.279 Durbin-Watson: 1.337
Prob(Omnibus): 0.320 Jarque-Bera (JB): 1.193
Skew: -0.691 Prob(JB): 0.551
Kurtosis: 2.971 Cond. No. 396.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.600
Model: OLS Adj. R-squared: 0.534
Method: Least Squares F-statistic: 9.013
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00407
Time: 05:12:13 Log-Likelihood: -68.421
No. Observations: 15 AIC: 142.8
Df Residuals: 12 BIC: 145.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 315.8505 116.858 2.703 0.019 61.240 570.461
C(dose)[T.1] 35.2995 14.901 2.369 0.035 2.832 67.767
expression -28.8564 13.526 -2.133 0.054 -58.328 0.615
Omnibus: 1.866 Durbin-Watson: 1.217
Prob(Omnibus): 0.393 Jarque-Bera (JB): 0.949
Skew: -0.615 Prob(JB): 0.622
Kurtosis: 2.936 Cond. No. 149.

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: 05:12:13 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: 0.413
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 9.164
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00972
Time: 05:12:13 Log-Likelihood: -71.299
No. Observations: 15 AIC: 146.6
Df Residuals: 13 BIC: 148.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 451.6674 118.519 3.811 0.002 195.623 707.712
expression -42.8638 14.160 -3.027 0.010 -73.454 -12.273
Omnibus: 1.586 Durbin-Watson: 2.178
Prob(Omnibus): 0.453 Jarque-Bera (JB): 1.239
Skew: 0.634 Prob(JB): 0.538
Kurtosis: 2.391 Cond. No. 129.