LID SPN TRAP

1458443_at

Crtc1 CREB regulated transcription coactivator 1

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
3.951 0.061 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.709
Model: OLS Adj. R-squared: 0.663
Method: Least Squares F-statistic: 15.40
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.54e-05
Time: 05:15:29 Log-Likelihood: -98.925
No. Observations: 23 AIC: 205.8
Df Residuals: 19 BIC: 210.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 291.7921 150.317 1.941 0.067 -22.825 606.409
C(dose)[T.1] -24.0575 203.030 -0.118 0.907 -449.004 400.889
expression -26.6798 16.868 -1.582 0.130 -61.985 8.625
expression:C(dose)[T.1] 7.6658 23.376 0.328 0.747 -41.262 56.593
Omnibus: 0.015 Durbin-Watson: 2.030
Prob(Omnibus): 0.993 Jarque-Bera (JB): 0.177
Skew: -0.050 Prob(JB): 0.915
Kurtosis: 2.582 Cond. No. 571.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.707
Model: OLS Adj. R-squared: 0.678
Method: Least Squares F-statistic: 24.12
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.67e-06
Time: 05:15:29 Log-Likelihood: -98.990
No. Observations: 23 AIC: 204.0
Df Residuals: 20 BIC: 207.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 256.2482 101.799 2.517 0.020 43.899 468.597
C(dose)[T.1] 42.4421 9.709 4.371 0.000 22.189 62.695
expression -22.6883 11.415 -1.988 0.061 -46.499 1.122
Omnibus: 0.009 Durbin-Watson: 2.006
Prob(Omnibus): 0.995 Jarque-Bera (JB): 0.112
Skew: 0.005 Prob(JB): 0.946
Kurtosis: 2.659 Cond. No. 224.

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: 05:15:29 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: 0.427
Model: OLS Adj. R-squared: 0.400
Method: Least Squares F-statistic: 15.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000722
Time: 05:15:29 Log-Likelihood: -106.70
No. Observations: 23 AIC: 217.4
Df Residuals: 21 BIC: 219.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 520.9277 111.676 4.665 0.000 288.685 753.170
expression -50.8579 12.857 -3.956 0.001 -77.596 -24.120
Omnibus: 2.517 Durbin-Watson: 2.679
Prob(Omnibus): 0.284 Jarque-Bera (JB): 1.425
Skew: 0.602 Prob(JB): 0.490
Kurtosis: 3.188 Cond. No. 180.

CP101

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

F-statistic p-value df difference
1.136 0.307 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.578
Model: OLS Adj. R-squared: 0.463
Method: Least Squares F-statistic: 5.024
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0196
Time: 05:15:29 Log-Likelihood: -68.827
No. Observations: 15 AIC: 145.7
Df Residuals: 11 BIC: 148.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.0410 140.682 0.391 0.703 -254.598 364.680
C(dose)[T.1] -323.2654 252.801 -1.279 0.227 -879.677 233.146
expression 1.4899 16.873 0.088 0.931 -35.647 38.627
expression:C(dose)[T.1] 43.3377 29.698 1.459 0.172 -22.026 108.702
Omnibus: 0.229 Durbin-Watson: 1.316
Prob(Omnibus): 0.892 Jarque-Bera (JB): 0.364
Skew: -0.228 Prob(JB): 0.834
Kurtosis: 2.388 Cond. No. 374.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.496
Model: OLS Adj. R-squared: 0.413
Method: Least Squares F-statistic: 5.915
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0163
Time: 05:15:29 Log-Likelihood: -70.155
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -61.2730 121.257 -0.505 0.622 -325.470 202.924
C(dose)[T.1] 45.0083 15.549 2.895 0.013 11.131 78.886
expression 15.4791 14.524 1.066 0.307 -16.166 47.124
Omnibus: 1.025 Durbin-Watson: 0.931
Prob(Omnibus): 0.599 Jarque-Bera (JB): 0.904
Skew: -0.432 Prob(JB): 0.636
Kurtosis: 2.162 Cond. No. 139.

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: 05:15:29 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: 0.145
Model: OLS Adj. R-squared: 0.079
Method: Least Squares F-statistic: 2.201
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.162
Time: 05:15:29 Log-Likelihood: -74.127
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -127.1484 149.123 -0.853 0.409 -449.309 195.012
expression 26.1047 17.594 1.484 0.162 -11.905 64.115
Omnibus: 0.696 Durbin-Watson: 1.487
Prob(Omnibus): 0.706 Jarque-Bera (JB): 0.615
Skew: -0.052 Prob(JB): 0.735
Kurtosis: 2.014 Cond. No. 136.