LID SPN TRAP

1429373_x_at

Crtc2 CREB regulated transcription coactivator 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
0.767 0.391 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: 0.657
Method: Least Squares F-statistic: 15.04
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.96e-05
Time: 04:59:52 Log-Likelihood: -99.115
No. Observations: 23 AIC: 206.2
Df Residuals: 19 BIC: 210.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 119.5978 127.485 0.938 0.360 -147.232 386.427
C(dose)[T.1] -242.1348 180.582 -1.341 0.196 -620.098 135.828
expression -11.0360 21.494 -0.513 0.614 -56.024 33.952
expression:C(dose)[T.1] 49.6717 30.368 1.636 0.118 -13.890 113.234
Omnibus: 1.176 Durbin-Watson: 1.993
Prob(Omnibus): 0.555 Jarque-Bera (JB): 1.100
Skew: 0.440 Prob(JB): 0.577
Kurtosis: 2.389 Cond. No. 347.

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): 1.94e-05
Time: 04:59:52 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 -27.8406 93.850 -0.297 0.770 -223.608 167.926
C(dose)[T.1] 52.9211 8.619 6.140 0.000 34.941 70.901
expression 13.8476 15.807 0.876 0.391 -19.126 46.821
Omnibus: 1.569 Durbin-Watson: 2.049
Prob(Omnibus): 0.456 Jarque-Bera (JB): 1.179
Skew: 0.326 Prob(JB): 0.555
Kurtosis: 2.102 Cond. No. 134.

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: 04:59:52 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: 0.025
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.5383
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.471
Time: 04:59:52 Log-Likelihood: -112.81
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.2905 155.551 -0.220 0.828 -357.776 289.195
expression 19.1949 26.162 0.734 0.471 -35.211 73.601
Omnibus: 2.914 Durbin-Watson: 2.698
Prob(Omnibus): 0.233 Jarque-Bera (JB): 1.377
Skew: 0.205 Prob(JB): 0.502
Kurtosis: 1.874 Cond. No. 133.

CP101

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

F-statistic p-value df difference
0.063 0.807 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.328
Method: Least Squares F-statistic: 3.281
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0623
Time: 04:59:52 Log-Likelihood: -70.507
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -29.0873 310.172 -0.094 0.927 -711.772 653.597
C(dose)[T.1] 314.1578 410.845 0.765 0.461 -590.107 1218.422
expression 15.1207 48.559 0.311 0.761 -91.756 121.997
expression:C(dose)[T.1] -43.5624 66.512 -0.655 0.526 -189.955 102.831
Omnibus: 4.130 Durbin-Watson: 0.708
Prob(Omnibus): 0.127 Jarque-Bera (JB): 2.481
Skew: -0.996 Prob(JB): 0.289
Kurtosis: 3.033 Cond. No. 435.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.942
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0272
Time: 04:59:52 Log-Likelihood: -70.794
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 119.1174 207.029 0.575 0.576 -331.960 570.195
C(dose)[T.1] 45.4672 21.653 2.100 0.058 -1.711 92.646
expression -8.0979 32.385 -0.250 0.807 -78.658 62.462
Omnibus: 2.994 Durbin-Watson: 0.846
Prob(Omnibus): 0.224 Jarque-Bera (JB): 1.995
Skew: -0.881 Prob(JB): 0.369
Kurtosis: 2.702 Cond. No. 168.

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: 04:59:52 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: 0.250
Model: OLS Adj. R-squared: 0.192
Method: Least Squares F-statistic: 4.337
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0576
Time: 04:59:52 Log-Likelihood: -73.141
No. Observations: 15 AIC: 150.3
Df Residuals: 13 BIC: 151.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 430.8108 162.134 2.657 0.020 80.542 781.080
expression -54.9327 26.378 -2.082 0.058 -111.920 2.054
Omnibus: 0.765 Durbin-Watson: 1.453
Prob(Omnibus): 0.682 Jarque-Bera (JB): 0.743
Skew: -0.396 Prob(JB): 0.690
Kurtosis: 2.250 Cond. No. 116.