LID SPN TRAP

1458339_at

Cdadc1 cytidine and dCMP deaminase domain containing 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
0.024 0.878 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.07
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000119
Time: 21:59:59 Log-Likelihood: -100.84
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 41.3469 43.480 0.951 0.354 -49.659 132.352
C(dose)[T.1] 91.7468 65.130 1.409 0.175 -44.573 228.066
expression 2.8587 9.567 0.299 0.768 -17.165 22.883
expression:C(dose)[T.1] -8.1543 13.812 -0.590 0.562 -37.063 20.755
Omnibus: 0.556 Durbin-Watson: 1.839
Prob(Omnibus): 0.757 Jarque-Bera (JB): 0.602
Skew: 0.021 Prob(JB): 0.740
Kurtosis: 2.208 Cond. No. 92.3

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.80e-05
Time: 21:59:59 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 58.9477 31.131 1.894 0.073 -5.989 123.885
C(dose)[T.1] 53.6800 9.039 5.939 0.000 34.826 72.534
expression -1.0534 6.787 -0.155 0.878 -15.211 13.104
Omnibus: 0.353 Durbin-Watson: 1.875
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.502
Skew: 0.049 Prob(JB): 0.778
Kurtosis: 2.283 Cond. No. 35.1

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: 21:59:59 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: 0.031
Model: OLS Adj. R-squared: -0.015
Method: Least Squares F-statistic: 0.6791
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.419
Time: 21:59:59 Log-Likelihood: -112.74
No. Observations: 23 AIC: 229.5
Df Residuals: 21 BIC: 231.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 38.7638 50.202 0.772 0.449 -65.637 143.164
expression 8.7984 10.677 0.824 0.419 -13.405 31.002
Omnibus: 1.908 Durbin-Watson: 2.528
Prob(Omnibus): 0.385 Jarque-Bera (JB): 1.361
Skew: 0.373 Prob(JB): 0.506
Kurtosis: 2.071 Cond. No. 34.7

CP101

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

F-statistic p-value df difference
1.694 0.218 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.525
Model: OLS Adj. R-squared: 0.395
Method: Least Squares F-statistic: 4.053
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0363
Time: 21:59:59 Log-Likelihood: -69.717
No. Observations: 15 AIC: 147.4
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.1212 48.212 2.616 0.024 20.008 232.234
C(dose)[T.1] 13.6627 71.575 0.191 0.852 -143.874 171.199
expression -15.4753 12.368 -1.251 0.237 -42.696 11.745
expression:C(dose)[T.1] 8.5536 19.831 0.431 0.675 -35.093 52.200
Omnibus: 1.691 Durbin-Watson: 1.044
Prob(Omnibus): 0.429 Jarque-Bera (JB): 1.290
Skew: -0.659 Prob(JB): 0.525
Kurtosis: 2.428 Cond. No. 46.5

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.517
Model: OLS Adj. R-squared: 0.436
Method: Least Squares F-statistic: 6.421
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0127
Time: 21:59:59 Log-Likelihood: -69.843
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 113.5033 37.000 3.068 0.010 32.887 194.119
C(dose)[T.1] 43.7681 15.313 2.858 0.014 10.404 77.132
expression -12.1484 9.334 -1.302 0.218 -32.485 8.189
Omnibus: 1.518 Durbin-Watson: 1.100
Prob(Omnibus): 0.468 Jarque-Bera (JB): 1.234
Skew: -0.592 Prob(JB): 0.540
Kurtosis: 2.245 Cond. No. 20.1

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: 21:59:59 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: 0.188
Model: OLS Adj. R-squared: 0.126
Method: Least Squares F-statistic: 3.012
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.106
Time: 21:59:59 Log-Likelihood: -73.737
No. Observations: 15 AIC: 151.5
Df Residuals: 13 BIC: 152.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 162.6740 40.802 3.987 0.002 74.526 250.822
expression -19.4149 11.187 -1.736 0.106 -43.583 4.753
Omnibus: 2.773 Durbin-Watson: 2.028
Prob(Omnibus): 0.250 Jarque-Bera (JB): 1.383
Skew: 0.410 Prob(JB): 0.501
Kurtosis: 1.759 Cond. No. 17.4