LID SPN TRAP

1460333_at

Ddx59 DEAD (Asp-Glu-Ala-Asp) box polypeptide 59

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
1.050 0.318 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 12.71
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.66e-05
Time: 05:15:17 Log-Likelihood: -100.44
No. Observations: 23 AIC: 208.9
Df Residuals: 19 BIC: 213.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -18.8753 102.080 -0.185 0.855 -232.530 194.780
C(dose)[T.1] 10.3239 188.490 0.055 0.957 -384.190 404.838
expression 9.2799 12.939 0.717 0.482 -17.801 36.361
expression:C(dose)[T.1] 5.4537 23.899 0.228 0.822 -44.567 55.474
Omnibus: 2.504 Durbin-Watson: 1.952
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.196
Skew: -0.081 Prob(JB): 0.550
Kurtosis: 1.895 Cond. No. 411.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 19.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.70e-05
Time: 05:15:17 Log-Likelihood: -100.47
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -31.4647 83.827 -0.375 0.711 -206.326 143.396
C(dose)[T.1] 53.2908 8.548 6.234 0.000 35.459 71.123
expression 10.8784 10.618 1.025 0.318 -11.269 33.026
Omnibus: 2.143 Durbin-Watson: 1.934
Prob(Omnibus): 0.343 Jarque-Bera (JB): 1.122
Skew: -0.098 Prob(JB): 0.571
Kurtosis: 1.936 Cond. No. 158.

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: 05:15:17 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: 0.019
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.3990
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.534
Time: 05:15:17 Log-Likelihood: -112.89
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -8.7362 140.211 -0.062 0.951 -300.321 282.849
expression 11.2286 17.776 0.632 0.534 -25.738 48.195
Omnibus: 2.915 Durbin-Watson: 2.539
Prob(Omnibus): 0.233 Jarque-Bera (JB): 1.448
Skew: 0.261 Prob(JB): 0.485
Kurtosis: 1.888 Cond. No. 157.

CP101

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

F-statistic p-value df difference
0.008 0.929 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.316
Method: Least Squares F-statistic: 3.155
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0684
Time: 05:15:17 Log-Likelihood: -70.644
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.0486 189.892 0.664 0.520 -291.901 543.998
C(dose)[T.1] -89.3161 265.667 -0.336 0.743 -674.045 495.413
expression -7.1423 23.092 -0.309 0.763 -57.966 43.682
expression:C(dose)[T.1] 17.0135 32.532 0.523 0.611 -54.589 88.616
Omnibus: 4.101 Durbin-Watson: 0.959
Prob(Omnibus): 0.129 Jarque-Bera (JB): 2.417
Skew: -0.983 Prob(JB): 0.299
Kurtosis: 3.067 Cond. No. 362.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.892
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 05:15:17 Log-Likelihood: -70.828
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.6950 129.902 0.429 0.676 -227.338 338.728
C(dose)[T.1] 49.3593 15.836 3.117 0.009 14.855 83.864
expression 1.4296 15.765 0.091 0.929 -32.920 35.780
Omnibus: 2.762 Durbin-Watson: 0.791
Prob(Omnibus): 0.251 Jarque-Bera (JB): 1.878
Skew: -0.849 Prob(JB): 0.391
Kurtosis: 2.648 Cond. No. 137.

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: 05:15:17 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: 0.003
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.04185
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.841
Time: 05:15:17 Log-Likelihood: -75.276
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 127.4073 165.234 0.771 0.454 -229.560 484.374
expression -4.1416 20.244 -0.205 0.841 -47.877 39.593
Omnibus: 0.557 Durbin-Watson: 1.626
Prob(Omnibus): 0.757 Jarque-Bera (JB): 0.566
Skew: 0.071 Prob(JB): 0.753
Kurtosis: 2.059 Cond. No. 135.