LID SPN TRAP

1452155_a_at

Ddx17 DEAD (Asp-Glu-Ala-Asp) box polypeptide 17

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
0.163 0.690 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 13.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.82e-05
Time: 04:06:39 Log-Likelihood: -100.15
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -70.2426 381.281 -0.184 0.856 -868.273 727.788
C(dose)[T.1] 870.4505 687.259 1.267 0.221 -567.999 2308.899
expression 12.8845 39.469 0.326 0.748 -69.726 95.495
expression:C(dose)[T.1] -83.5750 70.447 -1.186 0.250 -231.022 63.872
Omnibus: 0.297 Durbin-Watson: 1.863
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.472
Skew: -0.122 Prob(JB): 0.790
Kurtosis: 2.342 Cond. No. 1.88e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.61e-05
Time: 04:06:39 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 183.1564 319.037 0.574 0.572 -482.343 848.656
C(dose)[T.1] 55.1998 9.875 5.590 0.000 34.601 75.799
expression -13.3501 33.024 -0.404 0.690 -82.237 55.537
Omnibus: 0.243 Durbin-Watson: 1.961
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.434
Skew: 0.115 Prob(JB): 0.805
Kurtosis: 2.368 Cond. No. 720.

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: 04:06:39 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: 0.108
Model: OLS Adj. R-squared: 0.066
Method: Least Squares F-statistic: 2.544
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.126
Time: 04:06:39 Log-Likelihood: -111.79
No. Observations: 23 AIC: 227.6
Df Residuals: 21 BIC: 229.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -628.1439 443.822 -1.415 0.172 -1551.121 294.834
expression 72.7824 45.628 1.595 0.126 -22.107 167.672
Omnibus: 0.773 Durbin-Watson: 2.432
Prob(Omnibus): 0.680 Jarque-Bera (JB): 0.798
Skew: 0.289 Prob(JB): 0.671
Kurtosis: 2.294 Cond. No. 640.

CP101

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

F-statistic p-value df difference
0.037 0.851 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.306
Method: Least Squares F-statistic: 3.059
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0735
Time: 04:06:39 Log-Likelihood: -70.750
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 68.7898 490.497 0.140 0.891 -1010.787 1148.366
C(dose)[T.1] 331.9869 948.928 0.350 0.733 -1756.590 2420.564
expression -0.1397 50.325 -0.003 0.998 -110.904 110.625
expression:C(dose)[T.1] -28.6619 96.493 -0.297 0.772 -241.041 183.717
Omnibus: 2.798 Durbin-Watson: 0.741
Prob(Omnibus): 0.247 Jarque-Bera (JB): 1.882
Skew: -0.852 Prob(JB): 0.390
Kurtosis: 2.670 Cond. No. 1.40e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.918
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0275
Time: 04:06:39 Log-Likelihood: -70.810
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 144.7540 402.336 0.360 0.725 -731.860 1021.368
C(dose)[T.1] 50.1656 16.504 3.040 0.010 14.206 86.125
expression -7.9359 41.275 -0.192 0.851 -97.867 81.995
Omnibus: 3.094 Durbin-Watson: 0.840
Prob(Omnibus): 0.213 Jarque-Bera (JB): 2.087
Skew: -0.900 Prob(JB): 0.352
Kurtosis: 2.684 Cond. No. 510.

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: 04:06:39 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: 0.027
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.3657
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.556
Time: 04:06:40 Log-Likelihood: -75.092
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -204.3363 492.866 -0.415 0.685 -1269.109 860.437
expression 30.3811 50.237 0.605 0.556 -78.149 138.911
Omnibus: 1.243 Durbin-Watson: 1.483
Prob(Omnibus): 0.537 Jarque-Bera (JB): 0.791
Skew: 0.113 Prob(JB): 0.673
Kurtosis: 1.898 Cond. No. 488.