LID SPN TRAP

1453119_at

Otud1 OTU 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
0.787 0.386 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 12.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.61e-05
Time: 04:36:54 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 274.6085 214.270 1.282 0.215 -173.863 723.080
C(dose)[T.1] -144.9471 350.338 -0.414 0.684 -878.213 588.318
expression -25.9896 25.257 -1.029 0.316 -78.852 26.873
expression:C(dose)[T.1] 23.2973 42.140 0.553 0.587 -64.903 111.498
Omnibus: 0.926 Durbin-Watson: 2.179
Prob(Omnibus): 0.630 Jarque-Bera (JB): 0.818
Skew: -0.200 Prob(JB): 0.664
Kurtosis: 2.167 Cond. No. 828.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 19.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.93e-05
Time: 04:36:54 Log-Likelihood: -100.62
No. Observations: 23 AIC: 207.2
Df Residuals: 20 BIC: 210.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 203.6387 168.555 1.208 0.241 -147.960 555.238
C(dose)[T.1] 48.6545 10.093 4.821 0.000 27.601 69.708
expression -17.6208 19.864 -0.887 0.386 -59.056 23.814
Omnibus: 0.619 Durbin-Watson: 2.089
Prob(Omnibus): 0.734 Jarque-Bera (JB): 0.594
Skew: -0.337 Prob(JB): 0.743
Kurtosis: 2.592 Cond. No. 333.

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: 04:36:54 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: 0.270
Model: OLS Adj. R-squared: 0.235
Method: Least Squares F-statistic: 7.767
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0110
Time: 04:36:54 Log-Likelihood: -109.49
No. Observations: 23 AIC: 223.0
Df Residuals: 21 BIC: 225.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 645.2614 203.024 3.178 0.005 223.049 1067.473
expression -67.7037 24.294 -2.787 0.011 -118.225 -17.182
Omnibus: 0.699 Durbin-Watson: 2.667
Prob(Omnibus): 0.705 Jarque-Bera (JB): 0.698
Skew: -0.156 Prob(JB): 0.705
Kurtosis: 2.205 Cond. No. 279.

CP101

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

F-statistic p-value df difference
1.996 0.183 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.592
Model: OLS Adj. R-squared: 0.480
Method: Least Squares F-statistic: 5.312
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0166
Time: 04:36:54 Log-Likelihood: -68.583
No. Observations: 15 AIC: 145.2
Df Residuals: 11 BIC: 148.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 100.9906 174.767 0.578 0.575 -283.668 485.650
C(dose)[T.1] 383.0144 257.001 1.490 0.164 -182.641 948.670
expression -3.9315 20.436 -0.192 0.851 -48.911 41.049
expression:C(dose)[T.1] -39.9723 30.387 -1.315 0.215 -106.854 26.910
Omnibus: 0.818 Durbin-Watson: 1.455
Prob(Omnibus): 0.664 Jarque-Bera (JB): 0.691
Skew: -0.185 Prob(JB): 0.708
Kurtosis: 2.016 Cond. No. 406.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.527
Model: OLS Adj. R-squared: 0.449
Method: Least Squares F-statistic: 6.696
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0111
Time: 04:36:54 Log-Likelihood: -69.679
No. Observations: 15 AIC: 145.4
Df Residuals: 12 BIC: 147.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 255.3298 133.410 1.914 0.080 -35.345 546.005
C(dose)[T.1] 45.4772 14.810 3.071 0.010 13.210 77.745
expression -22.0107 15.578 -1.413 0.183 -55.952 11.930
Omnibus: 1.818 Durbin-Watson: 1.301
Prob(Omnibus): 0.403 Jarque-Bera (JB): 1.121
Skew: -0.371 Prob(JB): 0.571
Kurtosis: 1.885 Cond. No. 158.

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: 04:36:54 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: 0.156
Model: OLS Adj. R-squared: 0.091
Method: Least Squares F-statistic: 2.403
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.145
Time: 04:36:54 Log-Likelihood: -74.028
No. Observations: 15 AIC: 152.1
Df Residuals: 13 BIC: 153.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 351.3993 166.510 2.110 0.055 -8.324 711.123
expression -30.5128 19.682 -1.550 0.145 -73.033 12.008
Omnibus: 0.214 Durbin-Watson: 1.901
Prob(Omnibus): 0.899 Jarque-Bera (JB): 0.404
Skew: -0.066 Prob(JB): 0.817
Kurtosis: 2.207 Cond. No. 153.