LID SPN TRAP

1460020_at

Ankrd11 ankyrin repeat domain 11

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
1.001 0.329 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 13.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.40e-05
Time: 05:07:24 Log-Likelihood: -100.07
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.5882 125.838 0.410 0.686 -211.794 314.970
C(dose)[T.1] -91.9291 169.214 -0.543 0.593 -446.099 262.241
expression 0.3855 18.494 0.021 0.984 -38.323 39.094
expression:C(dose)[T.1] 21.0829 24.717 0.853 0.404 -30.650 72.816
Omnibus: 4.023 Durbin-Watson: 1.883
Prob(Omnibus): 0.134 Jarque-Bera (JB): 1.810
Skew: 0.347 Prob(JB): 0.404
Kurtosis: 1.814 Cond. No. 366.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 19.92
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.74e-05
Time: 05:07:24 Log-Likelihood: -100.50
No. Observations: 23 AIC: 207.0
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -28.6348 83.033 -0.345 0.734 -201.838 144.568
C(dose)[T.1] 52.2161 8.631 6.050 0.000 34.211 70.221
expression 12.1889 12.186 1.000 0.329 -13.230 37.608
Omnibus: 2.709 Durbin-Watson: 1.940
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.351
Skew: 0.221 Prob(JB): 0.509
Kurtosis: 1.898 Cond. No. 136.

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: 05:07:24 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: 0.054
Model: OLS Adj. R-squared: 0.009
Method: Least Squares F-statistic: 1.203
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.285
Time: 05:07:24 Log-Likelihood: -112.46
No. Observations: 23 AIC: 228.9
Df Residuals: 21 BIC: 231.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -69.1359 135.868 -0.509 0.616 -351.689 213.418
expression 21.7603 19.836 1.097 0.285 -19.490 63.011
Omnibus: 2.083 Durbin-Watson: 2.412
Prob(Omnibus): 0.353 Jarque-Bera (JB): 1.203
Skew: 0.220 Prob(JB): 0.548
Kurtosis: 1.970 Cond. No. 135.

CP101

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

F-statistic p-value df difference
0.355 0.562 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.321
Method: Least Squares F-statistic: 3.206
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0659
Time: 05:07:24 Log-Likelihood: -70.588
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 97.3671 94.771 1.027 0.326 -111.222 305.956
C(dose)[T.1] 74.7556 144.075 0.519 0.614 -242.352 391.863
expression -4.8767 15.317 -0.318 0.756 -38.589 28.836
expression:C(dose)[T.1] -4.7646 24.214 -0.197 0.848 -58.060 48.531
Omnibus: 1.445 Durbin-Watson: 0.827
Prob(Omnibus): 0.486 Jarque-Bera (JB): 1.184
Skew: -0.580 Prob(JB): 0.553
Kurtosis: 2.261 Cond. No. 139.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.465
Model: OLS Adj. R-squared: 0.375
Method: Least Squares F-statistic: 5.207
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0235
Time: 05:07:24 Log-Likelihood: -70.614
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 109.0710 70.764 1.541 0.149 -45.110 263.252
C(dose)[T.1] 46.5996 16.112 2.892 0.014 11.495 81.704
expression -6.7832 11.378 -0.596 0.562 -31.574 18.008
Omnibus: 1.446 Durbin-Watson: 0.801
Prob(Omnibus): 0.485 Jarque-Bera (JB): 1.180
Skew: -0.588 Prob(JB): 0.554
Kurtosis: 2.290 Cond. No. 56.5

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: 05:07:24 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: 0.091
Model: OLS Adj. R-squared: 0.022
Method: Least Squares F-statistic: 1.308
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.273
Time: 05:07:24 Log-Likelihood: -74.581
No. Observations: 15 AIC: 153.2
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 186.7279 81.947 2.279 0.040 9.693 363.763
expression -15.6804 13.711 -1.144 0.273 -45.301 13.940
Omnibus: 2.402 Durbin-Watson: 1.401
Prob(Omnibus): 0.301 Jarque-Bera (JB): 1.116
Skew: 0.234 Prob(JB): 0.572
Kurtosis: 1.749 Cond. No. 52.0