LID SPN TRAP

1460428_at

Ankrd13a ankyrin repeat domain 13a

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
1.726 0.204 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.708
Model: OLS Adj. R-squared: 0.661
Method: Least Squares F-statistic: 15.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.62e-05
Time: 04:08:22 Log-Likelihood: -98.967
No. Observations: 23 AIC: 205.9
Df Residuals: 19 BIC: 210.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.2096 291.888 0.155 0.879 -565.719 656.139
C(dose)[T.1] 651.0819 420.149 1.550 0.138 -228.301 1530.464
expression 1.0036 32.548 0.031 0.976 -67.120 69.128
expression:C(dose)[T.1] -65.3605 46.371 -1.410 0.175 -162.415 31.694
Omnibus: 0.276 Durbin-Watson: 1.418
Prob(Omnibus): 0.871 Jarque-Bera (JB): 0.458
Skew: 0.115 Prob(JB): 0.795
Kurtosis: 2.348 Cond. No. 1.21e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.645
Method: Least Squares F-statistic: 20.95
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.24e-05
Time: 04:08:22 Log-Likelihood: -100.11
No. Observations: 23 AIC: 206.2
Df Residuals: 20 BIC: 209.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 333.9389 213.007 1.568 0.133 -110.386 778.264
C(dose)[T.1] 59.0130 9.459 6.239 0.000 39.283 78.743
expression -31.1983 23.748 -1.314 0.204 -80.735 18.339
Omnibus: 2.690 Durbin-Watson: 1.791
Prob(Omnibus): 0.261 Jarque-Bera (JB): 1.218
Skew: -0.012 Prob(JB): 0.544
Kurtosis: 1.873 Cond. No. 465.

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: 04:08:22 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: 0.048
Model: OLS Adj. R-squared: 0.003
Method: Least Squares F-statistic: 1.063
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.314
Time: 04:08:22 Log-Likelihood: -112.54
No. Observations: 23 AIC: 229.1
Df Residuals: 21 BIC: 231.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -250.5254 320.451 -0.782 0.443 -916.940 415.889
expression 36.4779 35.388 1.031 0.314 -37.115 110.071
Omnibus: 1.796 Durbin-Watson: 2.399
Prob(Omnibus): 0.407 Jarque-Bera (JB): 1.415
Skew: 0.431 Prob(JB): 0.493
Kurtosis: 2.145 Cond. No. 417.

CP101

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

F-statistic p-value df difference
0.353 0.563 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.489
Model: OLS Adj. R-squared: 0.350
Method: Least Squares F-statistic: 3.513
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0527
Time: 04:08:22 Log-Likelihood: -70.260
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 668.1327 643.577 1.038 0.321 -748.371 2084.637
C(dose)[T.1] -570.9591 848.407 -0.673 0.515 -2438.289 1296.371
expression -67.1183 71.897 -0.934 0.371 -225.363 91.126
expression:C(dose)[T.1] 69.2988 94.910 0.730 0.481 -139.597 278.195
Omnibus: 1.743 Durbin-Watson: 0.685
Prob(Omnibus): 0.418 Jarque-Bera (JB): 1.266
Skew: -0.669 Prob(JB): 0.531
Kurtosis: 2.518 Cond. No. 1.34e+03

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: 5.205
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0236
Time: 04:08:22 Log-Likelihood: -70.615
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 312.2222 411.965 0.758 0.463 -585.373 1209.818
C(dose)[T.1] 48.3969 15.571 3.108 0.009 14.470 82.323
expression -27.3515 46.013 -0.594 0.563 -127.604 72.901
Omnibus: 3.671 Durbin-Watson: 0.642
Prob(Omnibus): 0.160 Jarque-Bera (JB): 2.178
Skew: -0.933 Prob(JB): 0.337
Kurtosis: 2.992 Cond. No. 482.

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: 04:08:22 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: 0.033
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.4503
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.514
Time: 04:08:22 Log-Likelihood: -75.045
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 448.4028 528.753 0.848 0.412 -693.898 1590.703
expression -39.7048 59.172 -0.671 0.514 -167.537 88.127
Omnibus: 2.622 Durbin-Watson: 1.585
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.085
Skew: 0.120 Prob(JB): 0.581
Kurtosis: 1.704 Cond. No. 479.