LID SPN TRAP

1422595_s_at

Sike1 suppressor of IKBKE 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
0.937 0.345 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 13.27
Date: Tue, 03 Dec 2024 Prob (F-statistic): 6.61e-05
Time: 11:44:01 Log-Likelihood: -100.11
No. Observations: 23 AIC: 208.2
Df Residuals: 19 BIC: 212.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.1561 128.924 0.405 0.690 -217.686 321.998
C(dose)[T.1] 196.5738 169.699 1.158 0.261 -158.609 551.757
expression 0.2554 16.026 0.016 0.987 -33.288 33.799
expression:C(dose)[T.1] -17.9202 21.139 -0.848 0.407 -62.164 26.324
Omnibus: 0.086 Durbin-Watson: 1.826
Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.068
Skew: 0.062 Prob(JB): 0.966
Kurtosis: 2.764 Cond. No. 430.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 19.83
Date: Tue, 03 Dec 2024 Prob (F-statistic): 1.79e-05
Time: 11:44:01 Log-Likelihood: -100.54
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 134.9284 83.596 1.614 0.122 -39.450 309.307
C(dose)[T.1] 52.9012 8.583 6.163 0.000 34.997 70.805
expression -10.0450 10.377 -0.968 0.345 -31.691 11.601
Omnibus: 0.126 Durbin-Watson: 1.948
Prob(Omnibus): 0.939 Jarque-Bera (JB): 0.349
Skew: -0.019 Prob(JB): 0.840
Kurtosis: 2.398 Cond. No. 159.

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: Tue, 03 Dec 2024 Prob (F-statistic): 3.51e-06
Time: 11:44:01 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: 0.028
Model: OLS Adj. R-squared: -0.018
Method: Least Squares F-statistic: 0.6057
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.445
Time: 11:44:01 Log-Likelihood: -112.78
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 187.1259 138.198 1.354 0.190 -100.273 474.525
expression -13.4008 17.219 -0.778 0.445 -49.211 22.409
Omnibus: 2.587 Durbin-Watson: 2.581
Prob(Omnibus): 0.274 Jarque-Bera (JB): 1.277
Skew: 0.175 Prob(JB): 0.528
Kurtosis: 1.900 Cond. No. 158.

CP101

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

F-statistic p-value df difference
0.508 0.489 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.488
Model: OLS Adj. R-squared: 0.348
Method: Least Squares F-statistic: 3.495
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0534
Time: 11:44:01 Log-Likelihood: -70.279
No. Observations: 15 AIC: 148.6
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 109.1794 272.874 0.400 0.697 -491.412 709.771
C(dose)[T.1] 301.8735 424.677 0.711 0.492 -632.834 1236.581
expression -5.7409 37.487 -0.153 0.881 -88.250 76.768
expression:C(dose)[T.1] -35.4576 58.960 -0.601 0.560 -165.228 94.313
Omnibus: 1.371 Durbin-Watson: 0.919
Prob(Omnibus): 0.504 Jarque-Bera (JB): 0.928
Skew: -0.579 Prob(JB): 0.629
Kurtosis: 2.623 Cond. No. 503.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.471
Model: OLS Adj. R-squared: 0.383
Method: Least Squares F-statistic: 5.346
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.0219
Time: 11:44:01 Log-Likelihood: -70.522
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 213.4228 205.064 1.041 0.319 -233.372 660.218
C(dose)[T.1] 46.6676 15.819 2.950 0.012 12.201 81.135
expression -20.0747 28.154 -0.713 0.489 -81.418 41.269
Omnibus: 2.027 Durbin-Watson: 0.872
Prob(Omnibus): 0.363 Jarque-Bera (JB): 1.389
Skew: -0.719 Prob(JB): 0.499
Kurtosis: 2.607 Cond. No. 196.

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: Tue, 03 Dec 2024 Prob (F-statistic): 0.00629
Time: 11:44:01 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: 0.088
Model: OLS Adj. R-squared: 0.017
Method: Least Squares F-statistic: 1.249
Date: Tue, 03 Dec 2024 Prob (F-statistic): 0.284
Time: 11:44:01 Log-Likelihood: -74.612
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 372.4855 249.675 1.492 0.160 -166.906 911.876
expression -38.6961 34.625 -1.118 0.284 -113.499 36.107
Omnibus: 0.116 Durbin-Watson: 1.636
Prob(Omnibus): 0.943 Jarque-Bera (JB): 0.339
Skew: 0.041 Prob(JB): 0.844
Kurtosis: 2.269 Cond. No. 189.