LID SPN TRAP

1460352_s_at

Pik3r4 phosphatidylinositol 3 kinase, regulatory subunit, polypeptide 4, p150

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
0.756 0.395 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.672
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 12.99
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.56e-05
Time: 05:04:35 Log-Likelihood: -100.27
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 68.6688 272.407 0.252 0.804 -501.486 638.824
C(dose)[T.1] -223.3911 354.119 -0.631 0.536 -964.571 517.789
expression -1.5453 29.103 -0.053 0.958 -62.458 59.368
expression:C(dose)[T.1] 29.3596 37.714 0.778 0.446 -49.576 108.296
Omnibus: 1.713 Durbin-Watson: 2.057
Prob(Omnibus): 0.425 Jarque-Bera (JB): 1.099
Skew: 0.216 Prob(JB): 0.577
Kurtosis: 2.021 Cond. No. 1.05e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.96e-05
Time: 05:04:35 Log-Likelihood: -100.64
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -94.9366 171.602 -0.553 0.586 -452.892 263.018
C(dose)[T.1] 52.1995 8.707 5.995 0.000 34.036 70.363
expression 15.9379 18.327 0.870 0.395 -22.291 54.167
Omnibus: 3.263 Durbin-Watson: 2.047
Prob(Omnibus): 0.196 Jarque-Bera (JB): 1.368
Skew: 0.118 Prob(JB): 0.505
Kurtosis: 1.829 Cond. No. 380.

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: 05:04:35 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: 1.204
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.285
Time: 05:04:35 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 -224.9841 277.823 -0.810 0.427 -802.748 352.780
expression 32.4427 29.571 1.097 0.285 -29.054 93.940
Omnibus: 2.563 Durbin-Watson: 2.636
Prob(Omnibus): 0.278 Jarque-Bera (JB): 1.307
Skew: 0.210 Prob(JB): 0.520
Kurtosis: 1.910 Cond. No. 376.

CP101

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

F-statistic p-value df difference
0.184 0.676 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.555
Model: OLS Adj. R-squared: 0.433
Method: Least Squares F-statistic: 4.566
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0260
Time: 05:04:35 Log-Likelihood: -69.234
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -9.1911 178.275 -0.052 0.960 -401.571 383.189
C(dose)[T.1] 575.6327 339.378 1.696 0.118 -171.334 1322.599
expression 8.5793 19.925 0.431 0.675 -35.276 52.435
expression:C(dose)[T.1] -58.8262 37.899 -1.552 0.149 -142.242 24.590
Omnibus: 2.000 Durbin-Watson: 0.945
Prob(Omnibus): 0.368 Jarque-Bera (JB): 1.511
Skew: -0.630 Prob(JB): 0.470
Kurtosis: 2.088 Cond. No. 507.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 5.051
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0256
Time: 05:04:35 Log-Likelihood: -70.719
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 136.0218 160.418 0.848 0.413 -213.499 485.542
C(dose)[T.1] 49.3607 15.625 3.159 0.008 15.316 83.405
expression -7.6806 17.917 -0.429 0.676 -46.718 31.357
Omnibus: 1.768 Durbin-Watson: 0.849
Prob(Omnibus): 0.413 Jarque-Bera (JB): 1.390
Skew: -0.664 Prob(JB): 0.499
Kurtosis: 2.321 Cond. No. 187.

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: 05:04:35 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: 0.006
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.07299
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.791
Time: 05:04:35 Log-Likelihood: -75.258
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 149.9328 208.510 0.719 0.485 -300.526 600.391
expression -6.2922 23.290 -0.270 0.791 -56.607 44.023
Omnibus: 0.776 Durbin-Watson: 1.634
Prob(Omnibus): 0.678 Jarque-Bera (JB): 0.640
Skew: 0.035 Prob(JB): 0.726
Kurtosis: 1.991 Cond. No. 187.