LID SPN TRAP

1431074_a_at

Pitpnc1 phosphatidylinositol transfer protein, cytoplasmic 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
3.160 0.091 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.700
Model: OLS Adj. R-squared: 0.652
Method: Least Squares F-statistic: 14.75
Date: Thu, 03 Apr 2025 Prob (F-statistic): 3.36e-05
Time: 23:01:29 Log-Likelihood: -99.273
No. Observations: 23 AIC: 206.5
Df Residuals: 19 BIC: 211.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -72.9930 105.640 -0.691 0.498 -294.100 148.114
C(dose)[T.1] 108.4673 119.043 0.911 0.374 -140.692 357.627
expression 19.9492 16.543 1.206 0.243 -14.676 54.574
expression:C(dose)[T.1] -7.8161 18.921 -0.413 0.684 -47.418 31.786
Omnibus: 1.552 Durbin-Watson: 2.100
Prob(Omnibus): 0.460 Jarque-Bera (JB): 1.051
Skew: 0.216 Prob(JB): 0.591
Kurtosis: 2.046 Cond. No. 264.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 23.00
Date: Thu, 03 Apr 2025 Prob (F-statistic): 6.53e-06
Time: 23:01:29 Log-Likelihood: -99.376
No. Observations: 23 AIC: 204.8
Df Residuals: 20 BIC: 208.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.8945 50.437 -0.692 0.497 -140.104 70.315
C(dose)[T.1] 59.4333 8.842 6.722 0.000 40.990 77.877
expression 13.9742 7.861 1.778 0.091 -2.423 30.371
Omnibus: 1.691 Durbin-Watson: 1.998
Prob(Omnibus): 0.429 Jarque-Bera (JB): 1.135
Skew: 0.257 Prob(JB): 0.567
Kurtosis: 2.041 Cond. No. 79.1

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 23:01:29 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: 0.012
Model: OLS Adj. R-squared: -0.035
Method: Least Squares F-statistic: 0.2608
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.615
Time: 23:01:29 Log-Likelihood: -112.96
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 119.9228 79.055 1.517 0.144 -44.482 284.328
expression -6.5188 12.765 -0.511 0.615 -33.065 20.027
Omnibus: 2.280 Durbin-Watson: 2.323
Prob(Omnibus): 0.320 Jarque-Bera (JB): 1.375
Skew: 0.314 Prob(JB): 0.503
Kurtosis: 1.980 Cond. No. 70.0

CP101

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

F-statistic p-value df difference
0.298 0.595 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.591
Model: OLS Adj. R-squared: 0.479
Method: Least Squares F-statistic: 5.294
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0167
Time: 23:01:29 Log-Likelihood: -68.598
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 22.1096 82.248 0.269 0.793 -158.916 203.136
C(dose)[T.1] 318.2220 145.308 2.190 0.051 -1.600 638.044
expression 6.9441 12.503 0.555 0.590 -20.574 34.462
expression:C(dose)[T.1] -41.1911 22.146 -1.860 0.090 -89.933 7.551
Omnibus: 0.382 Durbin-Watson: 1.153
Prob(Omnibus): 0.826 Jarque-Bera (JB): 0.447
Skew: -0.302 Prob(JB): 0.800
Kurtosis: 2.409 Cond. No. 170.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.372
Method: Least Squares F-statistic: 5.155
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0242
Time: 23:01:29 Log-Likelihood: -70.649
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 107.7919 74.795 1.441 0.175 -55.172 270.756
C(dose)[T.1] 49.2330 15.548 3.167 0.008 15.357 83.109
expression -6.1848 11.328 -0.546 0.595 -30.866 18.496
Omnibus: 2.078 Durbin-Watson: 0.947
Prob(Omnibus): 0.354 Jarque-Bera (JB): 1.569
Skew: -0.735 Prob(JB): 0.456
Kurtosis: 2.408 Cond. No. 65.0

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 23:01:29 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: 0.013
Model: OLS Adj. R-squared: -0.063
Method: Least Squares F-statistic: 0.1672
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.689
Time: 23:01:29 Log-Likelihood: -75.204
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 133.0395 96.805 1.374 0.193 -76.094 342.173
expression -6.0301 14.745 -0.409 0.689 -37.885 25.825
Omnibus: 0.518 Durbin-Watson: 1.674
Prob(Omnibus): 0.772 Jarque-Bera (JB): 0.548
Skew: -0.042 Prob(JB): 0.760
Kurtosis: 2.067 Cond. No. 64.4