LID SPN TRAP

1435066_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
2.411 0.136 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.723
Model: OLS Adj. R-squared: 0.679
Method: Least Squares F-statistic: 16.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.58e-05
Time: 04:18:31 Log-Likelihood: -98.339
No. Observations: 23 AIC: 204.7
Df Residuals: 19 BIC: 209.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 112.1782 95.143 1.179 0.253 -86.959 311.316
C(dose)[T.1] 347.7582 188.360 1.846 0.080 -46.484 742.000
expression -6.8171 11.170 -0.610 0.549 -30.196 16.561
expression:C(dose)[T.1] -35.3687 22.428 -1.577 0.131 -82.311 11.574
Omnibus: 3.270 Durbin-Watson: 1.833
Prob(Omnibus): 0.195 Jarque-Bera (JB): 1.822
Skew: 0.660 Prob(JB): 0.402
Kurtosis: 3.399 Cond. No. 473.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.687
Model: OLS Adj. R-squared: 0.655
Method: Least Squares F-statistic: 21.93
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.08e-06
Time: 04:18:31 Log-Likelihood: -99.754
No. Observations: 23 AIC: 205.5
Df Residuals: 20 BIC: 208.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 186.7750 85.564 2.183 0.041 8.291 365.259
C(dose)[T.1] 50.9939 8.421 6.056 0.000 33.428 68.560
expression -15.5895 10.040 -1.553 0.136 -36.532 5.353
Omnibus: 1.333 Durbin-Watson: 1.737
Prob(Omnibus): 0.513 Jarque-Bera (JB): 0.573
Skew: 0.380 Prob(JB): 0.751
Kurtosis: 3.140 Cond. No. 177.

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: 04:18:31 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: 0.113
Model: OLS Adj. R-squared: 0.070
Method: Least Squares F-statistic: 2.664
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.118
Time: 04:18:31 Log-Likelihood: -111.73
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 303.0225 136.976 2.212 0.038 18.164 587.881
expression -26.4839 16.225 -1.632 0.118 -60.226 7.259
Omnibus: 2.877 Durbin-Watson: 2.127
Prob(Omnibus): 0.237 Jarque-Bera (JB): 1.341
Skew: 0.178 Prob(JB): 0.511
Kurtosis: 1.872 Cond. No. 172.

CP101

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

F-statistic p-value df difference
0.010 0.922 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 3.627
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0486
Time: 04:18:31 Log-Likelihood: -70.142
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -20.3698 222.634 -0.091 0.929 -510.383 469.644
C(dose)[T.1] 546.8439 484.822 1.128 0.283 -520.241 1613.929
expression 10.1933 25.813 0.395 0.700 -46.621 67.008
expression:C(dose)[T.1] -56.1264 54.723 -1.026 0.327 -176.572 64.319
Omnibus: 1.685 Durbin-Watson: 0.881
Prob(Omnibus): 0.431 Jarque-Bera (JB): 1.279
Skew: -0.658 Prob(JB): 0.528
Kurtosis: 2.439 Cond. No. 661.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.894
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 04:18:31 Log-Likelihood: -70.827
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 87.1970 196.808 0.443 0.666 -341.610 516.004
C(dose)[T.1] 49.9066 17.244 2.894 0.013 12.335 87.478
expression -2.2951 22.810 -0.101 0.922 -51.994 47.404
Omnibus: 2.808 Durbin-Watson: 0.828
Prob(Omnibus): 0.246 Jarque-Bera (JB): 1.919
Skew: -0.857 Prob(JB): 0.383
Kurtosis: 2.639 Cond. No. 224.

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: 04:18:32 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: 0.065
Model: OLS Adj. R-squared: -0.007
Method: Least Squares F-statistic: 0.9007
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.360
Time: 04:18:32 Log-Likelihood: -74.798
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -123.4064 228.934 -0.539 0.599 -617.988 371.175
expression 24.7282 26.055 0.949 0.360 -31.561 81.017
Omnibus: 2.041 Durbin-Watson: 1.264
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.002
Skew: 0.172 Prob(JB): 0.606
Kurtosis: 1.782 Cond. No. 207.