LID SPN TRAP

1448130_at

Fdft1 farnesyl diphosphate farnesyl transferase 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
5.353 0.031 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.755
Model: OLS Adj. R-squared: 0.717
Method: Least Squares F-statistic: 19.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.98e-06
Time: 03:32:16 Log-Likelihood: -96.922
No. Observations: 23 AIC: 201.8
Df Residuals: 19 BIC: 206.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -138.8637 219.880 -0.632 0.535 -599.078 321.351
C(dose)[T.1] -499.8780 350.702 -1.425 0.170 -1233.906 234.149
expression 20.7499 23.624 0.878 0.391 -28.697 70.196
expression:C(dose)[T.1] 59.3682 37.657 1.577 0.131 -19.449 138.186
Omnibus: 2.035 Durbin-Watson: 1.780
Prob(Omnibus): 0.362 Jarque-Bera (JB): 1.133
Skew: 0.541 Prob(JB): 0.567
Kurtosis: 3.108 Cond. No. 1.09e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.723
Model: OLS Adj. R-squared: 0.695
Method: Least Squares F-statistic: 26.12
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.65e-06
Time: 03:32:16 Log-Likelihood: -98.335
No. Observations: 23 AIC: 202.7
Df Residuals: 20 BIC: 206.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -356.2755 177.506 -2.007 0.058 -726.546 13.994
C(dose)[T.1] 52.8912 7.792 6.788 0.000 36.638 69.144
expression 44.1156 19.068 2.314 0.031 4.340 83.891
Omnibus: 0.432 Durbin-Watson: 1.929
Prob(Omnibus): 0.806 Jarque-Bera (JB): 0.534
Skew: 0.265 Prob(JB): 0.766
Kurtosis: 2.473 Cond. No. 430.

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: 03:32:16 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: 0.085
Model: OLS Adj. R-squared: 0.042
Method: Least Squares F-statistic: 1.958
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.176
Time: 03:32:16 Log-Likelihood: -112.08
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -360.7905 314.871 -1.146 0.265 -1015.601 294.020
expression 47.3178 33.814 1.399 0.176 -23.003 117.638
Omnibus: 4.386 Durbin-Watson: 2.468
Prob(Omnibus): 0.112 Jarque-Bera (JB): 1.556
Skew: 0.114 Prob(JB): 0.459
Kurtosis: 1.747 Cond. No. 430.

CP101

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

F-statistic p-value df difference
0.851 0.375 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.501
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 3.687
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0466
Time: 03:32:16 Log-Likelihood: -70.081
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -296.8372 341.617 -0.869 0.403 -1048.732 455.058
C(dose)[T.1] 354.4435 523.419 0.677 0.512 -797.594 1506.481
expression 39.7389 37.247 1.067 0.309 -42.242 121.720
expression:C(dose)[T.1] -33.4826 56.154 -0.596 0.563 -157.077 90.112
Omnibus: 2.213 Durbin-Watson: 0.897
Prob(Omnibus): 0.331 Jarque-Bera (JB): 1.448
Skew: -0.744 Prob(JB): 0.485
Kurtosis: 2.684 Cond. No. 818.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.485
Model: OLS Adj. R-squared: 0.399
Method: Least Squares F-statistic: 5.656
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0186
Time: 03:32:16 Log-Likelihood: -70.319
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -161.8022 248.799 -0.650 0.528 -703.889 380.284
C(dose)[T.1] 42.5198 16.845 2.524 0.027 5.818 79.221
expression 25.0075 27.115 0.922 0.375 -34.071 84.087
Omnibus: 2.694 Durbin-Watson: 0.789
Prob(Omnibus): 0.260 Jarque-Bera (JB): 1.899
Skew: -0.844 Prob(JB): 0.387
Kurtosis: 2.568 Cond. No. 310.

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: 03:32:16 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: 0.212
Model: OLS Adj. R-squared: 0.151
Method: Least Squares F-statistic: 3.496
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0842
Time: 03:32:16 Log-Likelihood: -73.514
No. Observations: 15 AIC: 151.0
Df Residuals: 13 BIC: 152.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -412.9483 271.088 -1.523 0.152 -998.599 172.702
expression 54.4229 29.105 1.870 0.084 -8.456 117.301
Omnibus: 3.586 Durbin-Watson: 1.577
Prob(Omnibus): 0.166 Jarque-Bera (JB): 1.374
Skew: 0.294 Prob(JB): 0.503
Kurtosis: 1.639 Cond. No. 283.