LID SPN TRAP

1455522_at

Arhgef15 Rho guanine nucleotide exchange factor (GEF) 15

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
0.048 0.828 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.713
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 15.70
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.23e-05
Time: 04:48:13 Log-Likelihood: -98.768
No. Observations: 23 AIC: 205.5
Df Residuals: 19 BIC: 210.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -139.1384 123.435 -1.127 0.274 -397.492 119.215
C(dose)[T.1] 419.9438 180.403 2.328 0.031 42.356 797.532
expression 26.3628 16.813 1.568 0.133 -8.827 61.553
expression:C(dose)[T.1] -49.8597 24.503 -2.035 0.056 -101.144 1.425
Omnibus: 1.538 Durbin-Watson: 1.847
Prob(Omnibus): 0.464 Jarque-Bera (JB): 0.529
Skew: -0.324 Prob(JB): 0.768
Kurtosis: 3.362 Cond. No. 425.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 04:48:13 Log-Likelihood: -101.04
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.0295 96.675 0.342 0.736 -168.632 234.691
C(dose)[T.1] 53.2226 8.775 6.065 0.000 34.919 71.527
expression 2.8877 13.156 0.220 0.828 -24.555 30.330
Omnibus: 0.355 Durbin-Watson: 1.919
Prob(Omnibus): 0.837 Jarque-Bera (JB): 0.508
Skew: 0.102 Prob(JB): 0.776
Kurtosis: 2.301 Cond. No. 166.

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: 04:48:13 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: 0.006
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.1249
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.727
Time: 04:48:13 Log-Likelihood: -113.04
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 23.5887 158.957 0.148 0.883 -306.981 354.158
expression 7.6334 21.596 0.353 0.727 -37.277 52.544
Omnibus: 2.743 Durbin-Watson: 2.497
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.661
Skew: 0.407 Prob(JB): 0.436
Kurtosis: 1.966 Cond. No. 166.

CP101

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

F-statistic p-value df difference
0.066 0.802 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.303
Method: Least Squares F-statistic: 3.027
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0753
Time: 04:48:13 Log-Likelihood: -70.786
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.3332 399.706 0.116 0.910 -833.413 926.079
C(dose)[T.1] 9.0297 465.755 0.019 0.985 -1016.089 1034.149
expression 2.8259 53.520 0.053 0.959 -114.970 120.622
expression:C(dose)[T.1] 5.7294 63.061 0.091 0.929 -133.067 144.526
Omnibus: 3.167 Durbin-Watson: 0.812
Prob(Omnibus): 0.205 Jarque-Bera (JB): 2.060
Skew: -0.900 Prob(JB): 0.357
Kurtosis: 2.762 Cond. No. 621.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.944
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0271
Time: 04:48:13 Log-Likelihood: -70.792
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 15.5266 202.708 0.077 0.940 -426.136 457.189
C(dose)[T.1] 51.3122 17.733 2.894 0.013 12.676 89.949
expression 6.9527 27.111 0.256 0.802 -52.117 66.022
Omnibus: 3.156 Durbin-Watson: 0.812
Prob(Omnibus): 0.206 Jarque-Bera (JB): 2.076
Skew: -0.902 Prob(JB): 0.354
Kurtosis: 2.740 Cond. No. 193.

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: 04:48:13 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: 0.069
Model: OLS Adj. R-squared: -0.002
Method: Least Squares F-statistic: 0.9672
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.343
Time: 04:48:13 Log-Likelihood: -74.762
No. Observations: 15 AIC: 153.5
Df Residuals: 13 BIC: 154.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 309.4296 219.610 1.409 0.182 -165.008 783.867
expression -29.5454 30.042 -0.983 0.343 -94.448 35.357
Omnibus: 0.707 Durbin-Watson: 1.368
Prob(Omnibus): 0.702 Jarque-Bera (JB): 0.643
Skew: 0.163 Prob(JB): 0.725
Kurtosis: 2.039 Cond. No. 167.