LID SPN TRAP

1447669_s_at

Gng4 guanine nucleotide binding protein (G protein), gamma 4

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
0.431 0.519 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.603
Method: Least Squares F-statistic: 12.14
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000115
Time: 04:15:44 Log-Likelihood: -100.79
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 15.8629 77.725 0.204 0.840 -146.817 178.543
C(dose)[T.1] 5.0791 204.804 0.025 0.980 -423.580 433.738
expression 4.6023 9.300 0.495 0.626 -14.862 24.066
expression:C(dose)[T.1] 4.6873 22.341 0.210 0.836 -42.073 51.447
Omnibus: 0.022 Durbin-Watson: 1.886
Prob(Omnibus): 0.989 Jarque-Bera (JB): 0.104
Skew: -0.033 Prob(JB): 0.950
Kurtosis: 2.678 Cond. No. 484.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.29e-05
Time: 04:15:44 Log-Likelihood: -100.82
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 9.0962 69.007 0.132 0.896 -134.849 153.042
C(dose)[T.1] 47.9722 11.922 4.024 0.001 23.104 72.840
expression 5.4145 8.251 0.656 0.519 -11.797 22.626
Omnibus: 0.003 Durbin-Watson: 1.848
Prob(Omnibus): 0.999 Jarque-Bera (JB): 0.180
Skew: 0.004 Prob(JB): 0.914
Kurtosis: 2.566 Cond. No. 144.

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: 04:15:44 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: 0.378
Model: OLS Adj. R-squared: 0.349
Method: Least Squares F-statistic: 12.78
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00179
Time: 04:15:44 Log-Likelihood: -107.64
No. Observations: 23 AIC: 219.3
Df Residuals: 21 BIC: 221.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -168.4501 69.654 -2.418 0.025 -313.304 -23.596
expression 28.1828 7.884 3.575 0.002 11.788 44.578
Omnibus: 1.663 Durbin-Watson: 1.830
Prob(Omnibus): 0.435 Jarque-Bera (JB): 0.977
Skew: -0.003 Prob(JB): 0.613
Kurtosis: 1.990 Cond. No. 110.

CP101

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

F-statistic p-value df difference
3.167 0.100 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.564
Model: OLS Adj. R-squared: 0.445
Method: Least Squares F-statistic: 4.749
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0232
Time: 04:15:44 Log-Likelihood: -69.069
No. Observations: 15 AIC: 146.1
Df Residuals: 11 BIC: 149.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.5448 544.203 -0.087 0.932 -1245.327 1150.238
C(dose)[T.1] -18.3872 554.850 -0.033 0.974 -1239.604 1202.830
expression 12.7512 60.344 0.211 0.837 -120.064 145.566
expression:C(dose)[T.1] 6.4341 61.396 0.105 0.918 -128.698 141.566
Omnibus: 1.164 Durbin-Watson: 1.097
Prob(Omnibus): 0.559 Jarque-Bera (JB): 0.837
Skew: -0.251 Prob(JB): 0.658
Kurtosis: 1.957 Cond. No. 1.18e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.564
Model: OLS Adj. R-squared: 0.491
Method: Least Squares F-statistic: 7.758
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00688
Time: 04:15:44 Log-Likelihood: -69.076
No. Observations: 15 AIC: 144.2
Df Residuals: 12 BIC: 146.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -103.5869 96.637 -1.072 0.305 -314.142 106.968
C(dose)[T.1] 39.7361 14.975 2.653 0.021 7.107 72.365
expression 18.9666 10.657 1.780 0.100 -4.254 42.187
Omnibus: 1.334 Durbin-Watson: 1.072
Prob(Omnibus): 0.513 Jarque-Bera (JB): 0.870
Skew: -0.227 Prob(JB): 0.647
Kurtosis: 1.911 Cond. No. 131.

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: 04:15:44 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: 0.308
Model: OLS Adj. R-squared: 0.255
Method: Least Squares F-statistic: 5.786
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0318
Time: 04:15:44 Log-Likelihood: -72.539
No. Observations: 15 AIC: 149.1
Df Residuals: 13 BIC: 150.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -175.5755 112.250 -1.564 0.142 -418.077 66.926
expression 29.0047 12.058 2.405 0.032 2.955 55.055
Omnibus: 0.161 Durbin-Watson: 1.673
Prob(Omnibus): 0.922 Jarque-Bera (JB): 0.267
Skew: 0.198 Prob(JB): 0.875
Kurtosis: 2.481 Cond. No. 125.