LID SPN TRAP

1455265_a_at

Rgs16 regulator of G-protein signaling 16

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
0.019 0.890 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.721
Model: OLS Adj. R-squared: 0.677
Method: Least Squares F-statistic: 16.34
Date: Thu, 03 Apr 2025 Prob (F-statistic): 1.71e-05
Time: 23:03:02 Log-Likelihood: -98.437
No. Observations: 23 AIC: 204.9
Df Residuals: 19 BIC: 209.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 152.0935 52.984 2.871 0.010 41.197 262.990
C(dose)[T.1] -83.3870 62.467 -1.335 0.198 -214.132 47.358
expression -30.6799 16.515 -1.858 0.079 -65.246 3.887
expression:C(dose)[T.1] 41.4744 18.834 2.202 0.040 2.054 80.895
Omnibus: 0.476 Durbin-Watson: 2.136
Prob(Omnibus): 0.788 Jarque-Bera (JB): 0.594
Skew: 0.189 Prob(JB): 0.743
Kurtosis: 2.310 Cond. No. 85.4

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.52
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.81e-05
Time: 23:03:02 Log-Likelihood: -101.05
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 50.3481 28.317 1.778 0.091 -8.721 109.417
C(dose)[T.1] 52.8441 9.451 5.592 0.000 33.130 72.558
expression 1.2099 8.670 0.140 0.890 -16.875 19.294
Omnibus: 0.379 Durbin-Watson: 1.862
Prob(Omnibus): 0.827 Jarque-Bera (JB): 0.518
Skew: 0.061 Prob(JB): 0.772
Kurtosis: 2.275 Cond. No. 24.4

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: 23:03:02 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: 0.101
Model: OLS Adj. R-squared: 0.059
Method: Least Squares F-statistic: 2.367
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.139
Time: 23:03:03 Log-Likelihood: -111.88
No. Observations: 23 AIC: 227.8
Df Residuals: 21 BIC: 230.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 14.2736 43.080 0.331 0.744 -75.317 103.864
expression 19.3311 12.564 1.539 0.139 -6.797 45.459
Omnibus: 0.209 Durbin-Watson: 2.205
Prob(Omnibus): 0.901 Jarque-Bera (JB): 0.408
Skew: 0.111 Prob(JB): 0.816
Kurtosis: 2.387 Cond. No. 23.4

CP101

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

F-statistic p-value df difference
1.639 0.225 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.587
Model: OLS Adj. R-squared: 0.474
Method: Least Squares F-statistic: 5.213
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0175
Time: 23:03:03 Log-Likelihood: -68.667
No. Observations: 15 AIC: 145.3
Df Residuals: 11 BIC: 148.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 175.9345 143.392 1.227 0.245 -139.668 491.538
C(dose)[T.1] -198.7671 164.052 -1.212 0.251 -559.844 162.310
expression -29.3264 38.653 -0.759 0.464 -114.401 55.749
expression:C(dose)[T.1] 58.1393 41.966 1.385 0.193 -34.228 150.507
Omnibus: 4.346 Durbin-Watson: 1.135
Prob(Omnibus): 0.114 Jarque-Bera (JB): 2.627
Skew: -1.025 Prob(JB): 0.269
Kurtosis: 3.052 Cond. No. 169.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.515
Model: OLS Adj. R-squared: 0.434
Method: Least Squares F-statistic: 6.371
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0130
Time: 23:03:03 Log-Likelihood: -69.873
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -6.5525 58.787 -0.111 0.913 -134.639 121.534
C(dose)[T.1] 26.3989 23.132 1.141 0.276 -24.002 76.800
expression 19.9952 15.619 1.280 0.225 -14.036 54.026
Omnibus: 3.143 Durbin-Watson: 0.544
Prob(Omnibus): 0.208 Jarque-Bera (JB): 2.142
Skew: -0.910 Prob(JB): 0.343
Kurtosis: 2.666 Cond. No. 38.6

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: 23:03:03 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: 0.462
Model: OLS Adj. R-squared: 0.421
Method: Least Squares F-statistic: 11.18
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00528
Time: 23:03:03 Log-Likelihood: -70.646
No. Observations: 15 AIC: 145.3
Df Residuals: 13 BIC: 146.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -51.5894 44.076 -1.170 0.263 -146.810 43.631
expression 33.7176 10.084 3.344 0.005 11.933 55.503
Omnibus: 1.746 Durbin-Watson: 0.728
Prob(Omnibus): 0.418 Jarque-Bera (JB): 1.388
Skew: -0.644 Prob(JB): 0.500
Kurtosis: 2.252 Cond. No. 27.2