LID SPN TRAP

1433485_x_at

Gpr56 G protein-coupled receptor 56

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 0.829 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.608
Method: Least Squares F-statistic: 12.37
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000102
Time: 05:23:37 Log-Likelihood: -100.65
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.1922 87.361 0.288 0.776 -157.657 208.041
C(dose)[T.1] 174.9320 149.699 1.169 0.257 -138.392 488.256
expression 5.4329 16.317 0.333 0.743 -28.719 39.585
expression:C(dose)[T.1] -20.9673 26.100 -0.803 0.432 -75.594 33.660
Omnibus: 0.364 Durbin-Watson: 2.029
Prob(Omnibus): 0.834 Jarque-Bera (JB): 0.516
Skew: 0.123 Prob(JB): 0.773
Kurtosis: 2.309 Cond. No. 245.

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: 05:23:37 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 68.9622 67.682 1.019 0.320 -72.219 210.144
C(dose)[T.1] 55.0466 11.736 4.690 0.000 30.566 79.527
expression -2.7625 12.622 -0.219 0.829 -29.091 23.566
Omnibus: 0.523 Durbin-Watson: 1.875
Prob(Omnibus): 0.770 Jarque-Bera (JB): 0.594
Skew: 0.077 Prob(JB): 0.743
Kurtosis: 2.228 Cond. No. 91.2

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: 05:23:37 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: 0.265
Model: OLS Adj. R-squared: 0.230
Method: Least Squares F-statistic: 7.562
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0120
Time: 05:23:37 Log-Likelihood: -109.57
No. Observations: 23 AIC: 223.1
Df Residuals: 21 BIC: 225.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -126.7998 75.352 -1.683 0.107 -283.503 29.903
expression 36.6376 13.323 2.750 0.012 8.931 64.344
Omnibus: 1.489 Durbin-Watson: 1.888
Prob(Omnibus): 0.475 Jarque-Bera (JB): 1.064
Skew: 0.250 Prob(JB): 0.587
Kurtosis: 2.072 Cond. No. 71.0

CP101

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

F-statistic p-value df difference
0.242 0.631 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.483
Model: OLS Adj. R-squared: 0.342
Method: Least Squares F-statistic: 3.420
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0563
Time: 05:23:37 Log-Likelihood: -70.358
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 58.6550 94.974 0.618 0.549 -150.380 267.690
C(dose)[T.1] 151.2057 146.154 1.035 0.323 -170.477 472.889
expression 1.5228 16.360 0.093 0.928 -34.485 37.530
expression:C(dose)[T.1] -17.4615 24.997 -0.699 0.499 -72.479 37.556
Omnibus: 1.597 Durbin-Watson: 0.956
Prob(Omnibus): 0.450 Jarque-Bera (JB): 1.260
Skew: -0.633 Prob(JB): 0.532
Kurtosis: 2.358 Cond. No. 142.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 5.105
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0249
Time: 05:23:37 Log-Likelihood: -70.683
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 101.7483 70.653 1.440 0.175 -52.192 255.689
C(dose)[T.1] 49.7203 15.619 3.183 0.008 15.688 83.752
expression -5.9566 12.103 -0.492 0.631 -32.326 20.413
Omnibus: 2.084 Durbin-Watson: 0.803
Prob(Omnibus): 0.353 Jarque-Bera (JB): 1.550
Skew: -0.738 Prob(JB): 0.461
Kurtosis: 2.449 Cond. No. 54.9

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: 05:23:37 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: 0.003
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.04470
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.836
Time: 05:23:37 Log-Likelihood: -75.274
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 113.0153 92.074 1.227 0.241 -85.897 311.928
expression -3.3311 15.755 -0.211 0.836 -37.367 30.705
Omnibus: 0.494 Durbin-Watson: 1.620
Prob(Omnibus): 0.781 Jarque-Bera (JB): 0.539
Skew: 0.045 Prob(JB): 0.764
Kurtosis: 2.076 Cond. No. 54.6