LID SPN TRAP

1457299_at

Grm4 glutamate receptor, metabotropic 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
3.079 0.095 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.699
Model: OLS Adj. R-squared: 0.651
Method: Least Squares F-statistic: 14.69
Date: Mon, 27 Jan 2025 Prob (F-statistic): 3.46e-05
Time: 22:41:16 Log-Likelihood: -99.309
No. Observations: 23 AIC: 206.6
Df Residuals: 19 BIC: 211.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -298.6407 215.206 -1.388 0.181 -749.071 151.790
C(dose)[T.1] 198.2256 380.168 0.521 0.608 -597.476 993.927
expression 35.9685 21.930 1.640 0.117 -9.931 81.868
expression:C(dose)[T.1] -15.8166 37.454 -0.422 0.678 -94.209 62.576
Omnibus: 0.168 Durbin-Watson: 2.054
Prob(Omnibus): 0.919 Jarque-Bera (JB): 0.140
Skew: 0.144 Prob(JB): 0.932
Kurtosis: 2.748 Cond. No. 1.13e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.696
Model: OLS Adj. R-squared: 0.665
Method: Least Squares F-statistic: 22.88
Date: Mon, 27 Jan 2025 Prob (F-statistic): 6.77e-06
Time: 22:41:16 Log-Likelihood: -99.416
No. Observations: 23 AIC: 204.8
Df Residuals: 20 BIC: 208.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -245.4494 170.871 -1.436 0.166 -601.880 110.981
C(dose)[T.1] 37.7680 12.057 3.132 0.005 12.617 62.919
expression 30.5463 17.409 1.755 0.095 -5.767 66.860
Omnibus: 0.746 Durbin-Watson: 2.085
Prob(Omnibus): 0.689 Jarque-Bera (JB): 0.372
Skew: 0.309 Prob(JB): 0.830
Kurtosis: 2.922 Cond. No. 427.

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: Mon, 27 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 22:41:16 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: 0.547
Model: OLS Adj. R-squared: 0.525
Method: Least Squares F-statistic: 25.32
Date: Mon, 27 Jan 2025 Prob (F-statistic): 5.55e-05
Time: 22:41:16 Log-Likelihood: -104.01
No. Observations: 23 AIC: 212.0
Df Residuals: 21 BIC: 214.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -630.8283 141.280 -4.465 0.000 -924.636 -337.021
expression 70.6750 14.044 5.032 0.000 41.468 99.881
Omnibus: 7.299 Durbin-Watson: 2.406
Prob(Omnibus): 0.026 Jarque-Bera (JB): 5.050
Skew: 1.038 Prob(JB): 0.0801
Kurtosis: 3.982 Cond. No. 295.

CP101

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

F-statistic p-value df difference
0.206 0.658 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: 3.688
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.0466
Time: 22:41:16 Log-Likelihood: -70.080
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 373.9883 292.310 1.279 0.227 -269.382 1017.359
C(dose)[T.1] -312.9456 370.256 -0.845 0.416 -1127.873 501.982
expression -34.5885 32.956 -1.050 0.316 -107.123 37.946
expression:C(dose)[T.1] 40.8901 41.814 0.978 0.349 -51.142 132.923
Omnibus: 2.492 Durbin-Watson: 0.964
Prob(Omnibus): 0.288 Jarque-Bera (JB): 1.780
Skew: -0.811 Prob(JB): 0.411
Kurtosis: 2.535 Cond. No. 597.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 5.072
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.0253
Time: 22:41:16 Log-Likelihood: -70.705
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 148.8695 179.811 0.828 0.424 -242.906 540.645
C(dose)[T.1] 48.8037 15.630 3.122 0.009 14.748 82.859
expression -9.1888 20.247 -0.454 0.658 -53.303 34.925
Omnibus: 3.510 Durbin-Watson: 0.849
Prob(Omnibus): 0.173 Jarque-Bera (JB): 2.010
Skew: -0.896 Prob(JB): 0.366
Kurtosis: 3.032 Cond. No. 207.

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: Mon, 27 Jan 2025 Prob (F-statistic): 0.00629
Time: 22:41:16 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: 0.018
Model: OLS Adj. R-squared: -0.058
Method: Least Squares F-statistic: 0.2355
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.636
Time: 22:41:16 Log-Likelihood: -75.165
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 205.8430 231.377 0.890 0.390 -294.016 705.702
expression -12.6893 26.148 -0.485 0.636 -69.179 43.801
Omnibus: 0.481 Durbin-Watson: 1.610
Prob(Omnibus): 0.786 Jarque-Bera (JB): 0.546
Skew: 0.136 Prob(JB): 0.761
Kurtosis: 2.106 Cond. No. 206.