LID SPN TRAP

1454845_x_at

Mchr1 melanin-concentrating hormone receptor 1

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
0.327 0.574 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.679
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 13.41
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.20e-05
Time: 03:44:24 Log-Likelihood: -100.03
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -168.6579 195.848 -0.861 0.400 -578.573 241.257
C(dose)[T.1] 486.8659 359.069 1.356 0.191 -264.675 1238.407
expression 24.3317 21.372 1.138 0.269 -20.401 69.064
expression:C(dose)[T.1] -47.5568 39.461 -1.205 0.243 -130.150 35.037
Omnibus: 2.040 Durbin-Watson: 2.127
Prob(Omnibus): 0.361 Jarque-Bera (JB): 1.075
Skew: 0.026 Prob(JB): 0.584
Kurtosis: 1.942 Cond. No. 918.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.96
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.41e-05
Time: 03:44:24 Log-Likelihood: -100.88
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -40.8854 166.521 -0.246 0.809 -388.242 306.471
C(dose)[T.1] 54.2612 8.848 6.132 0.000 35.804 72.718
expression 10.3820 18.168 0.571 0.574 -27.516 48.280
Omnibus: 0.124 Durbin-Watson: 2.100
Prob(Omnibus): 0.940 Jarque-Bera (JB): 0.347
Skew: 0.001 Prob(JB): 0.841
Kurtosis: 2.398 Cond. No. 354.

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: 03:44:24 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: 0.005
Model: OLS Adj. R-squared: -0.042
Method: Least Squares F-statistic: 0.1138
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.739
Time: 03:44:24 Log-Likelihood: -113.04
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 170.7184 269.816 0.633 0.534 -390.395 731.832
expression -9.9816 29.585 -0.337 0.739 -71.506 51.543
Omnibus: 3.002 Durbin-Watson: 2.405
Prob(Omnibus): 0.223 Jarque-Bera (JB): 1.431
Skew: 0.235 Prob(JB): 0.489
Kurtosis: 1.872 Cond. No. 346.

CP101

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

F-statistic p-value df difference
16.003 0.002 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.783
Model: OLS Adj. R-squared: 0.724
Method: Least Squares F-statistic: 13.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000566
Time: 03:44:24 Log-Likelihood: -63.828
No. Observations: 15 AIC: 135.7
Df Residuals: 11 BIC: 138.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -327.3619 189.277 -1.730 0.112 -743.958 89.234
C(dose)[T.1] -235.6138 269.140 -0.875 0.400 -827.988 356.760
expression 41.8407 20.044 2.087 0.061 -2.276 85.958
expression:C(dose)[T.1] 27.9903 28.068 0.997 0.340 -33.786 89.767
Omnibus: 0.888 Durbin-Watson: 1.116
Prob(Omnibus): 0.641 Jarque-Bera (JB): 0.788
Skew: -0.464 Prob(JB): 0.674
Kurtosis: 2.367 Cond. No. 678.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.764
Model: OLS Adj. R-squared: 0.724
Method: Least Squares F-statistic: 19.40
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000174
Time: 03:44:24 Log-Likelihood: -64.478
No. Observations: 15 AIC: 135.0
Df Residuals: 12 BIC: 137.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -462.0535 132.573 -3.485 0.005 -750.906 -173.201
C(dose)[T.1] 32.5575 11.111 2.930 0.013 8.348 56.767
expression 56.1156 14.028 4.000 0.002 25.552 86.679
Omnibus: 1.227 Durbin-Watson: 1.397
Prob(Omnibus): 0.541 Jarque-Bera (JB): 0.906
Skew: -0.321 Prob(JB): 0.636
Kurtosis: 1.981 Cond. No. 251.

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: 03:44:24 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: 0.595
Model: OLS Adj. R-squared: 0.564
Method: Least Squares F-statistic: 19.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000761
Time: 03:44:24 Log-Likelihood: -68.525
No. Observations: 15 AIC: 141.1
Df Residuals: 13 BIC: 142.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -592.3001 157.169 -3.769 0.002 -931.844 -252.756
expression 71.5018 16.369 4.368 0.001 36.139 106.864
Omnibus: 1.496 Durbin-Watson: 2.030
Prob(Omnibus): 0.473 Jarque-Bera (JB): 0.848
Skew: -0.093 Prob(JB): 0.654
Kurtosis: 1.850 Cond. No. 236.