LID SPN TRAP

1460386_a_at

Slc1a1 solute carrier family 1 (neuronal/epithelial high affinity glutamate transporter, system Xag), member 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
0.802 0.381 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 12.69
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.76e-05
Time: 05:01:48 Log-Likelihood: -100.46
No. Observations: 23 AIC: 208.9
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.7134 143.593 0.492 0.628 -229.830 371.257
C(dose)[T.1] 138.0033 175.394 0.787 0.441 -229.100 505.107
expression -2.2069 19.183 -0.115 0.910 -42.357 37.943
expression:C(dose)[T.1] -12.0002 23.816 -0.504 0.620 -61.848 37.848
Omnibus: 0.308 Durbin-Watson: 1.970
Prob(Omnibus): 0.857 Jarque-Bera (JB): 0.327
Skew: 0.233 Prob(JB): 0.849
Kurtosis: 2.649 Cond. No. 414.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 19.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.91e-05
Time: 05:01:48 Log-Likelihood: -100.61
No. Observations: 23 AIC: 207.2
Df Residuals: 20 BIC: 210.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.9361 83.639 1.542 0.139 -45.532 303.404
C(dose)[T.1] 49.7629 9.480 5.249 0.000 29.988 69.537
expression -9.9918 11.155 -0.896 0.381 -33.261 13.277
Omnibus: 0.136 Durbin-Watson: 2.005
Prob(Omnibus): 0.934 Jarque-Bera (JB): 0.340
Skew: 0.110 Prob(JB): 0.844
Kurtosis: 2.447 Cond. No. 146.

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: 05:01:48 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: 0.198
Model: OLS Adj. R-squared: 0.160
Method: Least Squares F-statistic: 5.175
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0335
Time: 05:01:48 Log-Likelihood: -110.57
No. Observations: 23 AIC: 225.1
Df Residuals: 21 BIC: 227.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 332.8612 111.464 2.986 0.007 101.059 564.663
expression -34.6400 15.227 -2.275 0.034 -66.306 -2.974
Omnibus: 0.571 Durbin-Watson: 2.642
Prob(Omnibus): 0.752 Jarque-Bera (JB): 0.479
Skew: 0.317 Prob(JB): 0.787
Kurtosis: 2.688 Cond. No. 129.

CP101

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

F-statistic p-value df difference
0.930 0.354 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.575
Model: OLS Adj. R-squared: 0.459
Method: Least Squares F-statistic: 4.966
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0203
Time: 05:01:48 Log-Likelihood: -68.878
No. Observations: 15 AIC: 145.8
Df Residuals: 11 BIC: 148.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -200.0240 150.327 -1.331 0.210 -530.891 130.843
C(dose)[T.1] 361.1431 208.921 1.729 0.112 -98.690 820.976
expression 35.0088 19.629 1.784 0.102 -8.194 78.212
expression:C(dose)[T.1] -40.8011 27.210 -1.499 0.162 -100.690 19.088
Omnibus: 4.448 Durbin-Watson: 1.039
Prob(Omnibus): 0.108 Jarque-Bera (JB): 2.312
Skew: -0.941 Prob(JB): 0.315
Kurtosis: 3.393 Cond. No. 304.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.488
Model: OLS Adj. R-squared: 0.403
Method: Least Squares F-statistic: 5.729
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0179
Time: 05:01:48 Log-Likelihood: -70.273
No. Observations: 15 AIC: 146.5
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -37.8141 109.679 -0.345 0.736 -276.784 201.156
C(dose)[T.1] 48.6176 15.175 3.204 0.008 15.555 81.681
expression 13.7760 14.283 0.964 0.354 -17.345 44.897
Omnibus: 2.967 Durbin-Watson: 0.817
Prob(Omnibus): 0.227 Jarque-Bera (JB): 1.630
Skew: -0.808 Prob(JB): 0.443
Kurtosis: 3.015 Cond. No. 114.

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: 05:01:48 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: 0.051
Model: OLS Adj. R-squared: -0.022
Method: Least Squares F-statistic: 0.6963
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.419
Time: 05:01:48 Log-Likelihood: -74.909
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.7511 143.451 -0.180 0.860 -335.658 284.156
expression 15.5857 18.678 0.834 0.419 -24.765 55.937
Omnibus: 2.767 Durbin-Watson: 1.594
Prob(Omnibus): 0.251 Jarque-Bera (JB): 1.347
Skew: 0.384 Prob(JB): 0.510
Kurtosis: 1.749 Cond. No. 113.