LID SPN TRAP

1460565_at

Slc41a1 solute carrier family 41, 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
0.619 0.441 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 12.83
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.18e-05
Time: 03:39:36 Log-Likelihood: -100.37
No. Observations: 23 AIC: 208.7
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 156.0996 279.450 0.559 0.583 -428.797 740.996
C(dose)[T.1] -172.6460 304.795 -0.566 0.578 -810.590 465.298
expression -12.5436 34.394 -0.365 0.719 -84.532 59.445
expression:C(dose)[T.1] 28.5390 37.794 0.755 0.459 -50.564 107.642
Omnibus: 0.858 Durbin-Watson: 1.682
Prob(Omnibus): 0.651 Jarque-Bera (JB): 0.279
Skew: -0.266 Prob(JB): 0.870
Kurtosis: 3.092 Cond. No. 842.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 19.38
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.09e-05
Time: 03:39:36 Log-Likelihood: -100.71
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -35.8980 114.702 -0.313 0.758 -275.161 203.365
C(dose)[T.1] 57.3859 10.054 5.707 0.000 36.413 78.359
expression 11.0928 14.102 0.787 0.441 -18.322 40.508
Omnibus: 0.224 Durbin-Watson: 1.906
Prob(Omnibus): 0.894 Jarque-Bera (JB): 0.122
Skew: -0.150 Prob(JB): 0.941
Kurtosis: 2.807 Cond. No. 215.

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: 03:39:37 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: 0.105
Model: OLS Adj. R-squared: 0.063
Method: Least Squares F-statistic: 2.467
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.131
Time: 03:39:37 Log-Likelihood: -111.83
No. Observations: 23 AIC: 227.7
Df Residuals: 21 BIC: 229.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 319.0212 152.503 2.092 0.049 1.874 636.169
expression -30.1072 19.167 -1.571 0.131 -69.968 9.754
Omnibus: 2.678 Durbin-Watson: 2.400
Prob(Omnibus): 0.262 Jarque-Bera (JB): 2.270
Skew: 0.721 Prob(JB): 0.321
Kurtosis: 2.465 Cond. No. 181.

CP101

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

F-statistic p-value df difference
0.296 0.596 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: 03:39:37 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 -151.5320 262.994 -0.576 0.576 -730.377 427.313
C(dose)[T.1] 317.9062 411.070 0.773 0.456 -586.852 1222.665
expression 26.2465 31.494 0.833 0.422 -43.071 95.564
expression:C(dose)[T.1] -32.0534 48.478 -0.661 0.522 -138.753 74.646
Omnibus: 1.902 Durbin-Watson: 1.030
Prob(Omnibus): 0.386 Jarque-Bera (JB): 1.344
Skew: -0.699 Prob(JB): 0.511
Kurtosis: 2.558 Cond. No. 571.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.462
Model: OLS Adj. R-squared: 0.372
Method: Least Squares F-statistic: 5.153
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0242
Time: 03:39:37 Log-Likelihood: -70.650
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.6744 195.330 -0.198 0.846 -464.262 386.914
C(dose)[T.1] 46.3365 16.413 2.823 0.015 10.575 82.098
expression 12.7184 23.374 0.544 0.596 -38.210 63.647
Omnibus: 3.189 Durbin-Watson: 0.803
Prob(Omnibus): 0.203 Jarque-Bera (JB): 2.071
Skew: -0.903 Prob(JB): 0.355
Kurtosis: 2.768 Cond. No. 217.

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: 03:39:37 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: 0.105
Model: OLS Adj. R-squared: 0.036
Method: Least Squares F-statistic: 1.521
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.239
Time: 03:39:37 Log-Likelihood: -74.470
No. Observations: 15 AIC: 152.9
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -192.7859 232.448 -0.829 0.422 -694.960 309.388
expression 33.8501 27.445 1.233 0.239 -25.441 93.141
Omnibus: 1.343 Durbin-Watson: 1.413
Prob(Omnibus): 0.511 Jarque-Bera (JB): 0.857
Skew: 0.201 Prob(JB): 0.651
Kurtosis: 1.900 Cond. No. 208.