LID SPN TRAP

1433950_at

Igsf21 immunoglobin superfamily, member 21

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
1.041 0.320 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: 12.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.88e-05
Time: 03:51:40 Log-Likelihood: -100.47
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 132.7629 100.259 1.324 0.201 -77.081 342.607
C(dose)[T.1] 38.4480 143.904 0.267 0.792 -262.747 339.643
expression -12.3705 15.760 -0.785 0.442 -45.356 20.615
expression:C(dose)[T.1] 2.1158 22.887 0.092 0.927 -45.786 50.018
Omnibus: 0.819 Durbin-Watson: 1.782
Prob(Omnibus): 0.664 Jarque-Bera (JB): 0.838
Skew: 0.353 Prob(JB): 0.658
Kurtosis: 2.387 Cond. No. 272.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 19.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.71e-05
Time: 03:51:40 Log-Likelihood: -100.48
No. Observations: 23 AIC: 207.0
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.3921 70.994 1.780 0.090 -21.700 274.484
C(dose)[T.1] 51.7263 8.695 5.949 0.000 33.589 69.863
expression -11.3673 11.141 -1.020 0.320 -34.607 11.873
Omnibus: 0.907 Durbin-Watson: 1.787
Prob(Omnibus): 0.635 Jarque-Bera (JB): 0.902
Skew: 0.376 Prob(JB): 0.637
Kurtosis: 2.388 Cond. No. 108.

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: 03:51:40 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: 0.076
Model: OLS Adj. R-squared: 0.032
Method: Least Squares F-statistic: 1.730
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.203
Time: 03:51:40 Log-Likelihood: -112.19
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 226.7419 112.001 2.024 0.056 -6.177 459.661
expression -23.4027 17.794 -1.315 0.203 -60.407 13.601
Omnibus: 1.436 Durbin-Watson: 2.476
Prob(Omnibus): 0.488 Jarque-Bera (JB): 1.269
Skew: 0.443 Prob(JB): 0.530
Kurtosis: 2.266 Cond. No. 104.

CP101

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

F-statistic p-value df difference
0.873 0.369 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.537
Model: OLS Adj. R-squared: 0.411
Method: Least Squares F-statistic: 4.255
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0318
Time: 03:51:40 Log-Likelihood: -69.523
No. Observations: 15 AIC: 147.0
Df Residuals: 11 BIC: 149.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -90.3117 113.466 -0.796 0.443 -340.049 159.425
C(dose)[T.1] 271.4610 202.490 1.341 0.207 -174.218 717.139
expression 26.6428 19.074 1.397 0.190 -15.340 68.625
expression:C(dose)[T.1] -37.5437 34.112 -1.101 0.295 -112.624 37.537
Omnibus: 3.874 Durbin-Watson: 1.162
Prob(Omnibus): 0.144 Jarque-Bera (JB): 1.808
Skew: -0.817 Prob(JB): 0.405
Kurtosis: 3.473 Cond. No. 202.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.486
Model: OLS Adj. R-squared: 0.401
Method: Least Squares F-statistic: 5.677
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0184
Time: 03:51:40 Log-Likelihood: -70.306
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -20.8117 95.097 -0.219 0.830 -228.010 186.386
C(dose)[T.1] 49.2180 15.197 3.239 0.007 16.107 82.329
expression 14.9040 15.952 0.934 0.369 -19.853 49.661
Omnibus: 1.654 Durbin-Watson: 0.876
Prob(Omnibus): 0.437 Jarque-Bera (JB): 1.323
Skew: -0.630 Prob(JB): 0.516
Kurtosis: 2.272 Cond. No. 76.9

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: 03:51:40 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: 0.037
Model: OLS Adj. R-squared: -0.037
Method: Least Squares F-statistic: 0.4993
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.492
Time: 03:51:40 Log-Likelihood: -75.017
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 5.9022 124.607 0.047 0.963 -263.296 275.100
expression 14.8256 20.982 0.707 0.492 -30.503 60.154
Omnibus: 3.210 Durbin-Watson: 1.637
Prob(Omnibus): 0.201 Jarque-Bera (JB): 1.413
Skew: 0.374 Prob(JB): 0.493
Kurtosis: 1.696 Cond. No. 76.3