LID SPN TRAP

1459872_x_at

H2-DMa histocompatibility 2, class II, locus DMa

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
1.374 0.255 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.643
Method: Least Squares F-statistic: 14.19
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.33e-05
Time: 04:49:45 Log-Likelihood: -99.585
No. Observations: 23 AIC: 207.2
Df Residuals: 19 BIC: 211.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.8191 94.093 0.487 0.632 -151.121 242.759
C(dose)[T.1] -82.8959 130.173 -0.637 0.532 -355.351 189.559
expression 1.5947 17.852 0.089 0.930 -35.770 38.959
expression:C(dose)[T.1] 28.4715 25.823 1.103 0.284 -25.576 82.519
Omnibus: 0.750 Durbin-Watson: 1.998
Prob(Omnibus): 0.687 Jarque-Bera (JB): 0.762
Skew: 0.359 Prob(JB): 0.683
Kurtosis: 2.472 Cond. No. 209.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.672
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 20.45
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.46e-05
Time: 04:49:45 Log-Likelihood: -100.30
No. Observations: 23 AIC: 206.6
Df Residuals: 20 BIC: 210.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -25.7659 68.472 -0.376 0.711 -168.595 117.064
C(dose)[T.1] 60.1858 10.300 5.843 0.000 38.700 81.672
expression 15.2024 12.968 1.172 0.255 -11.848 42.253
Omnibus: 0.324 Durbin-Watson: 2.038
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.423
Skew: 0.238 Prob(JB): 0.809
Kurtosis: 2.536 Cond. No. 85.5

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: 04:49:45 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: 0.111
Model: OLS Adj. R-squared: 0.069
Method: Least Squares F-statistic: 2.623
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.120
Time: 04:49:45 Log-Likelihood: -111.75
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 219.8468 86.788 2.533 0.019 39.361 400.333
expression -27.7750 17.149 -1.620 0.120 -63.439 7.889
Omnibus: 3.060 Durbin-Watson: 2.230
Prob(Omnibus): 0.217 Jarque-Bera (JB): 1.592
Skew: 0.333 Prob(JB): 0.451
Kurtosis: 1.896 Cond. No. 67.1

CP101

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

F-statistic p-value df difference
2.499 0.140 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.561
Model: OLS Adj. R-squared: 0.441
Method: Least Squares F-statistic: 4.679
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0242
Time: 04:49:45 Log-Likelihood: -69.131
No. Observations: 15 AIC: 146.3
Df Residuals: 11 BIC: 149.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.1759 175.608 -0.195 0.849 -420.687 352.336
C(dose)[T.1] -114.9041 244.072 -0.471 0.647 -652.104 422.295
expression 24.1108 41.594 0.580 0.574 -67.438 115.659
expression:C(dose)[T.1] 37.0818 56.998 0.651 0.529 -88.369 162.533
Omnibus: 2.225 Durbin-Watson: 1.091
Prob(Omnibus): 0.329 Jarque-Bera (JB): 1.631
Skew: -0.764 Prob(JB): 0.442
Kurtosis: 2.477 Cond. No. 203.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.544
Model: OLS Adj. R-squared: 0.468
Method: Least Squares F-statistic: 7.151
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00902
Time: 04:49:45 Log-Likelihood: -69.414
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -117.3936 117.393 -1.000 0.337 -373.170 138.383
C(dose)[T.1] 43.5805 14.754 2.954 0.012 11.435 75.726
expression 43.8584 27.747 1.581 0.140 -16.596 104.313
Omnibus: 2.496 Durbin-Watson: 1.155
Prob(Omnibus): 0.287 Jarque-Bera (JB): 1.848
Skew: -0.813 Prob(JB): 0.397
Kurtosis: 2.440 Cond. No. 74.9

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: 04:49:45 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: 0.212
Model: OLS Adj. R-squared: 0.151
Method: Least Squares F-statistic: 3.498
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0841
Time: 04:49:45 Log-Likelihood: -73.513
No. Observations: 15 AIC: 151.0
Df Residuals: 13 BIC: 152.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -178.6724 145.893 -1.225 0.242 -493.854 136.509
expression 63.5957 34.003 1.870 0.084 -9.864 137.055
Omnibus: 0.354 Durbin-Watson: 1.944
Prob(Omnibus): 0.838 Jarque-Bera (JB): 0.479
Skew: -0.063 Prob(JB): 0.787
Kurtosis: 2.133 Cond. No. 73.2