LID SPN TRAP

1424534_at

Mmd2 monocyte to macrophage differentiation-associated 2

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
0.011 0.919 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.55
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.38e-05
Time: 05:00:40 Log-Likelihood: -100.54
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -4.0099 100.315 -0.040 0.969 -213.972 205.952
C(dose)[T.1] 185.5426 142.185 1.305 0.207 -112.054 483.139
expression 7.8810 13.555 0.581 0.568 -20.489 36.251
expression:C(dose)[T.1] -17.9817 19.293 -0.932 0.363 -58.362 22.399
Omnibus: 0.662 Durbin-Watson: 1.788
Prob(Omnibus): 0.718 Jarque-Bera (JB): 0.727
Skew: 0.303 Prob(JB): 0.695
Kurtosis: 2.374 Cond. No. 314.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 05:00:40 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.5592 71.278 0.864 0.398 -87.123 210.241
C(dose)[T.1] 53.2753 8.788 6.062 0.000 34.944 71.606
expression -0.9951 9.614 -0.104 0.919 -21.049 19.059
Omnibus: 0.435 Durbin-Watson: 1.901
Prob(Omnibus): 0.804 Jarque-Bera (JB): 0.550
Skew: 0.078 Prob(JB): 0.760
Kurtosis: 2.259 Cond. No. 122.

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: 05:00:40 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: 0.005
Model: OLS Adj. R-squared: -0.043
Method: Least Squares F-statistic: 0.09890
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.756
Time: 05:00:40 Log-Likelihood: -113.05
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 116.2003 116.234 1.000 0.329 -125.523 357.923
expression -4.9587 15.768 -0.314 0.756 -37.750 27.833
Omnibus: 2.702 Durbin-Watson: 2.550
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.410
Skew: 0.267 Prob(JB): 0.494
Kurtosis: 1.912 Cond. No. 121.

CP101

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

F-statistic p-value df difference
2.712 0.126 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.559
Model: OLS Adj. R-squared: 0.439
Method: Least Squares F-statistic: 4.651
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0247
Time: 05:00:40 Log-Likelihood: -69.156
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 -165.7673 247.202 -0.671 0.516 -709.855 378.320
C(dose)[T.1] -140.1684 396.247 -0.354 0.730 -1012.303 731.966
expression 29.4365 31.175 0.944 0.365 -39.179 98.052
expression:C(dose)[T.1] 23.2812 49.629 0.469 0.648 -85.951 132.513
Omnibus: 3.563 Durbin-Watson: 0.863
Prob(Omnibus): 0.168 Jarque-Bera (JB): 1.308
Skew: -0.231 Prob(JB): 0.520
Kurtosis: 1.629 Cond. No. 556.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.550
Model: OLS Adj. R-squared: 0.475
Method: Least Squares F-statistic: 7.344
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00826
Time: 05:00:40 Log-Likelihood: -69.305
No. Observations: 15 AIC: 144.6
Df Residuals: 12 BIC: 146.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -238.5437 186.101 -1.282 0.224 -644.023 166.936
C(dose)[T.1] 45.5835 14.384 3.169 0.008 14.244 76.923
expression 38.6231 23.455 1.647 0.126 -12.481 89.727
Omnibus: 3.750 Durbin-Watson: 0.907
Prob(Omnibus): 0.153 Jarque-Bera (JB): 1.319
Skew: -0.212 Prob(JB): 0.517
Kurtosis: 1.610 Cond. No. 213.

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: 05:00:40 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: 0.174
Model: OLS Adj. R-squared: 0.111
Method: Least Squares F-statistic: 2.740
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.122
Time: 05:00:40 Log-Likelihood: -73.866
No. Observations: 15 AIC: 151.7
Df Residuals: 13 BIC: 153.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -304.6219 240.809 -1.265 0.228 -824.858 215.615
expression 49.9617 30.185 1.655 0.122 -15.249 115.173
Omnibus: 3.241 Durbin-Watson: 1.540
Prob(Omnibus): 0.198 Jarque-Bera (JB): 1.356
Skew: 0.326 Prob(JB): 0.508
Kurtosis: 1.679 Cond. No. 211.