LID SPN TRAP

1417110_at

Man1a mannosidase 1, alpha

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
0.681 0.419 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.67
Date: Thu, 03 Apr 2025 Prob (F-statistic): 5.50e-05
Time: 22:46:35 Log-Likelihood: -99.881
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.3559 174.975 0.191 0.851 -332.871 399.583
C(dose)[T.1] -350.9217 345.141 -1.017 0.322 -1073.309 371.466
expression 2.4969 20.939 0.119 0.906 -41.330 46.324
expression:C(dose)[T.1] 47.8345 40.970 1.168 0.257 -37.917 133.586
Omnibus: 0.315 Durbin-Watson: 1.950
Prob(Omnibus): 0.854 Jarque-Bera (JB): 0.482
Skew: 0.177 Prob(JB): 0.786
Kurtosis: 2.386 Cond. No. 813.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 19.47
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.03e-05
Time: 22:46:35 Log-Likelihood: -100.68
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 -70.9956 151.786 -0.468 0.645 -387.615 245.624
C(dose)[T.1] 51.9162 8.794 5.903 0.000 33.572 70.261
expression 14.9918 18.161 0.826 0.419 -22.891 52.874
Omnibus: 0.063 Durbin-Watson: 1.942
Prob(Omnibus): 0.969 Jarque-Bera (JB): 0.187
Skew: 0.106 Prob(JB): 0.911
Kurtosis: 2.611 Cond. No. 301.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:46:35 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: 0.069
Model: OLS Adj. R-squared: 0.025
Method: Least Squares F-statistic: 1.562
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.225
Time: 22:46:35 Log-Likelihood: -112.28
No. Observations: 23 AIC: 228.6
Df Residuals: 21 BIC: 230.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -222.3586 241.783 -0.920 0.368 -725.174 280.457
expression 35.9751 28.783 1.250 0.225 -23.882 95.832
Omnibus: 1.935 Durbin-Watson: 2.348
Prob(Omnibus): 0.380 Jarque-Bera (JB): 1.095
Skew: 0.139 Prob(JB): 0.578
Kurtosis: 1.968 Cond. No. 296.

CP101

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

F-statistic p-value df difference
0.112 0.744 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.532
Model: OLS Adj. R-squared: 0.404
Method: Least Squares F-statistic: 4.169
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0336
Time: 22:46:35 Log-Likelihood: -69.604
No. Observations: 15 AIC: 147.2
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -10.2287 188.460 -0.054 0.958 -425.027 404.570
C(dose)[T.1] 546.7238 366.361 1.492 0.164 -259.632 1353.080
expression 10.7755 26.105 0.413 0.688 -46.682 68.233
expression:C(dose)[T.1] -67.7836 49.991 -1.356 0.202 -177.813 42.246
Omnibus: 0.310 Durbin-Watson: 1.002
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.376
Skew: -0.275 Prob(JB): 0.829
Kurtosis: 2.454 Cond. No. 438.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.986
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0265
Time: 22:46:35 Log-Likelihood: -70.763
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 122.9817 166.351 0.739 0.474 -239.467 485.430
C(dose)[T.1] 50.4165 16.085 3.134 0.009 15.370 85.463
expression -7.7084 23.028 -0.335 0.744 -57.882 42.465
Omnibus: 2.909 Durbin-Watson: 0.949
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.958
Skew: -0.870 Prob(JB): 0.376
Kurtosis: 2.676 Cond. No. 159.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:46:35 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: 0.007
Model: OLS Adj. R-squared: -0.070
Method: Least Squares F-statistic: 0.08852
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.771
Time: 22:46:35 Log-Likelihood: -75.249
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 30.6255 212.130 0.144 0.887 -427.655 488.905
expression 8.6462 29.061 0.298 0.771 -54.136 71.428
Omnibus: 1.311 Durbin-Watson: 1.519
Prob(Omnibus): 0.519 Jarque-Bera (JB): 0.805
Skew: 0.100 Prob(JB): 0.669
Kurtosis: 1.883 Cond. No. 156.