LID SPN TRAP

1435458_at

Pim1 proviral integration site 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
0.490 0.492 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.603
Method: Least Squares F-statistic: 12.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000114
Time: 04:56:02 Log-Likelihood: -100.78
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -51.9309 203.813 -0.255 0.802 -478.516 374.654
C(dose)[T.1] 22.5334 369.246 0.061 0.952 -750.308 795.375
expression 11.2852 21.660 0.521 0.608 -34.051 56.621
expression:C(dose)[T.1] 2.3369 37.508 0.062 0.951 -76.168 80.842
Omnibus: 0.103 Durbin-Watson: 1.912
Prob(Omnibus): 0.950 Jarque-Bera (JB): 0.206
Skew: 0.135 Prob(JB): 0.902
Kurtosis: 2.623 Cond. No. 992.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.19
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.22e-05
Time: 04:56:02 Log-Likelihood: -100.78
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -59.2608 162.232 -0.365 0.719 -397.671 279.149
C(dose)[T.1] 45.5212 14.134 3.221 0.004 16.038 75.005
expression 12.0646 17.238 0.700 0.492 -23.892 48.021
Omnibus: 0.094 Durbin-Watson: 1.900
Prob(Omnibus): 0.954 Jarque-Bera (JB): 0.215
Skew: 0.130 Prob(JB): 0.898
Kurtosis: 2.604 Cond. No. 370.

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: 04:56:03 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: 0.480
Model: OLS Adj. R-squared: 0.455
Method: Least Squares F-statistic: 19.37
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000249
Time: 04:56:03 Log-Likelihood: -105.59
No. Observations: 23 AIC: 215.2
Df Residuals: 21 BIC: 217.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -463.6060 123.566 -3.752 0.001 -720.575 -206.637
expression 55.9264 12.708 4.401 0.000 29.499 82.354
Omnibus: 1.281 Durbin-Watson: 2.204
Prob(Omnibus): 0.527 Jarque-Bera (JB): 1.159
Skew: 0.480 Prob(JB): 0.560
Kurtosis: 2.463 Cond. No. 233.

CP101

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

F-statistic p-value df difference
1.469 0.249 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.511
Model: OLS Adj. R-squared: 0.378
Method: Least Squares F-statistic: 3.838
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0420
Time: 04:56:03 Log-Likelihood: -69.929
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 263.2773 251.555 1.047 0.318 -290.392 816.947
C(dose)[T.1] 140.9726 408.022 0.346 0.736 -757.078 1039.023
expression -23.4469 30.086 -0.779 0.452 -89.665 42.771
expression:C(dose)[T.1] -11.7292 49.468 -0.237 0.817 -120.607 97.149
Omnibus: 8.596 Durbin-Watson: 0.865
Prob(Omnibus): 0.014 Jarque-Bera (JB): 5.125
Skew: -1.322 Prob(JB): 0.0771
Kurtosis: 4.097 Cond. No. 556.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.509
Model: OLS Adj. R-squared: 0.427
Method: Least Squares F-statistic: 6.217
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0140
Time: 04:56:03 Log-Likelihood: -69.967
No. Observations: 15 AIC: 145.9
Df Residuals: 12 BIC: 148.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 299.5165 191.784 1.562 0.144 -118.344 717.377
C(dose)[T.1] 44.3018 15.395 2.878 0.014 10.758 77.846
expression -27.7854 22.923 -1.212 0.249 -77.731 22.160
Omnibus: 6.949 Durbin-Watson: 0.877
Prob(Omnibus): 0.031 Jarque-Bera (JB): 3.948
Skew: -1.196 Prob(JB): 0.139
Kurtosis: 3.774 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: 04:56:03 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: 0.170
Model: OLS Adj. R-squared: 0.106
Method: Least Squares F-statistic: 2.663
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.127
Time: 04:56:03 Log-Likelihood: -73.902
No. Observations: 15 AIC: 151.8
Df Residuals: 13 BIC: 153.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 466.0409 228.375 2.041 0.062 -27.334 959.416
expression -45.0876 27.629 -1.632 0.127 -104.777 14.602
Omnibus: 1.588 Durbin-Watson: 1.903
Prob(Omnibus): 0.452 Jarque-Bera (JB): 0.911
Skew: -0.188 Prob(JB): 0.634
Kurtosis: 1.853 Cond. No. 207.