LID SPN TRAP

1447612_x_at

Kdm6b KDM1 lysine (K)-specific demethylase 6B

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
0.413 0.528 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.97e-05
Time: 04:28:12 Log-Likelihood: -100.17
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 92.0466 282.800 0.325 0.748 -499.861 683.954
C(dose)[T.1] -454.7276 475.934 -0.955 0.351 -1450.869 541.414
expression -3.4438 25.733 -0.134 0.895 -57.303 50.415
expression:C(dose)[T.1] 44.4071 42.118 1.054 0.305 -43.747 132.561
Omnibus: 0.817 Durbin-Watson: 1.860
Prob(Omnibus): 0.665 Jarque-Bera (JB): 0.735
Skew: 0.124 Prob(JB): 0.692
Kurtosis: 2.160 Cond. No. 1.53e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.08
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.31e-05
Time: 04:28:12 Log-Likelihood: -100.83
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -90.0841 224.535 -0.401 0.693 -558.456 378.288
C(dose)[T.1] 46.8801 13.276 3.531 0.002 19.188 74.572
expression 13.1324 20.428 0.643 0.528 -29.480 55.745
Omnibus: 0.906 Durbin-Watson: 1.758
Prob(Omnibus): 0.636 Jarque-Bera (JB): 0.785
Skew: 0.156 Prob(JB): 0.675
Kurtosis: 2.150 Cond. No. 587.

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: 04:28:12 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: 0.442
Model: OLS Adj. R-squared: 0.415
Method: Least Squares F-statistic: 16.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000541
Time: 04:28:12 Log-Likelihood: -106.40
No. Observations: 23 AIC: 216.8
Df Residuals: 21 BIC: 219.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -680.1928 186.483 -3.647 0.002 -1068.006 -292.380
expression 67.7121 16.610 4.077 0.001 33.170 102.254
Omnibus: 3.699 Durbin-Watson: 1.828
Prob(Omnibus): 0.157 Jarque-Bera (JB): 1.406
Skew: 0.004 Prob(JB): 0.495
Kurtosis: 1.789 Cond. No. 391.

CP101

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

F-statistic p-value df difference
0.432 0.523 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.326
Method: Least Squares F-statistic: 3.255
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0635
Time: 04:28:12 Log-Likelihood: -70.535
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 371.1517 484.275 0.766 0.460 -694.731 1437.035
C(dose)[T.1] -117.8067 768.293 -0.153 0.881 -1808.808 1573.194
expression -26.2838 41.896 -0.627 0.543 -118.497 65.929
expression:C(dose)[T.1] 14.5163 66.256 0.219 0.831 -131.312 160.344
Omnibus: 3.414 Durbin-Watson: 0.608
Prob(Omnibus): 0.181 Jarque-Bera (JB): 2.225
Skew: -0.937 Prob(JB): 0.329
Kurtosis: 2.781 Cond. No. 1.42e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.468
Model: OLS Adj. R-squared: 0.379
Method: Least Squares F-statistic: 5.277
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0227
Time: 04:28:12 Log-Likelihood: -70.567
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 304.0786 360.045 0.845 0.415 -480.393 1088.550
C(dose)[T.1] 50.4849 15.587 3.239 0.007 16.523 84.446
expression -20.4794 31.143 -0.658 0.523 -88.333 47.374
Omnibus: 3.915 Durbin-Watson: 0.614
Prob(Omnibus): 0.141 Jarque-Bera (JB): 2.435
Skew: -0.986 Prob(JB): 0.296
Kurtosis: 2.933 Cond. No. 546.

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: 04:28:12 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: 0.003
Model: OLS Adj. R-squared: -0.074
Method: Least Squares F-statistic: 0.03685
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.851
Time: 04:28:12 Log-Likelihood: -75.279
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 184.0652 471.055 0.391 0.702 -833.588 1201.718
expression -7.8003 40.637 -0.192 0.851 -95.591 79.991
Omnibus: 0.673 Durbin-Watson: 1.631
Prob(Omnibus): 0.714 Jarque-Bera (JB): 0.607
Skew: 0.052 Prob(JB): 0.738
Kurtosis: 2.020 Cond. No. 542.