LID SPN TRAP

1440346_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
1.831 0.191 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.686
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 13.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.03e-05
Time: 05:20:18 Log-Likelihood: -99.772
No. Observations: 23 AIC: 207.5
Df Residuals: 19 BIC: 212.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -120.1655 241.624 -0.497 0.625 -625.890 385.560
C(dose)[T.1] -262.5110 443.641 -0.592 0.561 -1191.063 666.041
expression 16.0665 22.256 0.722 0.479 -30.516 62.649
expression:C(dose)[T.1] 27.4318 39.812 0.689 0.499 -55.896 110.759
Omnibus: 0.048 Durbin-Watson: 1.674
Prob(Omnibus): 0.976 Jarque-Bera (JB): 0.133
Skew: 0.081 Prob(JB): 0.935
Kurtosis: 2.664 Cond. No. 1.40e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.646
Method: Least Squares F-statistic: 21.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.18e-05
Time: 05:20:18 Log-Likelihood: -100.06
No. Observations: 23 AIC: 206.1
Df Residuals: 20 BIC: 209.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -213.2098 197.720 -1.078 0.294 -625.646 199.227
C(dose)[T.1] 43.0707 11.315 3.807 0.001 19.468 66.673
expression 24.6395 18.210 1.353 0.191 -13.345 62.624
Omnibus: 0.042 Durbin-Watson: 1.585
Prob(Omnibus): 0.979 Jarque-Bera (JB): 0.167
Skew: 0.086 Prob(JB): 0.920
Kurtosis: 2.619 Cond. No. 527.

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: 05:20:18 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: 0.446
Model: OLS Adj. R-squared: 0.419
Method: Least Squares F-statistic: 16.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000502
Time: 05:20:18 Log-Likelihood: -106.32
No. Observations: 23 AIC: 216.6
Df Residuals: 21 BIC: 218.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -706.3373 191.421 -3.690 0.001 -1104.419 -308.256
expression 71.1201 17.312 4.108 0.001 35.117 107.123
Omnibus: 3.841 Durbin-Watson: 1.753
Prob(Omnibus): 0.147 Jarque-Bera (JB): 1.660
Skew: 0.282 Prob(JB): 0.436
Kurtosis: 1.811 Cond. No. 397.

CP101

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

F-statistic p-value df difference
0.522 0.484 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.489
Model: OLS Adj. R-squared: 0.350
Method: Least Squares F-statistic: 3.512
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0528
Time: 05:20:18 Log-Likelihood: -70.261
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 460.5276 423.740 1.087 0.300 -472.119 1393.174
C(dose)[T.1] -404.6969 744.537 -0.544 0.598 -2043.412 1234.018
expression -34.8592 37.562 -0.928 0.373 -117.534 47.815
expression:C(dose)[T.1] 40.1945 65.547 0.613 0.552 -104.074 184.463
Omnibus: 2.387 Durbin-Watson: 0.704
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.712
Skew: -0.793 Prob(JB): 0.425
Kurtosis: 2.525 Cond. No. 1.33e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.384
Method: Least Squares F-statistic: 5.359
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0217
Time: 05:20:18 Log-Likelihood: -70.514
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 311.6777 338.174 0.922 0.375 -425.140 1048.495
C(dose)[T.1] 51.7544 15.809 3.274 0.007 17.309 86.200
expression -21.6595 29.972 -0.723 0.484 -86.963 43.644
Omnibus: 4.662 Durbin-Watson: 0.646
Prob(Omnibus): 0.097 Jarque-Bera (JB): 2.713
Skew: -1.037 Prob(JB): 0.258
Kurtosis: 3.187 Cond. No. 503.

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: 05:20:18 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 6.406e-05
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.994
Time: 05:20:18 Log-Likelihood: -75.300
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 90.1620 437.996 0.206 0.840 -856.071 1036.395
expression 0.3091 38.614 0.008 0.994 -83.112 83.731
Omnibus: 0.575 Durbin-Watson: 1.621
Prob(Omnibus): 0.750 Jarque-Bera (JB): 0.570
Skew: 0.044 Prob(JB): 0.752
Kurtosis: 2.049 Cond. No. 493.