LID SPN TRAP

1459869_x_at

Pdpr pyruvate dehydrogenase phosphatase regulatory subunit

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
0.001 0.974 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.689
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 14.00
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.71e-05
Time: 05:14:47 Log-Likelihood: -99.688
No. Observations: 23 AIC: 207.4
Df Residuals: 19 BIC: 211.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 182.1095 121.287 1.501 0.150 -71.746 435.965
C(dose)[T.1] -227.9168 181.315 -1.257 0.224 -607.413 151.579
expression -17.0806 16.178 -1.056 0.304 -50.942 16.781
expression:C(dose)[T.1] 37.8559 24.381 1.553 0.137 -13.174 88.886
Omnibus: 2.252 Durbin-Watson: 1.718
Prob(Omnibus): 0.324 Jarque-Bera (JB): 1.920
Skew: 0.654 Prob(JB): 0.383
Kurtosis: 2.457 Cond. No. 410.

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: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 05:14:47 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 57.2926 93.969 0.610 0.549 -138.723 253.308
C(dose)[T.1] 53.2932 8.871 6.008 0.000 34.789 71.797
expression -0.4119 12.523 -0.033 0.974 -26.534 25.710
Omnibus: 0.285 Durbin-Watson: 1.882
Prob(Omnibus): 0.867 Jarque-Bera (JB): 0.462
Skew: 0.051 Prob(JB): 0.794
Kurtosis: 2.313 Cond. No. 163.

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: 05:14:47 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: 0.016
Model: OLS Adj. R-squared: -0.031
Method: Least Squares F-statistic: 0.3363
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.568
Time: 05:14:47 Log-Likelihood: -112.92
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 166.9784 150.653 1.108 0.280 -146.321 480.278
expression -11.7332 20.234 -0.580 0.568 -53.812 30.346
Omnibus: 4.818 Durbin-Watson: 2.305
Prob(Omnibus): 0.090 Jarque-Bera (JB): 1.949
Skew: 0.350 Prob(JB): 0.377
Kurtosis: 1.757 Cond. No. 159.

CP101

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

F-statistic p-value df difference
0.499 0.494 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.508
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 3.781
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0437
Time: 05:14:47 Log-Likelihood: -69.985
No. Observations: 15 AIC: 148.0
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 95.8462 225.882 0.424 0.680 -401.317 593.010
C(dose)[T.1] 413.0662 411.688 1.003 0.337 -493.053 1319.186
expression -3.4261 27.198 -0.126 0.902 -63.289 56.437
expression:C(dose)[T.1] -47.4614 52.262 -0.908 0.383 -162.489 67.566
Omnibus: 0.835 Durbin-Watson: 0.738
Prob(Omnibus): 0.659 Jarque-Bera (JB): 0.789
Skew: -0.410 Prob(JB): 0.674
Kurtosis: 2.231 Cond. No. 520.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.471
Model: OLS Adj. R-squared: 0.383
Method: Least Squares F-statistic: 5.337
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0220
Time: 05:14:47 Log-Likelihood: -70.528
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 202.4690 191.558 1.057 0.311 -214.900 619.838
C(dose)[T.1] 39.6629 20.496 1.935 0.077 -4.995 84.321
expression -16.2807 23.055 -0.706 0.494 -66.513 33.951
Omnibus: 2.223 Durbin-Watson: 0.679
Prob(Omnibus): 0.329 Jarque-Bera (JB): 1.717
Skew: -0.738 Prob(JB): 0.424
Kurtosis: 2.247 Cond. No. 203.

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: 05:14:47 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: 0.306
Model: OLS Adj. R-squared: 0.252
Method: Least Squares F-statistic: 5.722
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0326
Time: 05:14:47 Log-Likelihood: -72.565
No. Observations: 15 AIC: 149.1
Df Residuals: 13 BIC: 150.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 458.1776 152.622 3.002 0.010 128.457 787.898
expression -45.6655 19.091 -2.392 0.033 -86.909 -4.422
Omnibus: 1.052 Durbin-Watson: 1.297
Prob(Omnibus): 0.591 Jarque-Bera (JB): 0.755
Skew: -0.159 Prob(JB): 0.686
Kurtosis: 1.948 Cond. No. 146.