LID SPN TRAP

1460278_a_at

Pppde2 PPPDE peptidase domain containing 2

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
1.652 0.213 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.738
Model: OLS Adj. R-squared: 0.697
Method: Least Squares F-statistic: 17.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.32e-06
Time: 03:44:50 Log-Likelihood: -97.692
No. Observations: 23 AIC: 203.4
Df Residuals: 19 BIC: 207.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 109.7833 168.091 0.653 0.522 -242.035 461.602
C(dose)[T.1] -499.0392 258.998 -1.927 0.069 -1041.128 43.049
expression -6.3148 19.090 -0.331 0.744 -46.270 33.641
expression:C(dose)[T.1] 62.2832 29.271 2.128 0.047 1.019 123.548
Omnibus: 0.737 Durbin-Watson: 1.745
Prob(Omnibus): 0.692 Jarque-Bera (JB): 0.781
Skew: 0.309 Prob(JB): 0.677
Kurtosis: 2.342 Cond. No. 750.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.676
Model: OLS Adj. R-squared: 0.643
Method: Least Squares F-statistic: 20.85
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.28e-05
Time: 03:44:50 Log-Likelihood: -100.15
No. Observations: 23 AIC: 206.3
Df Residuals: 20 BIC: 209.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -123.3620 138.257 -0.892 0.383 -411.762 165.038
C(dose)[T.1] 51.8094 8.512 6.087 0.000 34.054 69.565
expression 20.1767 15.696 1.285 0.213 -12.564 52.918
Omnibus: 1.248 Durbin-Watson: 1.926
Prob(Omnibus): 0.536 Jarque-Bera (JB): 1.042
Skew: 0.312 Prob(JB): 0.594
Kurtosis: 2.164 Cond. No. 294.

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: 03:44:50 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: 0.075
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 1.712
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.205
Time: 03:44:50 Log-Likelihood: -112.20
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -216.4536 226.477 -0.956 0.350 -687.439 254.532
expression 33.5150 25.616 1.308 0.205 -19.757 86.787
Omnibus: 4.219 Durbin-Watson: 2.735
Prob(Omnibus): 0.121 Jarque-Bera (JB): 1.499
Skew: 0.052 Prob(JB): 0.473
Kurtosis: 1.754 Cond. No. 292.

CP101

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

F-statistic p-value df difference
0.137 0.718 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.468
Model: OLS Adj. R-squared: 0.323
Method: Least Squares F-statistic: 3.224
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0650
Time: 03:44:50 Log-Likelihood: -70.568
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -147.1257 810.458 -0.182 0.859 -1930.931 1636.679
C(dose)[T.1] 528.5801 934.270 0.566 0.583 -1527.734 2584.894
expression 23.2308 87.743 0.265 0.796 -169.890 216.352
expression:C(dose)[T.1] -52.4936 101.660 -0.516 0.616 -276.246 171.259
Omnibus: 1.962 Durbin-Watson: 0.901
Prob(Omnibus): 0.375 Jarque-Bera (JB): 1.195
Skew: -0.681 Prob(JB): 0.550
Kurtosis: 2.762 Cond. No. 1.59e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 5.009
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0262
Time: 03:44:50 Log-Likelihood: -70.748
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 214.0348 396.727 0.540 0.599 -650.359 1078.429
C(dose)[T.1] 46.2486 17.565 2.633 0.022 7.978 84.519
expression -15.8738 42.938 -0.370 0.718 -109.427 77.680
Omnibus: 2.686 Durbin-Watson: 0.886
Prob(Omnibus): 0.261 Jarque-Bera (JB): 1.696
Skew: -0.816 Prob(JB): 0.428
Kurtosis: 2.772 Cond. No. 471.

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: 03:44:50 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: 0.140
Model: OLS Adj. R-squared: 0.074
Method: Least Squares F-statistic: 2.118
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.169
Time: 03:44:50 Log-Likelihood: -74.168
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 707.6185 421.950 1.677 0.117 -203.949 1619.186
expression -67.1961 46.170 -1.455 0.169 -166.941 32.549
Omnibus: 0.439 Durbin-Watson: 1.528
Prob(Omnibus): 0.803 Jarque-Bera (JB): 0.001
Skew: 0.008 Prob(JB): 1.00
Kurtosis: 2.973 Cond. No. 414.