LID SPN TRAP

1427225_at

Epn2 epsin 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
0.311 0.583 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.703
Model: OLS Adj. R-squared: 0.656
Method: Least Squares F-statistic: 14.96
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.07e-05
Time: 05:05:21 Log-Likelihood: -99.162
No. Observations: 23 AIC: 206.3
Df Residuals: 19 BIC: 210.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 227.9486 149.604 1.524 0.144 -85.176 541.074
C(dose)[T.1] -247.5153 173.902 -1.423 0.171 -611.496 116.466
expression -21.0735 18.133 -1.162 0.260 -59.026 16.879
expression:C(dose)[T.1] 37.7130 21.515 1.753 0.096 -7.317 82.744
Omnibus: 0.127 Durbin-Watson: 1.552
Prob(Omnibus): 0.939 Jarque-Bera (JB): 0.134
Skew: 0.126 Prob(JB): 0.935
Kurtosis: 2.724 Cond. No. 483.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.43e-05
Time: 05:05:21 Log-Likelihood: -100.89
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 7.0912 84.741 0.084 0.934 -169.675 183.857
C(dose)[T.1] 56.7967 10.689 5.314 0.000 34.500 79.093
expression 5.7150 10.253 0.557 0.583 -15.671 27.101
Omnibus: 0.884 Durbin-Watson: 1.984
Prob(Omnibus): 0.643 Jarque-Bera (JB): 0.751
Skew: 0.095 Prob(JB): 0.687
Kurtosis: 2.136 Cond. No. 158.

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: 05:05:21 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: 0.167
Model: OLS Adj. R-squared: 0.127
Method: Least Squares F-statistic: 4.197
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0532
Time: 05:05:21 Log-Likelihood: -111.01
No. Observations: 23 AIC: 226.0
Df Residuals: 21 BIC: 228.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 285.8850 100.851 2.835 0.010 76.154 495.616
expression -25.9168 12.651 -2.049 0.053 -52.225 0.392
Omnibus: 2.476 Durbin-Watson: 1.884
Prob(Omnibus): 0.290 Jarque-Bera (JB): 1.895
Skew: 0.550 Prob(JB): 0.388
Kurtosis: 2.124 Cond. No. 124.

CP101

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

F-statistic p-value df difference
0.023 0.883 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.300
Method: Least Squares F-statistic: 3.001
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0768
Time: 05:05:21 Log-Likelihood: -70.815
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 11.6471 384.962 0.030 0.976 -835.648 858.943
C(dose)[T.1] 85.8019 465.008 0.185 0.857 -937.674 1109.278
expression 6.2124 42.853 0.145 0.887 -88.105 100.530
expression:C(dose)[T.1] -3.9719 52.569 -0.076 0.941 -119.675 111.731
Omnibus: 2.204 Durbin-Watson: 0.822
Prob(Omnibus): 0.332 Jarque-Bera (JB): 1.577
Skew: -0.759 Prob(JB): 0.455
Kurtosis: 2.531 Cond. No. 720.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.905
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0277
Time: 05:05:21 Log-Likelihood: -70.819
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 35.3458 213.744 0.165 0.871 -430.362 501.054
C(dose)[T.1] 50.6986 18.632 2.721 0.019 10.103 91.294
expression 3.5731 23.770 0.150 0.883 -48.218 55.364
Omnibus: 2.201 Durbin-Watson: 0.825
Prob(Omnibus): 0.333 Jarque-Bera (JB): 1.573
Skew: -0.758 Prob(JB): 0.455
Kurtosis: 2.533 Cond. No. 243.

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: 05:05:21 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: 0.110
Model: OLS Adj. R-squared: 0.042
Method: Least Squares F-statistic: 1.612
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.226
Time: 05:05:21 Log-Likelihood: -74.423
No. Observations: 15 AIC: 152.8
Df Residuals: 13 BIC: 154.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 366.1193 214.795 1.705 0.112 -97.918 830.156
expression -31.1203 24.510 -1.270 0.226 -84.071 21.830
Omnibus: 0.439 Durbin-Watson: 1.525
Prob(Omnibus): 0.803 Jarque-Bera (JB): 0.362
Skew: -0.318 Prob(JB): 0.835
Kurtosis: 2.583 Cond. No. 199.