LID SPN TRAP

1454056_at

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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
0.016 0.900 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 12.49
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.67e-05
Time: 05:08:10 Log-Likelihood: -100.58
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 23.0637 58.561 0.394 0.698 -99.505 145.632
C(dose)[T.1] 123.9204 80.086 1.547 0.138 -43.701 291.541
expression 5.0327 9.411 0.535 0.599 -14.666 24.731
expression:C(dose)[T.1] -12.0251 13.458 -0.894 0.383 -40.193 16.143
Omnibus: 0.291 Durbin-Watson: 2.079
Prob(Omnibus): 0.864 Jarque-Bera (JB): 0.466
Skew: -0.041 Prob(JB): 0.792
Kurtosis: 2.308 Cond. No. 144.

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: 18.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 05:08:10 Log-Likelihood: -101.05
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 59.4578 41.863 1.420 0.171 -27.867 146.783
C(dose)[T.1] 52.8720 9.504 5.563 0.000 33.048 72.696
expression -0.8483 6.693 -0.127 0.900 -14.810 13.114
Omnibus: 0.260 Durbin-Watson: 1.850
Prob(Omnibus): 0.878 Jarque-Bera (JB): 0.447
Skew: 0.050 Prob(JB): 0.800
Kurtosis: 2.325 Cond. No. 59.1

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: 05:08:10 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: 0.107
Model: OLS Adj. R-squared: 0.064
Method: Least Squares F-statistic: 2.507
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.128
Time: 05:08:10 Log-Likelihood: -111.81
No. Observations: 23 AIC: 227.6
Df Residuals: 21 BIC: 229.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 169.9642 57.400 2.961 0.007 50.594 289.335
expression -15.2283 9.617 -1.583 0.128 -35.228 4.772
Omnibus: 0.945 Durbin-Watson: 2.093
Prob(Omnibus): 0.623 Jarque-Bera (JB): 0.757
Skew: 0.002 Prob(JB): 0.685
Kurtosis: 2.111 Cond. No. 51.6

CP101

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

F-statistic p-value df difference
0.979 0.342 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: 0.374
Method: Least Squares F-statistic: 3.792
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0434
Time: 05:08:10 Log-Likelihood: -69.975
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 212.6920 126.714 1.679 0.121 -66.203 491.587
C(dose)[T.1] -80.4613 207.478 -0.388 0.706 -537.118 376.195
expression -24.1182 20.954 -1.151 0.274 -70.238 22.001
expression:C(dose)[T.1] 21.5738 33.965 0.635 0.538 -53.182 96.330
Omnibus: 3.659 Durbin-Watson: 1.202
Prob(Omnibus): 0.160 Jarque-Bera (JB): 2.238
Skew: -0.946 Prob(JB): 0.327
Kurtosis: 2.933 Cond. No. 212.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 5.773
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0175
Time: 05:08:10 Log-Likelihood: -70.245
No. Observations: 15 AIC: 146.5
Df Residuals: 12 BIC: 148.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 163.2369 97.454 1.675 0.120 -49.098 375.571
C(dose)[T.1] 50.9499 15.238 3.344 0.006 17.750 84.150
expression -15.9072 16.076 -0.989 0.342 -50.934 19.120
Omnibus: 5.209 Durbin-Watson: 1.002
Prob(Omnibus): 0.074 Jarque-Bera (JB): 3.058
Skew: -1.098 Prob(JB): 0.217
Kurtosis: 3.264 Cond. No. 81.2

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: 05:08:10 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: 0.016
Model: OLS Adj. R-squared: -0.060
Method: Least Squares F-statistic: 0.2051
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.658
Time: 05:08:10 Log-Likelihood: -75.183
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 152.3913 130.061 1.172 0.262 -128.588 433.370
expression -9.6559 21.321 -0.453 0.658 -55.717 36.405
Omnibus: 1.053 Durbin-Watson: 1.843
Prob(Omnibus): 0.591 Jarque-Bera (JB): 0.735
Skew: 0.100 Prob(JB): 0.692
Kurtosis: 1.934 Cond. No. 80.8