LID SPN TRAP

1439872_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
0.023 0.880 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.609
Method: Least Squares F-statistic: 12.42
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.98e-05
Time: 03:47:14 Log-Likelihood: -100.62
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.7328 159.575 1.057 0.304 -165.262 502.728
C(dose)[T.1] -131.3281 216.069 -0.608 0.551 -583.565 320.909
expression -14.9052 20.753 -0.718 0.481 -58.342 28.532
expression:C(dose)[T.1] 24.5560 28.839 0.851 0.405 -35.805 84.917
Omnibus: 0.078 Durbin-Watson: 1.699
Prob(Omnibus): 0.962 Jarque-Bera (JB): 0.144
Skew: -0.106 Prob(JB): 0.931
Kurtosis: 2.677 Cond. No. 489.

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: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 03:47:14 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 71.0244 110.125 0.645 0.526 -158.693 300.741
C(dose)[T.1] 52.4273 10.593 4.949 0.000 30.331 74.524
expression -2.1886 14.311 -0.153 0.880 -32.041 27.663
Omnibus: 0.438 Durbin-Watson: 1.862
Prob(Omnibus): 0.803 Jarque-Bera (JB): 0.552
Skew: 0.083 Prob(JB): 0.759
Kurtosis: 2.259 Cond. No. 192.

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: 03:47:15 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: 0.220
Model: OLS Adj. R-squared: 0.183
Method: Least Squares F-statistic: 5.928
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0239
Time: 03:47:15 Log-Likelihood: -110.25
No. Observations: 23 AIC: 224.5
Df Residuals: 21 BIC: 226.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 393.8195 129.163 3.049 0.006 125.211 662.428
expression -41.9657 17.236 -2.435 0.024 -77.810 -6.122
Omnibus: 2.020 Durbin-Watson: 2.289
Prob(Omnibus): 0.364 Jarque-Bera (JB): 1.694
Skew: 0.624 Prob(JB): 0.429
Kurtosis: 2.540 Cond. No. 155.

CP101

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

F-statistic p-value df difference
0.008 0.931 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.560
Method: Least Squares F-statistic: 6.936
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00690
Time: 03:47:15 Log-Likelihood: -67.336
No. Observations: 15 AIC: 142.7
Df Residuals: 11 BIC: 145.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -426.0752 313.303 -1.360 0.201 -1115.651 263.500
C(dose)[T.1] 1282.7236 483.180 2.655 0.022 219.252 2346.195
expression 55.0210 34.914 1.576 0.143 -21.825 131.867
expression:C(dose)[T.1] -137.3610 53.788 -2.554 0.027 -255.747 -18.975
Omnibus: 0.496 Durbin-Watson: 0.922
Prob(Omnibus): 0.780 Jarque-Bera (JB): 0.570
Skew: 0.219 Prob(JB): 0.752
Kurtosis: 2.151 Cond. No. 868.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.892
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 03:47:15 Log-Likelihood: -70.828
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.0396 288.083 0.323 0.752 -534.639 720.718
C(dose)[T.1] 49.2479 15.745 3.128 0.009 14.942 83.554
expression -2.8554 32.093 -0.089 0.931 -72.780 67.069
Omnibus: 2.677 Durbin-Watson: 0.806
Prob(Omnibus): 0.262 Jarque-Bera (JB): 1.879
Skew: -0.841 Prob(JB): 0.391
Kurtosis: 2.577 Cond. No. 334.

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: 03:47:15 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: 0.0004053
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.984
Time: 03:47:15 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 86.1619 372.902 0.231 0.821 -719.444 891.768
expression 0.8358 41.515 0.020 0.984 -88.852 90.524
Omnibus: 0.626 Durbin-Watson: 1.619
Prob(Omnibus): 0.731 Jarque-Bera (JB): 0.590
Skew: 0.055 Prob(JB): 0.744
Kurtosis: 2.034 Cond. No. 334.