LID SPN TRAP

1433987_at

Hpcal4 hippocalcin-like 4

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
0.459 0.506 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 12.80
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.31e-05
Time: 03:35:01 Log-Likelihood: -100.39
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 455.2187 376.268 1.210 0.241 -332.319 1242.757
C(dose)[T.1] -374.2738 505.540 -0.740 0.468 -1432.380 683.833
expression -37.1420 34.846 -1.066 0.300 -110.075 35.791
expression:C(dose)[T.1] 39.7192 47.790 0.831 0.416 -60.307 139.745
Omnibus: 0.256 Durbin-Watson: 2.038
Prob(Omnibus): 0.880 Jarque-Bera (JB): 0.443
Skew: 0.046 Prob(JB): 0.801
Kurtosis: 2.326 Cond. No. 1.62e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.26e-05
Time: 03:35:01 Log-Likelihood: -100.80
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 227.2297 255.543 0.889 0.384 -305.824 760.283
C(dose)[T.1] 45.7193 14.202 3.219 0.004 16.094 75.345
expression -16.0254 23.662 -0.677 0.506 -65.384 33.333
Omnibus: 0.263 Durbin-Watson: 1.977
Prob(Omnibus): 0.877 Jarque-Bera (JB): 0.448
Skew: 0.011 Prob(JB): 0.799
Kurtosis: 2.317 Cond. No. 631.

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: 03:35:01 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: 0.479
Model: OLS Adj. R-squared: 0.454
Method: Least Squares F-statistic: 19.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000252
Time: 03:35:02 Log-Likelihood: -105.60
No. Observations: 23 AIC: 215.2
Df Residuals: 21 BIC: 217.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 886.7192 183.676 4.828 0.000 504.744 1268.694
expression -76.3531 17.371 -4.395 0.000 -112.478 -40.228
Omnibus: 5.826 Durbin-Watson: 2.203
Prob(Omnibus): 0.054 Jarque-Bera (JB): 1.744
Skew: 0.096 Prob(JB): 0.418
Kurtosis: 1.665 Cond. No. 376.

CP101

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

F-statistic p-value df difference
4.819 0.049 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.609
Model: OLS Adj. R-squared: 0.502
Method: Least Squares F-statistic: 5.712
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0132
Time: 03:35:02 Log-Likelihood: -68.256
No. Observations: 15 AIC: 144.5
Df Residuals: 11 BIC: 147.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 551.0206 424.613 1.298 0.221 -383.546 1485.587
C(dose)[T.1] -68.4303 471.253 -0.145 0.887 -1105.652 968.791
expression -50.7940 44.586 -1.139 0.279 -148.928 47.340
expression:C(dose)[T.1] 12.6699 49.401 0.256 0.802 -96.060 121.400
Omnibus: 1.249 Durbin-Watson: 1.459
Prob(Omnibus): 0.536 Jarque-Bera (JB): 0.894
Skew: -0.296 Prob(JB): 0.640
Kurtosis: 1.961 Cond. No. 1.01e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.607
Model: OLS Adj. R-squared: 0.541
Method: Least Squares F-statistic: 9.256
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00370
Time: 03:35:02 Log-Likelihood: -68.301
No. Observations: 15 AIC: 142.6
Df Residuals: 12 BIC: 144.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 452.7597 175.792 2.576 0.024 69.743 835.777
C(dose)[T.1] 52.3805 13.374 3.917 0.002 23.242 81.519
expression -40.4732 18.436 -2.195 0.049 -80.642 -0.304
Omnibus: 0.924 Durbin-Watson: 1.383
Prob(Omnibus): 0.630 Jarque-Bera (JB): 0.805
Skew: -0.331 Prob(JB): 0.669
Kurtosis: 2.079 Cond. No. 257.

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: 03:35:02 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: 0.104
Model: OLS Adj. R-squared: 0.035
Method: Least Squares F-statistic: 1.508
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.241
Time: 03:35:02 Log-Likelihood: -74.477
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 405.8094 254.342 1.596 0.135 -143.664 955.283
expression -32.6420 26.579 -1.228 0.241 -90.061 24.777
Omnibus: 0.537 Durbin-Watson: 1.811
Prob(Omnibus): 0.765 Jarque-Bera (JB): 0.586
Skew: 0.204 Prob(JB): 0.746
Kurtosis: 2.122 Cond. No. 256.