LID SPN TRAP

1437247_at

Fosl2 fos-like antigen 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
1.991 0.174 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.765
Model: OLS Adj. R-squared: 0.728
Method: Least Squares F-statistic: 20.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.41e-06
Time: 04:13:22 Log-Likelihood: -96.454
No. Observations: 23 AIC: 200.9
Df Residuals: 19 BIC: 205.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 93.8006 252.521 0.371 0.714 -434.732 622.333
C(dose)[T.1] 1463.0419 537.256 2.723 0.013 338.553 2587.531
expression -3.5965 22.934 -0.157 0.877 -51.598 44.405
expression:C(dose)[T.1] -124.7594 47.850 -2.607 0.017 -224.910 -24.609
Omnibus: 3.872 Durbin-Watson: 2.259
Prob(Omnibus): 0.144 Jarque-Bera (JB): 2.567
Skew: 0.813 Prob(JB): 0.277
Kurtosis: 3.179 Cond. No. 1.92e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.649
Method: Least Squares F-statistic: 21.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.10e-05
Time: 04:13:22 Log-Likelihood: -99.971
No. Observations: 23 AIC: 205.9
Df Residuals: 20 BIC: 209.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 409.3032 251.726 1.626 0.120 -115.787 934.393
C(dose)[T.1] 62.4550 10.569 5.909 0.000 40.408 84.501
expression -32.2562 22.860 -1.411 0.174 -79.942 15.430
Omnibus: 2.445 Durbin-Watson: 2.101
Prob(Omnibus): 0.294 Jarque-Bera (JB): 1.230
Skew: 0.156 Prob(JB): 0.541
Kurtosis: 1.911 Cond. No. 678.

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: 04:13:22 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: 0.124
Model: OLS Adj. R-squared: 0.082
Method: Least Squares F-statistic: 2.961
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.100
Time: 04:13:22 Log-Likelihood: -111.59
No. Observations: 23 AIC: 227.2
Df Residuals: 21 BIC: 229.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -481.2225 326.071 -1.476 0.155 -1159.325 196.880
expression 50.3367 29.254 1.721 0.100 -10.501 111.174
Omnibus: 1.739 Durbin-Watson: 2.131
Prob(Omnibus): 0.419 Jarque-Bera (JB): 1.215
Skew: 0.310 Prob(JB): 0.545
Kurtosis: 2.060 Cond. No. 542.

CP101

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

F-statistic p-value df difference
0.227 0.643 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.478
Model: OLS Adj. R-squared: 0.335
Method: Least Squares F-statistic: 3.352
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0592
Time: 04:13:22 Log-Likelihood: -70.430
No. Observations: 15 AIC: 148.9
Df Residuals: 11 BIC: 151.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 392.5885 418.063 0.939 0.368 -527.562 1312.739
C(dose)[T.1] -266.7986 504.459 -0.529 0.607 -1377.105 843.508
expression -33.2044 42.675 -0.778 0.453 -127.131 60.722
expression:C(dose)[T.1] 32.2631 51.581 0.625 0.544 -81.265 145.792
Omnibus: 2.758 Durbin-Watson: 0.941
Prob(Omnibus): 0.252 Jarque-Bera (JB): 1.861
Skew: -0.846 Prob(JB): 0.394
Kurtosis: 2.661 Cond. No. 900.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.459
Model: OLS Adj. R-squared: 0.369
Method: Least Squares F-statistic: 5.090
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0251
Time: 04:13:22 Log-Likelihood: -70.693
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 176.3296 228.988 0.770 0.456 -322.591 675.251
C(dose)[T.1] 48.5745 15.648 3.104 0.009 14.481 82.668
expression -11.1207 23.355 -0.476 0.643 -62.006 39.765
Omnibus: 3.130 Durbin-Watson: 0.766
Prob(Omnibus): 0.209 Jarque-Bera (JB): 2.169
Skew: -0.913 Prob(JB): 0.338
Kurtosis: 2.628 Cond. No. 291.

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: 04:13:22 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: 0.025
Model: OLS Adj. R-squared: -0.050
Method: Least Squares F-statistic: 0.3271
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.577
Time: 04:13:22 Log-Likelihood: -75.114
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 261.3203 293.297 0.891 0.389 -372.308 894.949
expression -17.1726 30.025 -0.572 0.577 -82.037 47.691
Omnibus: 1.125 Durbin-Watson: 1.569
Prob(Omnibus): 0.570 Jarque-Bera (JB): 0.752
Skew: 0.084 Prob(JB): 0.687
Kurtosis: 1.916 Cond. No. 288.