LID SPN TRAP

1431226_a_at

Fndc4 fibronectin type III domain containing 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
0.760 0.394 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: 12.47
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.74e-05
Time: 03:32:49 Log-Likelihood: -100.59
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 -9.5744 75.206 -0.127 0.900 -166.982 147.833
C(dose)[T.1] 89.5872 125.364 0.715 0.484 -172.803 351.978
expression 9.9715 11.719 0.851 0.405 -14.556 34.499
expression:C(dose)[T.1] -5.4814 20.095 -0.273 0.788 -47.541 36.578
Omnibus: 0.982 Durbin-Watson: 1.792
Prob(Omnibus): 0.612 Jarque-Bera (JB): 0.774
Skew: 0.045 Prob(JB): 0.679
Kurtosis: 2.106 Cond. No. 222.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.95e-05
Time: 03:32:49 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 2.3493 59.764 0.039 0.969 -122.317 127.016
C(dose)[T.1] 55.4828 8.952 6.197 0.000 36.808 74.157
expression 8.1074 9.297 0.872 0.394 -11.285 27.500
Omnibus: 1.230 Durbin-Watson: 1.752
Prob(Omnibus): 0.541 Jarque-Bera (JB): 0.872
Skew: 0.098 Prob(JB): 0.647
Kurtosis: 2.067 Cond. No. 89.9

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: 03:32:49 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: 0.013
Model: OLS Adj. R-squared: -0.034
Method: Least Squares F-statistic: 0.2687
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.610
Time: 03:32:49 Log-Likelihood: -112.96
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.1687 93.745 1.367 0.186 -66.785 323.122
expression -7.7275 14.908 -0.518 0.610 -38.730 23.275
Omnibus: 2.633 Durbin-Watson: 2.451
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.335
Skew: 0.221 Prob(JB): 0.513
Kurtosis: 1.906 Cond. No. 84.3

CP101

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

F-statistic p-value df difference
0.040 0.846 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.457
Model: OLS Adj. R-squared: 0.310
Method: Least Squares F-statistic: 3.092
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0717
Time: 03:32:50 Log-Likelihood: -70.713
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 44.4583 158.554 0.280 0.784 -304.516 393.433
C(dose)[T.1] 129.1386 214.720 0.601 0.560 -343.457 601.734
expression 3.9904 27.467 0.145 0.887 -56.463 64.444
expression:C(dose)[T.1] -13.9814 37.354 -0.374 0.715 -96.198 68.235
Omnibus: 2.481 Durbin-Watson: 0.814
Prob(Omnibus): 0.289 Jarque-Bera (JB): 1.841
Skew: -0.810 Prob(JB): 0.398
Kurtosis: 2.434 Cond. No. 211.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.921
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0275
Time: 03:32:50 Log-Likelihood: -70.808
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 87.9713 103.881 0.847 0.414 -138.366 314.309
C(dose)[T.1] 49.0038 15.744 3.113 0.009 14.701 83.306
expression -3.5687 17.936 -0.199 0.846 -42.648 35.510
Omnibus: 2.612 Durbin-Watson: 0.728
Prob(Omnibus): 0.271 Jarque-Bera (JB): 1.884
Skew: -0.833 Prob(JB): 0.390
Kurtosis: 2.512 Cond. No. 78.7

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: 03:32:50 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: 0.007
Model: OLS Adj. R-squared: -0.069
Method: Least Squares F-statistic: 0.09171
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.767
Time: 03:32:50 Log-Likelihood: -75.247
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 133.7742 132.824 1.007 0.332 -153.175 420.723
expression -7.0026 23.123 -0.303 0.767 -56.957 42.952
Omnibus: 0.676 Durbin-Watson: 1.589
Prob(Omnibus): 0.713 Jarque-Bera (JB): 0.606
Skew: 0.030 Prob(JB): 0.739
Kurtosis: 2.017 Cond. No. 77.6