LID SPN TRAP

1450212_at

Fmnl1 formin-like 1

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
1.930 0.180 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.684
Model: OLS Adj. R-squared: 0.634
Method: Least Squares F-statistic: 13.68
Date: Tue, 28 Jan 2025 Prob (F-statistic): 5.46e-05
Time: 21:46:23 Log-Likelihood: -99.873
No. Observations: 23 AIC: 207.7
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 296.3400 331.737 0.893 0.383 -397.993 990.673
C(dose)[T.1] 296.0908 512.792 0.577 0.570 -777.195 1369.376
expression -22.8874 31.352 -0.730 0.474 -88.509 42.734
expression:C(dose)[T.1] -22.4059 48.133 -0.465 0.647 -123.150 78.338
Omnibus: 0.098 Durbin-Watson: 1.950
Prob(Omnibus): 0.952 Jarque-Bera (JB): 0.319
Skew: 0.051 Prob(JB): 0.852
Kurtosis: 2.432 Cond. No. 1.62e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.648
Method: Least Squares F-statistic: 21.24
Date: Tue, 28 Jan 2025 Prob (F-statistic): 1.13e-05
Time: 21:46:23 Log-Likelihood: -100.00
No. Observations: 23 AIC: 206.0
Df Residuals: 20 BIC: 209.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 396.9090 246.761 1.608 0.123 -117.825 911.643
C(dose)[T.1] 57.4256 8.877 6.469 0.000 38.908 75.943
expression -32.3937 23.319 -1.389 0.180 -81.035 16.248
Omnibus: 0.361 Durbin-Watson: 2.025
Prob(Omnibus): 0.835 Jarque-Bera (JB): 0.507
Skew: 0.052 Prob(JB): 0.776
Kurtosis: 2.280 Cond. No. 634.

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 21:46:23 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: 0.010
Model: OLS Adj. R-squared: -0.037
Method: Least Squares F-statistic: 0.2177
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.646
Time: 21:46:23 Log-Likelihood: -112.99
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -107.7188 401.755 -0.268 0.791 -943.215 727.777
expression 17.6168 37.754 0.467 0.646 -60.898 96.131
Omnibus: 3.402 Durbin-Watson: 2.427
Prob(Omnibus): 0.183 Jarque-Bera (JB): 1.582
Skew: 0.285 Prob(JB): 0.454
Kurtosis: 1.848 Cond. No. 601.

CP101

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

F-statistic p-value df difference
0.635 0.441 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.551
Model: OLS Adj. R-squared: 0.428
Method: Least Squares F-statistic: 4.495
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0272
Time: 21:46:23 Log-Likelihood: -69.299
No. Observations: 15 AIC: 146.6
Df Residuals: 11 BIC: 149.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -268.0836 419.482 -0.639 0.536 -1191.357 655.190
C(dose)[T.1] 679.4249 472.081 1.439 0.178 -359.619 1718.469
expression 36.9946 46.238 0.800 0.441 -64.774 138.764
expression:C(dose)[T.1] -70.7096 52.444 -1.348 0.205 -186.137 44.718
Omnibus: 1.216 Durbin-Watson: 0.993
Prob(Omnibus): 0.544 Jarque-Bera (JB): 1.007
Skew: -0.451 Prob(JB): 0.604
Kurtosis: 2.108 Cond. No. 861.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.476
Model: OLS Adj. R-squared: 0.389
Method: Least Squares F-statistic: 5.461
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0206
Time: 21:46:23 Log-Likelihood: -70.446
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 230.4120 204.812 1.125 0.283 -215.834 676.658
C(dose)[T.1] 43.3051 17.027 2.543 0.026 6.206 80.404
expression -17.9711 22.549 -0.797 0.441 -67.102 31.160
Omnibus: 2.195 Durbin-Watson: 0.665
Prob(Omnibus): 0.334 Jarque-Bera (JB): 1.610
Skew: -0.646 Prob(JB): 0.447
Kurtosis: 2.048 Cond. No. 242.

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 21:46:23 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: 0.194
Model: OLS Adj. R-squared: 0.132
Method: Least Squares F-statistic: 3.135
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.100
Time: 21:46:23 Log-Likelihood: -73.680
No. Observations: 15 AIC: 151.4
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 474.9559 215.542 2.204 0.046 9.306 940.606
expression -42.8686 24.212 -1.771 0.100 -95.175 9.438
Omnibus: 0.034 Durbin-Watson: 1.312
Prob(Omnibus): 0.983 Jarque-Bera (JB): 0.196
Skew: -0.091 Prob(JB): 0.906
Kurtosis: 2.470 Cond. No. 213.