LID SPN TRAP

1433847_at

Fam40b family with sequence similarity 40, member B

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
0.079 0.782 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 13.01
Date: Thu, 03 Apr 2025 Prob (F-statistic): 7.50e-05
Time: 22:47:53 Log-Likelihood: -100.27
No. Observations: 23 AIC: 208.5
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -143.2958 231.231 -0.620 0.543 -627.267 340.676
C(dose)[T.1] 545.3135 436.349 1.250 0.227 -367.975 1458.602
expression 22.0773 25.839 0.854 0.404 -32.004 76.158
expression:C(dose)[T.1] -53.7187 47.415 -1.133 0.271 -152.959 45.522
Omnibus: 0.088 Durbin-Watson: 1.901
Prob(Omnibus): 0.957 Jarque-Bera (JB): 0.278
Skew: -0.106 Prob(JB): 0.870
Kurtosis: 2.505 Cond. No. 1.11e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.61
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.72e-05
Time: 22:47:53 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -0.5822 195.278 -0.003 0.998 -407.924 406.760
C(dose)[T.1] 51.1289 11.768 4.345 0.000 26.581 75.677
expression 6.1246 21.818 0.281 0.782 -39.387 51.636
Omnibus: 0.343 Durbin-Watson: 2.004
Prob(Omnibus): 0.843 Jarque-Bera (JB): 0.498
Skew: 0.070 Prob(JB): 0.780
Kurtosis: 2.293 Cond. No. 413.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:47:53 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: 0.321
Model: OLS Adj. R-squared: 0.288
Method: Least Squares F-statistic: 9.905
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00486
Time: 22:47:53 Log-Likelihood: -108.66
No. Observations: 23 AIC: 221.3
Df Residuals: 21 BIC: 223.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -553.9103 201.415 -2.750 0.012 -972.775 -135.046
expression 69.4886 22.079 3.147 0.005 23.573 115.405
Omnibus: 0.944 Durbin-Watson: 2.722
Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.917
Skew: 0.400 Prob(JB): 0.632
Kurtosis: 2.439 Cond. No. 313.

CP101

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

F-statistic p-value df difference
5.131 0.043 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.615
Model: OLS Adj. R-squared: 0.509
Method: Least Squares F-statistic: 5.847
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0122
Time: 22:47:53 Log-Likelihood: -68.149
No. Observations: 15 AIC: 144.3
Df Residuals: 11 BIC: 147.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -315.6565 366.161 -0.862 0.407 -1121.571 490.258
C(dose)[T.1] -34.2276 440.459 -0.078 0.939 -1003.671 935.216
expression 43.5382 41.599 1.047 0.318 -48.021 135.097
expression:C(dose)[T.1] 7.0062 49.325 0.142 0.890 -101.558 115.570
Omnibus: 2.390 Durbin-Watson: 0.516
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.828
Skew: -0.782 Prob(JB): 0.401
Kurtosis: 2.308 Cond. No. 860.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.614
Model: OLS Adj. R-squared: 0.550
Method: Least Squares F-statistic: 9.539
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00331
Time: 22:47:54 Log-Likelihood: -68.163
No. Observations: 15 AIC: 142.3
Df Residuals: 12 BIC: 144.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -359.5037 188.723 -1.905 0.081 -770.696 51.689
C(dose)[T.1] 28.2906 16.085 1.759 0.104 -6.755 63.336
expression 48.5215 21.421 2.265 0.043 1.850 95.193
Omnibus: 2.368 Durbin-Watson: 0.507
Prob(Omnibus): 0.306 Jarque-Bera (JB): 1.814
Skew: -0.750 Prob(JB): 0.404
Kurtosis: 2.194 Cond. No. 263.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:47:54 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: 0.514
Model: OLS Adj. R-squared: 0.477
Method: Least Squares F-statistic: 13.77
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00262
Time: 22:47:54 Log-Likelihood: -69.883
No. Observations: 15 AIC: 143.8
Df Residuals: 13 BIC: 145.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -539.5995 170.820 -3.159 0.008 -908.633 -170.566
expression 70.1399 18.904 3.710 0.003 29.301 110.979
Omnibus: 1.238 Durbin-Watson: 0.972
Prob(Omnibus): 0.539 Jarque-Bera (JB): 1.017
Skew: -0.553 Prob(JB): 0.601
Kurtosis: 2.366 Cond. No. 221.