LID SPN TRAP

1420514_at

Tmem47 transmembrane protein 47

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
0.407 0.531 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.602
Method: Least Squares F-statistic: 12.10
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000118
Time: 18:34:34 Log-Likelihood: -100.82
No. Observations: 23 AIC: 209.6
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 151.4334 165.403 0.916 0.371 -194.760 497.627
C(dose)[T.1] 17.2002 304.409 0.057 0.956 -619.936 654.336
expression -11.5201 19.585 -0.588 0.563 -52.512 29.472
expression:C(dose)[T.1] 4.4861 35.340 0.127 0.900 -69.480 78.453
Omnibus: 1.235 Durbin-Watson: 1.708
Prob(Omnibus): 0.539 Jarque-Bera (JB): 0.854
Skew: -0.008 Prob(JB): 0.653
Kurtosis: 2.056 Cond. No. 713.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.07
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.32e-05
Time: 18:34:34 Log-Likelihood: -100.83
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 139.8053 134.292 1.041 0.310 -140.323 419.933
C(dose)[T.1] 55.8229 9.516 5.866 0.000 35.973 75.673
expression -10.1423 15.896 -0.638 0.531 -43.301 23.017
Omnibus: 1.301 Durbin-Watson: 1.714
Prob(Omnibus): 0.522 Jarque-Bera (JB): 0.874
Skew: -0.005 Prob(JB): 0.646
Kurtosis: 2.045 Cond. No. 269.

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: 18:34:34 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: 0.064
Model: OLS Adj. R-squared: 0.020
Method: Least Squares F-statistic: 1.442
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.243
Time: 18:34:34 Log-Likelihood: -112.34
No. Observations: 23 AIC: 228.7
Df Residuals: 21 BIC: 231.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -160.1824 199.880 -0.801 0.432 -575.855 255.490
expression 28.0361 23.345 1.201 0.243 -20.512 76.584
Omnibus: 1.545 Durbin-Watson: 2.486
Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.136
Skew: 0.298 Prob(JB): 0.567
Kurtosis: 2.088 Cond. No. 248.

CP101

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

F-statistic p-value df difference
0.049 0.829 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.303
Method: Least Squares F-statistic: 3.027
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0753
Time: 18:34:34 Log-Likelihood: -70.786
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -26.0038 376.107 -0.069 0.946 -853.811 801.803
C(dose)[T.1] 119.9245 461.358 0.260 0.800 -895.518 1135.367
expression 11.6466 46.859 0.249 0.808 -91.489 114.783
expression:C(dose)[T.1] -8.8787 57.053 -0.156 0.879 -134.450 116.693
Omnibus: 2.660 Durbin-Watson: 0.738
Prob(Omnibus): 0.264 Jarque-Bera (JB): 1.861
Skew: -0.837 Prob(JB): 0.394
Kurtosis: 2.584 Cond. No. 669.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.929
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0274
Time: 18:34:34 Log-Likelihood: -70.803
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 22.0451 205.859 0.107 0.916 -426.483 470.573
C(dose)[T.1] 48.1759 16.374 2.942 0.012 12.501 83.851
expression 5.6572 25.621 0.221 0.829 -50.166 61.480
Omnibus: 2.812 Durbin-Watson: 0.735
Prob(Omnibus): 0.245 Jarque-Bera (JB): 1.976
Skew: -0.864 Prob(JB): 0.372
Kurtosis: 2.579 Cond. No. 217.

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: 18:34:34 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: 0.055
Model: OLS Adj. R-squared: -0.018
Method: Least Squares F-statistic: 0.7558
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.400
Time: 18:34:34 Log-Likelihood: -74.876
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -125.0130 251.733 -0.497 0.628 -668.848 418.822
expression 26.9359 30.983 0.869 0.400 -40.000 93.871
Omnibus: 1.100 Durbin-Watson: 1.485
Prob(Omnibus): 0.577 Jarque-Bera (JB): 0.745
Skew: 0.085 Prob(JB): 0.689
Kurtosis: 1.922 Cond. No. 210.