LID SPN TRAP

1455044_at

Tmem44 transmembrane protein 44

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
0.557 0.464 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.605
Method: Least Squares F-statistic: 12.25
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000109
Time: 04:50:21 Log-Likelihood: -100.72
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 88.7313 49.676 1.786 0.090 -15.243 192.705
C(dose)[T.1] 35.4591 77.924 0.455 0.654 -127.637 198.555
expression -6.0964 8.705 -0.700 0.492 -24.317 12.124
expression:C(dose)[T.1] 2.8433 14.556 0.195 0.847 -27.623 33.310
Omnibus: 0.059 Durbin-Watson: 1.723
Prob(Omnibus): 0.971 Jarque-Bera (JB): 0.279
Skew: 0.038 Prob(JB): 0.870
Kurtosis: 2.466 Cond. No. 121.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 19.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.15e-05
Time: 04:50:21 Log-Likelihood: -100.75
No. Observations: 23 AIC: 207.5
Df Residuals: 20 BIC: 210.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 82.9725 39.009 2.127 0.046 1.601 164.344
C(dose)[T.1] 50.5632 9.415 5.370 0.000 30.923 70.203
expression -5.0794 6.807 -0.746 0.464 -19.279 9.120
Omnibus: 0.069 Durbin-Watson: 1.776
Prob(Omnibus): 0.966 Jarque-Bera (JB): 0.291
Skew: 0.038 Prob(JB): 0.865
Kurtosis: 2.455 Cond. No. 51.2

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: 04:50:21 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: 0.166
Model: OLS Adj. R-squared: 0.126
Method: Least Squares F-statistic: 4.186
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0535
Time: 04:50:21 Log-Likelihood: -111.01
No. Observations: 23 AIC: 226.0
Df Residuals: 21 BIC: 228.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 185.1206 51.940 3.564 0.002 77.106 293.135
expression -19.5130 9.538 -2.046 0.053 -39.348 0.322
Omnibus: 1.606 Durbin-Watson: 2.184
Prob(Omnibus): 0.448 Jarque-Bera (JB): 1.105
Skew: 0.254 Prob(JB): 0.575
Kurtosis: 2.054 Cond. No. 44.3

CP101

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

F-statistic p-value df difference
0.535 0.479 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.631
Model: OLS Adj. R-squared: 0.530
Method: Least Squares F-statistic: 6.257
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00979
Time: 04:50:21 Log-Likelihood: -67.833
No. Observations: 15 AIC: 143.7
Df Residuals: 11 BIC: 146.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 130.8169 98.913 1.323 0.213 -86.889 348.523
C(dose)[T.1] -279.5312 154.791 -1.806 0.098 -620.224 61.162
expression -11.8148 18.345 -0.644 0.533 -52.192 28.562
expression:C(dose)[T.1] 65.9090 30.368 2.170 0.053 -0.931 132.749
Omnibus: 1.246 Durbin-Watson: 0.779
Prob(Omnibus): 0.536 Jarque-Bera (JB): 1.054
Skew: -0.515 Prob(JB): 0.590
Kurtosis: 2.209 Cond. No. 154.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.384
Method: Least Squares F-statistic: 5.370
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0216
Time: 04:50:21 Log-Likelihood: -70.506
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1.7788 90.448 0.020 0.985 -195.291 198.848
C(dose)[T.1] 54.8255 17.216 3.185 0.008 17.316 92.335
expression 12.2363 16.728 0.732 0.479 -24.210 48.683
Omnibus: 2.387 Durbin-Watson: 0.763
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.742
Skew: -0.794 Prob(JB): 0.419
Kurtosis: 2.484 Cond. No. 63.3

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: 04:50:21 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: 0.026
Model: OLS Adj. R-squared: -0.049
Method: Least Squares F-statistic: 0.3514
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.564
Time: 04:50:21 Log-Likelihood: -75.100
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 152.9314 100.483 1.522 0.152 -64.150 370.013
expression -11.5756 19.528 -0.593 0.564 -53.764 30.613
Omnibus: 1.567 Durbin-Watson: 1.577
Prob(Omnibus): 0.457 Jarque-Bera (JB): 0.864
Skew: 0.089 Prob(JB): 0.649
Kurtosis: 1.838 Cond. No. 53.5