LID SPN TRAP

1460122_at

Tmem41b transmembrane protein 41B

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
4.321 0.051 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.712
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 15.68
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.25e-05
Time: 05:26:01 Log-Likelihood: -98.776
No. Observations: 23 AIC: 205.6
Df Residuals: 19 BIC: 210.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -10.8395 42.877 -0.253 0.803 -100.582 78.903
C(dose)[T.1] 38.1892 73.291 0.521 0.608 -115.210 191.589
expression 10.9702 7.168 1.530 0.142 -4.033 25.974
expression:C(dose)[T.1] 3.1499 12.643 0.249 0.806 -23.312 29.612
Omnibus: 1.454 Durbin-Watson: 1.856
Prob(Omnibus): 0.483 Jarque-Bera (JB): 1.173
Skew: -0.354 Prob(JB): 0.556
Kurtosis: 2.151 Cond. No. 130.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.711
Model: OLS Adj. R-squared: 0.683
Method: Least Squares F-statistic: 24.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.01e-06
Time: 05:26:01 Log-Likelihood: -98.813
No. Observations: 23 AIC: 203.6
Df Residuals: 20 BIC: 207.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -16.8438 34.621 -0.487 0.632 -89.062 55.375
C(dose)[T.1] 56.3323 8.082 6.970 0.000 39.473 73.191
expression 11.9828 5.765 2.079 0.051 -0.042 24.008
Omnibus: 1.761 Durbin-Watson: 1.830
Prob(Omnibus): 0.415 Jarque-Bera (JB): 1.289
Skew: -0.358 Prob(JB): 0.525
Kurtosis: 2.088 Cond. No. 52.7

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: 05:26:01 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: 0.2211
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.643
Time: 05:26:01 Log-Likelihood: -112.98
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 51.7149 59.986 0.862 0.398 -73.034 176.463
expression 4.8197 10.251 0.470 0.643 -16.497 26.137
Omnibus: 3.328 Durbin-Watson: 2.576
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.568
Skew: 0.285 Prob(JB): 0.457
Kurtosis: 1.855 Cond. No. 50.3

CP101

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

F-statistic p-value df difference
0.477 0.503 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.489
Model: OLS Adj. R-squared: 0.350
Method: Least Squares F-statistic: 3.511
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0528
Time: 05:26:01 Log-Likelihood: -70.262
No. Observations: 15 AIC: 148.5
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.6258 149.414 0.372 0.717 -273.233 384.485
C(dose)[T.1] -94.7417 224.819 -0.421 0.682 -589.564 400.081
expression 2.7187 34.313 0.079 0.938 -72.804 78.242
expression:C(dose)[T.1] 33.4486 51.893 0.645 0.532 -80.768 147.665
Omnibus: 5.512 Durbin-Watson: 1.009
Prob(Omnibus): 0.064 Jarque-Bera (JB): 2.886
Skew: -1.024 Prob(JB): 0.236
Kurtosis: 3.649 Cond. No. 168.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.470
Model: OLS Adj. R-squared: 0.381
Method: Least Squares F-statistic: 5.318
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0222
Time: 05:26:01 Log-Likelihood: -70.541
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -7.8642 109.578 -0.072 0.944 -246.615 230.886
C(dose)[T.1] 49.8076 15.461 3.221 0.007 16.121 83.494
expression 17.3431 25.107 0.691 0.503 -37.359 72.046
Omnibus: 4.465 Durbin-Watson: 0.931
Prob(Omnibus): 0.107 Jarque-Bera (JB): 2.553
Skew: -1.006 Prob(JB): 0.279
Kurtosis: 3.190 Cond. No. 65.4

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: 05:26:01 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1495
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.705
Time: 05:26:01 Log-Likelihood: -75.214
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 38.7058 142.512 0.272 0.790 -269.172 346.584
expression 12.7149 32.886 0.387 0.705 -58.332 83.761
Omnibus: 0.205 Durbin-Watson: 1.742
Prob(Omnibus): 0.903 Jarque-Bera (JB): 0.399
Skew: -0.036 Prob(JB): 0.819
Kurtosis: 2.204 Cond. No. 64.4