LID SPN TRAP

1456663_x_at

Tm2d2 TM2 domain containing 2

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
4.346 0.050 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.716
Model: OLS Adj. R-squared: 0.671
Method: Least Squares F-statistic: 15.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.01e-05
Time: 03:56:02 Log-Likelihood: -98.641
No. Observations: 23 AIC: 205.3
Df Residuals: 19 BIC: 209.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -53.2705 133.360 -0.399 0.694 -332.397 225.856
C(dose)[T.1] -39.2705 168.365 -0.233 0.818 -391.662 313.121
expression 14.9006 18.472 0.807 0.430 -23.763 53.564
expression:C(dose)[T.1] 11.8863 23.020 0.516 0.612 -36.295 60.068
Omnibus: 1.411 Durbin-Watson: 2.042
Prob(Omnibus): 0.494 Jarque-Bera (JB): 0.401
Skew: -0.248 Prob(JB): 0.818
Kurtosis: 3.415 Cond. No. 431.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.712
Model: OLS Adj. R-squared: 0.683
Method: Least Squares F-statistic: 24.69
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.97e-06
Time: 03:56:02 Log-Likelihood: -98.801
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 -108.4790 78.230 -1.387 0.181 -271.663 54.705
C(dose)[T.1] 47.5514 8.419 5.648 0.000 29.989 65.113
expression 22.5546 10.819 2.085 0.050 -0.013 45.122
Omnibus: 1.033 Durbin-Watson: 2.113
Prob(Omnibus): 0.597 Jarque-Bera (JB): 0.196
Skew: -0.154 Prob(JB): 0.907
Kurtosis: 3.331 Cond. No. 148.

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: 03:56:02 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: 0.252
Model: OLS Adj. R-squared: 0.216
Method: Least Squares F-statistic: 7.070
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0147
Time: 03:56:02 Log-Likelihood: -109.77
No. Observations: 23 AIC: 223.5
Df Residuals: 21 BIC: 225.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -233.4928 117.958 -1.979 0.061 -478.799 11.813
expression 42.6966 16.057 2.659 0.015 9.303 76.090
Omnibus: 6.335 Durbin-Watson: 2.378
Prob(Omnibus): 0.042 Jarque-Bera (JB): 1.796
Skew: 0.076 Prob(JB): 0.407
Kurtosis: 1.640 Cond. No. 141.

CP101

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

F-statistic p-value df difference
2.934 0.112 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.557
Model: OLS Adj. R-squared: 0.436
Method: Least Squares F-statistic: 4.612
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0253
Time: 03:56:02 Log-Likelihood: -69.192
No. Observations: 15 AIC: 146.4
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -149.6770 199.224 -0.751 0.468 -588.165 288.811
C(dose)[T.1] 38.9096 272.551 0.143 0.889 -560.971 638.790
expression 30.1182 27.597 1.091 0.298 -30.623 90.859
expression:C(dose)[T.1] 0.8757 37.449 0.023 0.982 -81.548 83.300
Omnibus: 0.944 Durbin-Watson: 0.886
Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.851
Skew: -0.403 Prob(JB): 0.653
Kurtosis: 2.156 Cond. No. 373.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.557
Model: OLS Adj. R-squared: 0.483
Method: Least Squares F-statistic: 7.546
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00755
Time: 03:56:02 Log-Likelihood: -69.193
No. Observations: 15 AIC: 144.4
Df Residuals: 12 BIC: 146.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -153.1052 129.162 -1.185 0.259 -434.525 128.315
C(dose)[T.1] 45.2736 14.294 3.167 0.008 14.130 76.417
expression 30.5938 17.861 1.713 0.112 -8.322 69.510
Omnibus: 0.967 Durbin-Watson: 0.895
Prob(Omnibus): 0.617 Jarque-Bera (JB): 0.866
Skew: -0.410 Prob(JB): 0.649
Kurtosis: 2.156 Cond. No. 137.

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: 03:56:02 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: 0.187
Model: OLS Adj. R-squared: 0.124
Method: Least Squares F-statistic: 2.986
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.108
Time: 03:56:02 Log-Likelihood: -73.750
No. Observations: 15 AIC: 151.5
Df Residuals: 13 BIC: 152.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -194.9188 167.268 -1.165 0.265 -556.279 166.442
expression 39.6581 22.952 1.728 0.108 -9.926 89.243
Omnibus: 0.891 Durbin-Watson: 1.994
Prob(Omnibus): 0.640 Jarque-Bera (JB): 0.825
Skew: 0.414 Prob(JB): 0.662
Kurtosis: 2.202 Cond. No. 136.