LID SPN TRAP

1448882_at

Tmem93 transmembrane protein 93

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
0.610 0.444 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 13.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.47e-05
Time: 04:01:22 Log-Likelihood: -100.08
No. Observations: 23 AIC: 208.2
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 59.3741 198.648 0.299 0.768 -356.400 475.148
C(dose)[T.1] 415.0499 345.073 1.203 0.244 -307.196 1137.296
expression -0.6219 23.906 -0.026 0.980 -50.657 49.413
expression:C(dose)[T.1] -42.4144 40.822 -1.039 0.312 -127.857 43.028
Omnibus: 0.263 Durbin-Watson: 1.986
Prob(Omnibus): 0.877 Jarque-Bera (JB): 0.124
Skew: 0.159 Prob(JB): 0.940
Kurtosis: 2.832 Cond. No. 831.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 19.36
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.10e-05
Time: 04:01:22 Log-Likelihood: -100.72
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 180.1852 161.381 1.117 0.277 -156.449 516.820
C(dose)[T.1] 56.6584 9.629 5.884 0.000 36.573 76.743
expression -15.1672 19.416 -0.781 0.444 -55.669 25.335
Omnibus: 0.411 Durbin-Watson: 1.773
Prob(Omnibus): 0.814 Jarque-Bera (JB): 0.266
Skew: 0.242 Prob(JB): 0.876
Kurtosis: 2.793 Cond. No. 320.

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: 04:01:22 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: 0.070
Model: OLS Adj. R-squared: 0.026
Method: Least Squares F-statistic: 1.577
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.223
Time: 04:01:22 Log-Likelihood: -112.27
No. Observations: 23 AIC: 228.5
Df Residuals: 21 BIC: 230.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -217.0418 236.415 -0.918 0.369 -708.694 274.610
expression 35.2839 28.097 1.256 0.223 -23.147 93.715
Omnibus: 1.089 Durbin-Watson: 2.577
Prob(Omnibus): 0.580 Jarque-Bera (JB): 1.019
Skew: 0.367 Prob(JB): 0.601
Kurtosis: 2.276 Cond. No. 290.

CP101

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

F-statistic p-value df difference
0.012 0.913 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.305
Method: Least Squares F-statistic: 3.052
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0739
Time: 04:01:22 Log-Likelihood: -70.758
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -12.9392 275.657 -0.047 0.963 -619.657 593.778
C(dose)[T.1] 178.7287 416.413 0.429 0.676 -737.790 1095.248
expression 10.3600 35.501 0.292 0.776 -67.777 88.497
expression:C(dose)[T.1] -16.3925 52.202 -0.314 0.759 -131.288 98.503
Omnibus: 3.219 Durbin-Watson: 0.735
Prob(Omnibus): 0.200 Jarque-Bera (JB): 2.098
Skew: -0.908 Prob(JB): 0.350
Kurtosis: 2.763 Cond. No. 540.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.896
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 04:01:22 Log-Likelihood: -70.825
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 45.8739 194.516 0.236 0.818 -377.940 469.688
C(dose)[T.1] 48.1062 18.546 2.594 0.023 7.698 88.514
expression 2.7785 25.031 0.111 0.913 -51.759 57.316
Omnibus: 2.426 Durbin-Watson: 0.798
Prob(Omnibus): 0.297 Jarque-Bera (JB): 1.737
Skew: -0.800 Prob(JB): 0.419
Kurtosis: 2.528 Cond. No. 202.

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: 04:01:23 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: 0.141
Model: OLS Adj. R-squared: 0.074
Method: Least Squares F-statistic: 2.127
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.168
Time: 04:01:23 Log-Likelihood: -74.164
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -202.4087 203.248 -0.996 0.337 -641.499 236.681
expression 37.1636 25.484 1.458 0.168 -17.892 92.219
Omnibus: 1.058 Durbin-Watson: 1.099
Prob(Omnibus): 0.589 Jarque-Bera (JB): 0.867
Skew: 0.346 Prob(JB): 0.648
Kurtosis: 2.047 Cond. No. 175.