LID SPN TRAP

1460186_at

Tm9sf3 transmembrane 9 superfamily member 3

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
0.004 0.948 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.72
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.000142
Time: 09:45:33 Log-Likelihood: -101.06
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 32.5184 272.625 0.119 0.906 -538.093 603.129
C(dose)[T.1] 73.4466 431.269 0.170 0.867 -829.210 976.103
expression 2.1586 27.125 0.080 0.937 -54.615 58.932
expression:C(dose)[T.1] -2.0051 42.294 -0.047 0.963 -90.527 86.517
Omnibus: 0.271 Durbin-Watson: 1.903
Prob(Omnibus): 0.873 Jarque-Bera (JB): 0.454
Skew: 0.058 Prob(JB): 0.797
Kurtosis: 2.322 Cond. No. 1.23e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Fri, 31 Oct 2025 Prob (F-statistic): 2.83e-05
Time: 09:45:33 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 40.8056 203.925 0.200 0.843 -384.574 466.185
C(dose)[T.1] 53.0064 10.109 5.243 0.000 31.919 74.094
expression 1.3339 20.286 0.066 0.948 -40.982 43.649
Omnibus: 0.257 Durbin-Watson: 1.902
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.445
Skew: 0.051 Prob(JB): 0.801
Kurtosis: 2.326 Cond. No. 479.

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: Fri, 31 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 09:45:33 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: 0.167
Model: OLS Adj. R-squared: 0.127
Method: Least Squares F-statistic: 4.204
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0530
Time: 09:45:33 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 -471.9144 269.118 -1.754 0.094 -1031.576 87.748
expression 54.2587 26.463 2.050 0.053 -0.773 109.291
Omnibus: 3.468 Durbin-Watson: 2.489
Prob(Omnibus): 0.177 Jarque-Bera (JB): 1.396
Skew: 0.103 Prob(JB): 0.498
Kurtosis: 1.811 Cond. No. 419.

CP101

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

F-statistic p-value df difference
0.231 0.640 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.512
Model: OLS Adj. R-squared: 0.378
Method: Least Squares F-statistic: 3.840
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0419
Time: 09:45:33 Log-Likelihood: -69.926
No. Observations: 15 AIC: 147.9
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -519.5927 501.631 -1.036 0.323 -1623.675 584.489
C(dose)[T.1] 707.2229 606.540 1.166 0.268 -627.764 2042.209
expression 62.6379 53.513 1.171 0.267 -55.143 180.419
expression:C(dose)[T.1] -70.1865 64.624 -1.086 0.301 -212.423 72.050
Omnibus: 1.539 Durbin-Watson: 1.048
Prob(Omnibus): 0.463 Jarque-Bera (JB): 0.984
Skew: -0.607 Prob(JB): 0.611
Kurtosis: 2.684 Cond. No. 1.08e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.459
Model: OLS Adj. R-squared: 0.369
Method: Least Squares F-statistic: 5.094
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.0250
Time: 09:45:33 Log-Likelihood: -70.690
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -68.5731 283.486 -0.242 0.813 -686.236 549.089
C(dose)[T.1] 48.6915 15.626 3.116 0.009 14.645 82.738
expression 14.5120 30.225 0.480 0.640 -51.342 80.366
Omnibus: 3.559 Durbin-Watson: 0.773
Prob(Omnibus): 0.169 Jarque-Bera (JB): 2.237
Skew: -0.944 Prob(JB): 0.327
Kurtosis: 2.868 Cond. No. 347.

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: Fri, 31 Oct 2025 Prob (F-statistic): 0.00629
Time: 09:45:33 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: 0.022
Model: OLS Adj. R-squared: -0.054
Method: Least Squares F-statistic: 0.2863
Date: Fri, 31 Oct 2025 Prob (F-statistic): 0.602
Time: 09:45:33 Log-Likelihood: -75.137
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -102.1268 366.078 -0.279 0.785 -892.990 688.737
expression 20.8508 38.970 0.535 0.602 -63.340 105.041
Omnibus: 1.901 Durbin-Watson: 1.674
Prob(Omnibus): 0.387 Jarque-Bera (JB): 0.950
Skew: 0.124 Prob(JB): 0.622
Kurtosis: 1.793 Cond. No. 346.