LID SPN TRAP

1435472_at

Kremen1 kringle containing transmembrane protein 1

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
0.056 0.815 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.76
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000139
Time: 22:47:54 Log-Likelihood: -101.03
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 75.3222 132.006 0.571 0.575 -200.969 351.613
C(dose)[T.1] 55.6964 192.961 0.289 0.776 -348.176 459.569
expression -3.2008 19.989 -0.160 0.874 -45.039 38.637
expression:C(dose)[T.1] -0.3359 29.125 -0.012 0.991 -61.296 60.624
Omnibus: 0.310 Durbin-Watson: 1.846
Prob(Omnibus): 0.856 Jarque-Bera (JB): 0.480
Skew: 0.091 Prob(JB): 0.787
Kurtosis: 2.316 Cond. No. 373.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.76e-05
Time: 22:47:54 Log-Likelihood: -101.03
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 76.3659 93.670 0.815 0.425 -119.026 271.758
C(dose)[T.1] 53.4733 8.776 6.093 0.000 35.166 71.780
expression -3.3590 14.170 -0.237 0.815 -32.918 26.200
Omnibus: 0.309 Durbin-Watson: 1.844
Prob(Omnibus): 0.857 Jarque-Bera (JB): 0.479
Skew: 0.090 Prob(JB): 0.787
Kurtosis: 2.317 Cond. No. 145.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:47:54 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.009663
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.923
Time: 22:47:54 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 64.5511 154.455 0.418 0.680 -256.656 385.758
expression 2.2924 23.321 0.098 0.923 -46.205 50.790
Omnibus: 3.271 Durbin-Watson: 2.511
Prob(Omnibus): 0.195 Jarque-Bera (JB): 1.556
Skew: 0.285 Prob(JB): 0.459
Kurtosis: 1.860 Cond. No. 145.

CP101

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

F-statistic p-value df difference
2.394 0.148 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.554
Model: OLS Adj. R-squared: 0.433
Method: Least Squares F-statistic: 4.559
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0261
Time: 22:47:55 Log-Likelihood: -69.241
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -16.2218 265.769 -0.061 0.952 -601.176 568.732
C(dose)[T.1] -141.1154 317.372 -0.445 0.665 -839.646 557.415
expression 10.6195 33.712 0.315 0.759 -63.580 84.819
expression:C(dose)[T.1] 23.2671 39.941 0.583 0.572 -64.643 111.177
Omnibus: 2.373 Durbin-Watson: 0.755
Prob(Omnibus): 0.305 Jarque-Bera (JB): 1.731
Skew: -0.791 Prob(JB): 0.421
Kurtosis: 2.485 Cond. No. 511.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.540
Model: OLS Adj. R-squared: 0.464
Method: Least Squares F-statistic: 7.057
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00942
Time: 22:47:55 Log-Likelihood: -69.468
No. Observations: 15 AIC: 144.9
Df Residuals: 12 BIC: 147.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -146.7864 138.834 -1.057 0.311 -449.279 155.706
C(dose)[T.1] 43.5503 14.827 2.937 0.012 11.245 75.856
expression 27.1948 17.575 1.547 0.148 -11.097 65.487
Omnibus: 2.183 Durbin-Watson: 0.608
Prob(Omnibus): 0.336 Jarque-Bera (JB): 1.606
Skew: -0.646 Prob(JB): 0.448
Kurtosis: 2.052 Cond. No. 158.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:47:55 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: 0.210
Model: OLS Adj. R-squared: 0.149
Method: Least Squares F-statistic: 3.458
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0857
Time: 22:47:55 Log-Likelihood: -73.531
No. Observations: 15 AIC: 151.1
Df Residuals: 13 BIC: 152.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -225.0310 171.632 -1.311 0.213 -595.820 145.758
expression 39.8981 21.457 1.859 0.086 -6.457 86.253
Omnibus: 0.576 Durbin-Watson: 1.323
Prob(Omnibus): 0.750 Jarque-Bera (JB): 0.586
Skew: -0.136 Prob(JB): 0.746
Kurtosis: 2.071 Cond. No. 154.