LID SPN TRAP

1437560_at

Ntrk2 neurotrophic tyrosine kinase, receptor, type 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
1.124 0.302 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 12.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.33e-05
Time: 05:24:55 Log-Likelihood: -100.39
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 245.3765 358.298 0.685 0.502 -504.549 995.302
C(dose)[T.1] 196.2497 509.388 0.385 0.704 -869.912 1262.412
expression -20.4162 38.260 -0.534 0.600 -100.495 59.662
expression:C(dose)[T.1] -13.5150 53.064 -0.255 0.802 -124.579 97.549
Omnibus: 0.087 Durbin-Watson: 1.618
Prob(Omnibus): 0.958 Jarque-Bera (JB): 0.312
Skew: -0.011 Prob(JB): 0.856
Kurtosis: 2.430 Cond. No. 1.48e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.635
Method: Least Squares F-statistic: 20.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.64e-05
Time: 05:24:55 Log-Likelihood: -100.43
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 311.1640 242.436 1.283 0.214 -194.548 816.876
C(dose)[T.1] 66.5719 15.121 4.403 0.000 35.030 98.114
expression -27.4421 25.884 -1.060 0.302 -81.435 26.550
Omnibus: 0.159 Durbin-Watson: 1.620
Prob(Omnibus): 0.924 Jarque-Bera (JB): 0.375
Skew: 0.026 Prob(JB): 0.829
Kurtosis: 2.376 Cond. No. 553.

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: 05:24:55 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: 0.346
Model: OLS Adj. R-squared: 0.315
Method: Least Squares F-statistic: 11.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00317
Time: 05:24:55 Log-Likelihood: -108.23
No. Observations: 23 AIC: 220.5
Df Residuals: 21 BIC: 222.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -559.5773 192.006 -2.914 0.008 -958.876 -160.278
expression 66.6334 20.003 3.331 0.003 25.034 108.233
Omnibus: 1.870 Durbin-Watson: 2.553
Prob(Omnibus): 0.393 Jarque-Bera (JB): 0.781
Skew: 0.417 Prob(JB): 0.677
Kurtosis: 3.347 Cond. No. 319.

CP101

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

F-statistic p-value df difference
0.554 0.471 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.519
Model: OLS Adj. R-squared: 0.388
Method: Least Squares F-statistic: 3.956
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0388
Time: 05:24:55 Log-Likelihood: -69.811
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -423.3199 387.975 -1.091 0.299 -1277.248 430.608
C(dose)[T.1] 562.5230 506.224 1.111 0.290 -551.668 1676.714
expression 54.5320 43.094 1.265 0.232 -40.317 149.381
expression:C(dose)[T.1] -56.9746 55.618 -1.024 0.328 -179.389 65.440
Omnibus: 2.547 Durbin-Watson: 0.654
Prob(Omnibus): 0.280 Jarque-Bera (JB): 1.534
Skew: -0.779 Prob(JB): 0.464
Kurtosis: 2.830 Cond. No. 849.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.473
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 5.388
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0214
Time: 05:24:55 Log-Likelihood: -70.494
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -115.5068 245.931 -0.470 0.647 -651.344 420.330
C(dose)[T.1] 44.2356 16.768 2.638 0.022 7.700 80.771
expression 20.3278 27.299 0.745 0.471 -39.152 79.808
Omnibus: 2.464 Durbin-Watson: 0.704
Prob(Omnibus): 0.292 Jarque-Bera (JB): 1.745
Skew: -0.805 Prob(JB): 0.418
Kurtosis: 2.553 Cond. No. 297.

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: 05:24:55 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: 0.168
Model: OLS Adj. R-squared: 0.104
Method: Least Squares F-statistic: 2.617
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.130
Time: 05:24:55 Log-Likelihood: -73.925
No. Observations: 15 AIC: 151.8
Df Residuals: 13 BIC: 153.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -353.1286 276.359 -1.278 0.224 -950.166 243.909
expression 48.9401 30.254 1.618 0.130 -16.420 114.300
Omnibus: 2.121 Durbin-Watson: 1.288
Prob(Omnibus): 0.346 Jarque-Bera (JB): 1.262
Skew: 0.425 Prob(JB): 0.532
Kurtosis: 1.861 Cond. No. 276.