LID SPN TRAP

1458622_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
1.940 0.179 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 13.46
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.04e-05
Time: 04:32:55 Log-Likelihood: -99.998
No. Observations: 23 AIC: 208.0
Df Residuals: 19 BIC: 212.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 233.0695 342.308 0.681 0.504 -483.389 949.528
C(dose)[T.1] 72.9553 377.228 0.193 0.849 -716.592 862.503
expression -21.3492 40.852 -0.523 0.607 -106.854 64.156
expression:C(dose)[T.1] -1.1176 44.614 -0.025 0.980 -94.495 92.260
Omnibus: 1.622 Durbin-Watson: 1.505
Prob(Omnibus): 0.444 Jarque-Bera (JB): 0.974
Skew: 0.057 Prob(JB): 0.615
Kurtosis: 1.998 Cond. No. 1.16e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.648
Method: Least Squares F-statistic: 21.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.12e-05
Time: 04:32:55 Log-Likelihood: -99.998
No. Observations: 23 AIC: 206.0
Df Residuals: 20 BIC: 209.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 240.9203 134.189 1.795 0.088 -38.993 520.833
C(dose)[T.1] 63.5100 11.111 5.716 0.000 40.332 86.688
expression -22.2863 16.002 -1.393 0.179 -55.666 11.094
Omnibus: 1.666 Durbin-Watson: 1.504
Prob(Omnibus): 0.435 Jarque-Bera (JB): 0.985
Skew: 0.054 Prob(JB): 0.611
Kurtosis: 1.992 Cond. No. 281.

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: 04:32:55 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: 0.158
Model: OLS Adj. R-squared: 0.117
Method: Least Squares F-statistic: 3.926
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0608
Time: 04:32:55 Log-Likelihood: -111.13
No. Observations: 23 AIC: 226.3
Df Residuals: 21 BIC: 228.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -245.5573 164.296 -1.495 0.150 -587.230 96.115
expression 37.8394 19.097 1.981 0.061 -1.875 77.554
Omnibus: 3.422 Durbin-Watson: 2.494
Prob(Omnibus): 0.181 Jarque-Bera (JB): 2.215
Skew: 0.757 Prob(JB): 0.330
Kurtosis: 3.148 Cond. No. 216.

CP101

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

F-statistic p-value df difference
1.103 0.314 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.517
Model: OLS Adj. R-squared: 0.385
Method: Least Squares F-statistic: 3.924
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0396
Time: 04:32:55 Log-Likelihood: -69.843
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.9136 375.780 -0.093 0.928 -862.001 792.174
C(dose)[T.1] -353.4769 560.300 -0.631 0.541 -1586.688 879.734
expression 12.2871 45.096 0.272 0.790 -86.968 111.542
expression:C(dose)[T.1] 46.5872 66.178 0.704 0.496 -99.070 192.245
Omnibus: 2.395 Durbin-Watson: 0.614
Prob(Omnibus): 0.302 Jarque-Bera (JB): 1.478
Skew: -0.526 Prob(JB): 0.478
Kurtosis: 1.878 Cond. No. 819.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.495
Model: OLS Adj. R-squared: 0.411
Method: Least Squares F-statistic: 5.885
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0166
Time: 04:32:55 Log-Likelihood: -70.174
No. Observations: 15 AIC: 146.3
Df Residuals: 12 BIC: 148.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -215.0949 269.290 -0.799 0.440 -801.828 371.638
C(dose)[T.1] 40.7633 17.070 2.388 0.034 3.570 77.956
expression 33.9195 32.304 1.050 0.314 -36.464 104.303
Omnibus: 3.251 Durbin-Watson: 0.589
Prob(Omnibus): 0.197 Jarque-Bera (JB): 1.737
Skew: -0.563 Prob(JB): 0.420
Kurtosis: 1.771 Cond. No. 308.

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: 04:32:56 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: 0.255
Model: OLS Adj. R-squared: 0.198
Method: Least Squares F-statistic: 4.456
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0547
Time: 04:32:56 Log-Likelihood: -73.090
No. Observations: 15 AIC: 150.2
Df Residuals: 13 BIC: 151.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -500.4681 281.605 -1.777 0.099 -1108.838 107.902
expression 70.2135 33.263 2.111 0.055 -1.647 142.074
Omnibus: 1.516 Durbin-Watson: 1.382
Prob(Omnibus): 0.469 Jarque-Bera (JB): 0.840
Skew: -0.015 Prob(JB): 0.657
Kurtosis: 1.841 Cond. No. 276.