LID SPN TRAP

1439250_at

Slitrk3 SLIT and NTRK-like family, 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
0.014 0.908 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.606
Method: Least Squares F-statistic: 12.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000108
Time: 04:43:22 Log-Likelihood: -100.72
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -94.4902 221.438 -0.427 0.674 -557.966 368.986
C(dose)[T.1] 261.1352 276.422 0.945 0.357 -317.423 839.694
expression 16.0604 23.908 0.672 0.510 -33.979 66.100
expression:C(dose)[T.1] -22.4458 29.844 -0.752 0.461 -84.910 40.018
Omnibus: 0.389 Durbin-Watson: 2.089
Prob(Omnibus): 0.823 Jarque-Bera (JB): 0.522
Skew: 0.052 Prob(JB): 0.770
Kurtosis: 2.269 Cond. No. 808.

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: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 04:43:22 Log-Likelihood: -101.05
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 38.8767 131.183 0.296 0.770 -234.767 312.521
C(dose)[T.1] 53.3426 8.767 6.084 0.000 35.055 71.630
expression 1.6559 14.154 0.117 0.908 -27.868 31.180
Omnibus: 0.259 Durbin-Watson: 1.926
Prob(Omnibus): 0.878 Jarque-Bera (JB): 0.446
Skew: 0.043 Prob(JB): 0.800
Kurtosis: 2.323 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: 04:43:22 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.002642
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.959
Time: 04:43:22 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 68.6212 216.016 0.318 0.754 -380.608 517.850
expression 1.1987 23.322 0.051 0.959 -47.302 49.699
Omnibus: 3.326 Durbin-Watson: 2.495
Prob(Omnibus): 0.190 Jarque-Bera (JB): 1.578
Skew: 0.292 Prob(JB): 0.454
Kurtosis: 1.857 Cond. No. 280.

CP101

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

F-statistic p-value df difference
0.965 0.345 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.557
Method: Least Squares F-statistic: 6.862
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00716
Time: 04:43:22 Log-Likelihood: -67.389
No. Observations: 15 AIC: 142.8
Df Residuals: 11 BIC: 145.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.1511 202.808 0.257 0.802 -394.226 498.528
C(dose)[T.1] 991.1593 418.681 2.367 0.037 69.648 1912.671
expression 1.6500 21.879 0.075 0.941 -46.506 49.806
expression:C(dose)[T.1] -103.9243 45.956 -2.261 0.045 -205.072 -2.777
Omnibus: 0.267 Durbin-Watson: 1.128
Prob(Omnibus): 0.875 Jarque-Bera (JB): 0.221
Skew: -0.230 Prob(JB): 0.895
Kurtosis: 2.623 Cond. No. 710.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.490
Model: OLS Adj. R-squared: 0.405
Method: Least Squares F-statistic: 5.760
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0176
Time: 04:43:22 Log-Likelihood: -70.253
No. Observations: 15 AIC: 146.5
Df Residuals: 12 BIC: 148.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 270.2620 206.737 1.307 0.216 -180.180 720.704
C(dose)[T.1] 44.8520 15.775 2.843 0.015 10.482 79.222
expression -21.9064 22.296 -0.983 0.345 -70.485 26.673
Omnibus: 2.033 Durbin-Watson: 1.046
Prob(Omnibus): 0.362 Jarque-Bera (JB): 1.567
Skew: -0.668 Prob(JB): 0.457
Kurtosis: 2.149 Cond. No. 254.

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: 04:43:22 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: 0.146
Model: OLS Adj. R-squared: 0.080
Method: Least Squares F-statistic: 2.224
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.160
Time: 04:43:22 Log-Likelihood: -74.115
No. Observations: 15 AIC: 152.2
Df Residuals: 13 BIC: 153.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 456.8323 243.679 1.875 0.083 -69.603 983.268
expression -39.6758 26.602 -1.491 0.160 -97.146 17.795
Omnibus: 0.636 Durbin-Watson: 1.682
Prob(Omnibus): 0.728 Jarque-Bera (JB): 0.438
Skew: -0.377 Prob(JB): 0.803
Kurtosis: 2.638 Cond. No. 241.