LID SPN TRAP

1436493_at

Ctxn2 cortexin 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
0.009 0.926 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, 21 Nov 2024 Prob (F-statistic): 0.000139
Time: 04:57:18 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 102.8211 223.291 0.460 0.650 -364.533 570.175
C(dose)[T.1] 0.6001 260.109 0.002 0.998 -543.814 545.014
expression -5.0993 23.413 -0.218 0.830 -54.104 43.905
expression:C(dose)[T.1] 5.5680 27.884 0.200 0.844 -52.794 63.930
Omnibus: 0.245 Durbin-Watson: 1.922
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.437
Skew: 0.089 Prob(JB): 0.804
Kurtosis: 2.349 Cond. No. 763.

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): 2.82e-05
Time: 04:57:18 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 65.3969 118.434 0.552 0.587 -181.651 312.445
C(dose)[T.1] 52.4752 12.645 4.150 0.000 26.098 78.853
expression -1.1736 12.407 -0.095 0.926 -27.054 24.707
Omnibus: 0.186 Durbin-Watson: 1.900
Prob(Omnibus): 0.911 Jarque-Bera (JB): 0.395
Skew: 0.043 Prob(JB): 0.821
Kurtosis: 2.364 Cond. No. 253.

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: 04:57:18 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: 0.347
Model: OLS Adj. R-squared: 0.316
Method: Least Squares F-statistic: 11.17
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00309
Time: 04:57:18 Log-Likelihood: -108.20
No. Observations: 23 AIC: 220.4
Df Residuals: 21 BIC: 222.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 431.1442 105.322 4.094 0.001 212.114 650.174
expression -38.2732 11.453 -3.342 0.003 -62.091 -14.456
Omnibus: 0.970 Durbin-Watson: 2.119
Prob(Omnibus): 0.616 Jarque-Bera (JB): 0.780
Skew: 0.087 Prob(JB): 0.677
Kurtosis: 2.115 Cond. No. 168.

CP101

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

F-statistic p-value df difference
0.229 0.641 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.322
Method: Least Squares F-statistic: 3.217
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0653
Time: 04:57:18 Log-Likelihood: -70.576
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -23.5754 155.663 -0.151 0.882 -366.188 319.037
C(dose)[T.1] 216.1829 412.860 0.524 0.611 -692.515 1124.881
expression 11.0577 18.860 0.586 0.570 -30.453 52.568
expression:C(dose)[T.1] -19.8842 48.241 -0.412 0.688 -126.062 86.294
Omnibus: 2.906 Durbin-Watson: 1.124
Prob(Omnibus): 0.234 Jarque-Bera (JB): 2.044
Skew: -0.880 Prob(JB): 0.360
Kurtosis: 2.582 Cond. No. 520.

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: 5.093
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0250
Time: 04:57:18 Log-Likelihood: -70.691
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 1.4363 138.302 0.010 0.992 -299.897 302.770
C(dose)[T.1] 46.1613 16.831 2.743 0.018 9.490 82.833
expression 8.0186 16.748 0.479 0.641 -28.471 44.509
Omnibus: 3.479 Durbin-Watson: 1.013
Prob(Omnibus): 0.176 Jarque-Bera (JB): 2.207
Skew: -0.936 Prob(JB): 0.332
Kurtosis: 2.842 Cond. No. 153.

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: 04:57:18 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: 0.120
Model: OLS Adj. R-squared: 0.052
Method: Least Squares F-statistic: 1.774
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.206
Time: 04:57:18 Log-Likelihood: -74.341
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -119.8175 160.588 -0.746 0.469 -466.747 227.112
expression 25.3189 19.012 1.332 0.206 -15.754 66.392
Omnibus: 0.421 Durbin-Watson: 1.804
Prob(Omnibus): 0.810 Jarque-Bera (JB): 0.508
Skew: -0.038 Prob(JB): 0.776
Kurtosis: 2.102 Cond. No. 144.