LID SPN TRAP

1460230_at

Syn2 synapsin II

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
1.446 0.243 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.677
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 13.28
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.58e-05
Time: 05:03:16 Log-Likelihood: -100.10
No. Observations: 23 AIC: 208.2
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -255.7916 252.022 -1.015 0.323 -783.279 271.696
C(dose)[T.1] 250.7065 392.352 0.639 0.530 -570.496 1071.910
expression 32.9739 26.799 1.230 0.234 -23.118 89.066
expression:C(dose)[T.1] -21.1684 41.367 -0.512 0.615 -107.751 65.414
Omnibus: 0.515 Durbin-Watson: 2.107
Prob(Omnibus): 0.773 Jarque-Bera (JB): 0.583
Skew: 0.012 Prob(JB): 0.747
Kurtosis: 2.220 Cond. No. 1.09e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.673
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 20.55
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.41e-05
Time: 05:03:16 Log-Likelihood: -100.26
No. Observations: 23 AIC: 206.5
Df Residuals: 20 BIC: 209.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -172.2659 188.446 -0.914 0.372 -565.356 220.825
C(dose)[T.1] 49.9854 8.916 5.606 0.000 31.387 68.584
expression 24.0895 20.035 1.202 0.243 -17.702 65.881
Omnibus: 0.400 Durbin-Watson: 2.148
Prob(Omnibus): 0.819 Jarque-Bera (JB): 0.528
Skew: 0.052 Prob(JB): 0.768
Kurtosis: 2.265 Cond. No. 427.

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: 05:03:16 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: 0.118
Method: Least Squares F-statistic: 3.952
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0600
Time: 05:03:16 Log-Likelihood: -111.12
No. Observations: 23 AIC: 226.2
Df Residuals: 21 BIC: 228.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -480.8405 282.046 -1.705 0.103 -1067.387 105.706
expression 59.2061 29.781 1.988 0.060 -2.728 121.140
Omnibus: 1.929 Durbin-Watson: 2.608
Prob(Omnibus): 0.381 Jarque-Bera (JB): 1.331
Skew: 0.350 Prob(JB): 0.514
Kurtosis: 2.052 Cond. No. 408.

CP101

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

F-statistic p-value df difference
0.535 0.478 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.497
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 3.621
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0488
Time: 05:03:16 Log-Likelihood: -70.149
No. Observations: 15 AIC: 148.3
Df Residuals: 11 BIC: 151.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -196.3245 546.744 -0.359 0.726 -1399.699 1007.050
C(dose)[T.1] 470.7370 573.998 0.820 0.430 -792.625 1734.099
expression 30.9771 64.199 0.483 0.639 -110.325 172.279
expression:C(dose)[T.1] -49.2986 67.319 -0.732 0.479 -197.467 98.870
Omnibus: 2.616 Durbin-Watson: 1.085
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.436
Skew: -0.758 Prob(JB): 0.488
Kurtosis: 2.965 Cond. No. 1.01e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.472
Model: OLS Adj. R-squared: 0.384
Method: Least Squares F-statistic: 5.370
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0216
Time: 05:03:16 Log-Likelihood: -70.506
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 185.4249 161.654 1.147 0.274 -166.789 537.639
C(dose)[T.1] 50.5505 15.511 3.259 0.007 16.756 84.345
expression -13.8584 18.940 -0.732 0.478 -55.125 27.408
Omnibus: 2.101 Durbin-Watson: 0.851
Prob(Omnibus): 0.350 Jarque-Bera (JB): 1.327
Skew: -0.716 Prob(JB): 0.515
Kurtosis: 2.727 Cond. No. 183.

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: 05:03:16 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: 0.005
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.06853
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.798
Time: 05:03:16 Log-Likelihood: -75.261
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 149.2956 212.742 0.702 0.495 -310.305 608.896
expression -6.4937 24.806 -0.262 0.798 -60.083 47.096
Omnibus: 0.391 Durbin-Watson: 1.637
Prob(Omnibus): 0.822 Jarque-Bera (JB): 0.494
Skew: 0.029 Prob(JB): 0.781
Kurtosis: 2.113 Cond. No. 182.