LID SPN TRAP

1426106_a_at

Syt6 synaptotagmin VI

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
1.952 0.178 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.695
Model: OLS Adj. R-squared: 0.647
Method: Least Squares F-statistic: 14.43
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.89e-05
Time: 06:23:24 Log-Likelihood: -99.452
No. Observations: 23 AIC: 206.9
Df Residuals: 19 BIC: 211.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 35.5783 352.084 0.101 0.921 -701.341 772.498
C(dose)[T.1] -386.9223 444.728 -0.870 0.395 -1317.750 543.905
expression 1.9515 36.875 0.053 0.958 -75.230 79.133
expression:C(dose)[T.1] 43.6895 45.713 0.956 0.351 -51.989 139.368
Omnibus: 0.023 Durbin-Watson: 1.939
Prob(Omnibus): 0.989 Jarque-Bera (JB): 0.120
Skew: -0.049 Prob(JB): 0.942
Kurtosis: 2.660 Cond. No. 1.46e+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: 21.28
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.12e-05
Time: 06:23:24 Log-Likelihood: -99.992
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 -235.8287 207.683 -1.136 0.270 -669.048 197.390
C(dose)[T.1] 37.9127 13.855 2.736 0.013 9.011 66.814
expression 30.3811 21.746 1.397 0.178 -14.981 75.743
Omnibus: 0.013 Durbin-Watson: 1.880
Prob(Omnibus): 0.994 Jarque-Bera (JB): 0.118
Skew: -0.030 Prob(JB): 0.943
Kurtosis: 2.654 Cond. No. 493.

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: 06:23:24 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: 0.561
Model: OLS Adj. R-squared: 0.540
Method: Least Squares F-statistic: 26.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.96e-05
Time: 06:23:25 Log-Likelihood: -103.65
No. Observations: 23 AIC: 211.3
Df Residuals: 21 BIC: 213.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -681.8828 147.228 -4.631 0.000 -988.060 -375.706
expression 77.7981 15.032 5.176 0.000 46.538 109.058
Omnibus: 1.153 Durbin-Watson: 2.172
Prob(Omnibus): 0.562 Jarque-Bera (JB): 0.835
Skew: 0.061 Prob(JB): 0.659
Kurtosis: 2.075 Cond. No. 305.

CP101

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

F-statistic p-value df difference
0.022 0.884 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.629
Model: OLS Adj. R-squared: 0.527
Method: Least Squares F-statistic: 6.205
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0101
Time: 06:23:25 Log-Likelihood: -67.872
No. Observations: 15 AIC: 143.7
Df Residuals: 11 BIC: 146.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -730.7689 433.674 -1.685 0.120 -1685.280 223.742
C(dose)[T.1] 1220.4366 509.993 2.393 0.036 97.950 2342.923
expression 86.9582 47.234 1.841 0.093 -17.002 190.919
expression:C(dose)[T.1] -129.2293 56.166 -2.301 0.042 -252.851 -5.608
Omnibus: 2.524 Durbin-Watson: 1.863
Prob(Omnibus): 0.283 Jarque-Bera (JB): 0.804
Skew: -0.490 Prob(JB): 0.669
Kurtosis: 3.571 Cond. No. 1.00e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.905
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0277
Time: 06:23:25 Log-Likelihood: -70.819
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 108.1322 273.610 0.395 0.700 -488.012 704.277
C(dose)[T.1] 47.6262 18.934 2.515 0.027 6.373 88.880
expression -4.4344 29.782 -0.149 0.884 -69.323 60.454
Omnibus: 2.632 Durbin-Watson: 0.810
Prob(Omnibus): 0.268 Jarque-Bera (JB): 1.807
Skew: -0.829 Prob(JB): 0.405
Kurtosis: 2.621 Cond. No. 318.

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: 06:23:25 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: 0.160
Model: OLS Adj. R-squared: 0.095
Method: Least Squares F-statistic: 2.470
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.140
Time: 06:23:25 Log-Likelihood: -73.995
No. Observations: 15 AIC: 152.0
Df Residuals: 13 BIC: 153.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 508.6462 264.194 1.925 0.076 -62.110 1079.402
expression -46.1589 29.368 -1.572 0.140 -109.606 17.288
Omnibus: 1.319 Durbin-Watson: 1.489
Prob(Omnibus): 0.517 Jarque-Bera (JB): 0.491
Skew: -0.443 Prob(JB): 0.782
Kurtosis: 3.032 Cond. No. 258.