LID SPN TRAP

1460576_at

Exoc6 exocyst complex component 6

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
0.045 0.835 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.706
Model: OLS Adj. R-squared: 0.659
Method: Least Squares F-statistic: 15.19
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 04:48:29 Log-Likelihood: -99.034
No. Observations: 23 AIC: 206.1
Df Residuals: 19 BIC: 210.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 254.9124 165.365 1.542 0.140 -91.201 601.025
C(dose)[T.1] -513.1026 297.429 -1.725 0.101 -1135.628 109.423
expression -21.3774 17.603 -1.214 0.239 -58.221 15.466
expression:C(dose)[T.1] 62.0726 32.652 1.901 0.073 -6.268 130.413
Omnibus: 0.875 Durbin-Watson: 1.246
Prob(Omnibus): 0.646 Jarque-Bera (JB): 0.458
Skew: -0.344 Prob(JB): 0.795
Kurtosis: 2.928 Cond. No. 799.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.77e-05
Time: 04:48:29 Log-Likelihood: -101.04
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 85.5320 148.134 0.577 0.570 -223.470 394.534
C(dose)[T.1] 51.9979 10.807 4.812 0.000 29.456 74.540
expression -3.3363 15.765 -0.212 0.835 -36.221 29.549
Omnibus: 0.351 Durbin-Watson: 1.837
Prob(Omnibus): 0.839 Jarque-Bera (JB): 0.502
Skew: 0.062 Prob(JB): 0.778
Kurtosis: 2.287 Cond. No. 316.

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: 04:48:29 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: 0.244
Model: OLS Adj. R-squared: 0.209
Method: Least Squares F-statistic: 6.796
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0165
Time: 04:48:29 Log-Likelihood: -109.88
No. Observations: 23 AIC: 223.8
Df Residuals: 21 BIC: 226.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 518.9076 168.587 3.078 0.006 168.311 869.504
expression -47.7555 18.319 -2.607 0.016 -85.851 -9.660
Omnibus: 1.593 Durbin-Watson: 1.829
Prob(Omnibus): 0.451 Jarque-Bera (JB): 1.311
Skew: 0.551 Prob(JB): 0.519
Kurtosis: 2.606 Cond. No. 250.

CP101

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

F-statistic p-value df difference
0.004 0.951 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.500
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 3.673
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0471
Time: 04:48:29 Log-Likelihood: -70.095
No. Observations: 15 AIC: 148.2
Df Residuals: 11 BIC: 151.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 410.3026 530.287 0.774 0.455 -756.851 1577.456
C(dose)[T.1] -809.6584 807.031 -1.003 0.337 -2585.922 966.605
expression -34.2800 53.005 -0.647 0.531 -150.943 82.383
expression:C(dose)[T.1] 86.4750 81.212 1.065 0.310 -92.272 265.222
Omnibus: 2.683 Durbin-Watson: 0.836
Prob(Omnibus): 0.261 Jarque-Bera (JB): 1.527
Skew: -0.781 Prob(JB): 0.466
Kurtosis: 2.925 Cond. No. 1.34e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.888
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:48:29 Log-Likelihood: -70.830
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 41.8565 404.070 0.104 0.919 -838.537 922.250
C(dose)[T.1] 49.4943 16.425 3.013 0.011 13.706 85.282
expression 2.5567 40.382 0.063 0.951 -85.428 90.541
Omnibus: 2.815 Durbin-Watson: 0.798
Prob(Omnibus): 0.245 Jarque-Bera (JB): 1.915
Skew: -0.857 Prob(JB): 0.384
Kurtosis: 2.649 Cond. No. 518.

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: 04:48:29 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: 0.032
Model: OLS Adj. R-squared: -0.042
Method: Least Squares F-statistic: 0.4299
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.523
Time: 04:48:29 Log-Likelihood: -75.056
No. Observations: 15 AIC: 154.1
Df Residuals: 13 BIC: 155.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 414.7474 489.816 0.847 0.412 -643.435 1472.929
expression -32.3019 49.267 -0.656 0.523 -138.737 74.133
Omnibus: 1.874 Durbin-Watson: 1.569
Prob(Omnibus): 0.392 Jarque-Bera (JB): 1.005
Skew: 0.231 Prob(JB): 0.605
Kurtosis: 1.819 Cond. No. 492.