LID SPN TRAP

1460554_s_at

Glg1 golgi apparatus protein 1

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
3.176 0.090 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.649
Method: Least Squares F-statistic: 14.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.63e-05
Time: 04:44:18 Log-Likelihood: -99.368
No. Observations: 23 AIC: 206.7
Df Residuals: 19 BIC: 211.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 238.4289 201.103 1.186 0.250 -182.484 659.342
C(dose)[T.1] 54.0378 237.029 0.228 0.822 -442.070 550.146
expression -25.6357 27.973 -0.916 0.371 -84.185 32.913
expression:C(dose)[T.1] -0.2261 33.010 -0.007 0.995 -69.317 68.865
Omnibus: 3.180 Durbin-Watson: 1.619
Prob(Omnibus): 0.204 Jarque-Bera (JB): 1.558
Skew: 0.298 Prob(JB): 0.459
Kurtosis: 1.872 Cond. No. 593.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.667
Method: Least Squares F-statistic: 23.02
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.49e-06
Time: 04:44:18 Log-Likelihood: -99.368
No. Observations: 23 AIC: 204.7
Df Residuals: 20 BIC: 208.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 239.5957 104.176 2.300 0.032 22.288 456.903
C(dose)[T.1] 52.4152 8.163 6.421 0.000 35.387 69.443
expression -25.7981 14.476 -1.782 0.090 -55.994 4.398
Omnibus: 3.185 Durbin-Watson: 1.617
Prob(Omnibus): 0.203 Jarque-Bera (JB): 1.559
Skew: 0.298 Prob(JB): 0.459
Kurtosis: 1.872 Cond. No. 188.

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: 04:44:18 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: 0.073
Model: OLS Adj. R-squared: 0.029
Method: Least Squares F-statistic: 1.650
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.213
Time: 04:44:18 Log-Likelihood: -112.23
No. Observations: 23 AIC: 228.5
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 306.8882 176.981 1.734 0.098 -61.163 674.940
expression -31.6880 24.668 -1.285 0.213 -82.988 19.612
Omnibus: 3.525 Durbin-Watson: 2.547
Prob(Omnibus): 0.172 Jarque-Bera (JB): 1.619
Skew: 0.293 Prob(JB): 0.445
Kurtosis: 1.840 Cond. No. 186.

CP101

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

F-statistic p-value df difference
0.147 0.709 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.753
Model: OLS Adj. R-squared: 0.686
Method: Least Squares F-statistic: 11.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00115
Time: 04:44:18 Log-Likelihood: -64.812
No. Observations: 15 AIC: 137.6
Df Residuals: 11 BIC: 140.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -192.0443 138.990 -1.382 0.194 -497.959 113.870
C(dose)[T.1] 1458.5007 388.426 3.755 0.003 603.581 2313.420
expression 35.9273 19.213 1.870 0.088 -6.360 78.214
expression:C(dose)[T.1] -186.5037 51.228 -3.641 0.004 -299.255 -73.753
Omnibus: 1.382 Durbin-Watson: 1.830
Prob(Omnibus): 0.501 Jarque-Bera (JB): 0.907
Skew: 0.262 Prob(JB): 0.636
Kurtosis: 1.915 Cond. No. 636.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.018
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0261
Time: 04:44:18 Log-Likelihood: -70.742
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -2.5809 183.228 -0.014 0.989 -401.800 396.638
C(dose)[T.1] 45.1829 18.832 2.399 0.034 4.151 86.215
expression 9.6937 25.321 0.383 0.709 -45.476 64.863
Omnibus: 2.608 Durbin-Watson: 0.837
Prob(Omnibus): 0.271 Jarque-Bera (JB): 1.730
Skew: -0.817 Prob(JB): 0.421
Kurtosis: 2.683 Cond. No. 179.

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: 04:44:18 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: 0.194
Model: OLS Adj. R-squared: 0.132
Method: Least Squares F-statistic: 3.133
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.100
Time: 04:44:18 Log-Likelihood: -73.681
No. Observations: 15 AIC: 151.4
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -230.1982 183.199 -1.257 0.231 -625.975 165.578
expression 43.5128 24.583 1.770 0.100 -9.596 96.621
Omnibus: 3.010 Durbin-Watson: 1.317
Prob(Omnibus): 0.222 Jarque-Bera (JB): 1.223
Skew: 0.233 Prob(JB): 0.542
Kurtosis: 1.681 Cond. No. 152.