LID SPN TRAP

1460583_at

Golt1b golgi transport 1 homolog B (S. cerevisiae)

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
2.056 0.167 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.682
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 13.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.67e-05
Time: 05:17:43 Log-Likelihood: -99.918
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 120.4933 63.669 1.892 0.074 -12.768 253.755
C(dose)[T.1] 69.4154 108.051 0.642 0.528 -156.738 295.569
expression -8.8706 8.484 -1.046 0.309 -26.627 8.886
expression:C(dose)[T.1] -2.6206 14.818 -0.177 0.861 -33.635 28.394
Omnibus: 1.235 Durbin-Watson: 1.794
Prob(Omnibus): 0.539 Jarque-Bera (JB): 0.862
Skew: -0.065 Prob(JB): 0.650
Kurtosis: 2.060 Cond. No. 228.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.682
Model: OLS Adj. R-squared: 0.650
Method: Least Squares F-statistic: 21.42
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.07e-05
Time: 05:17:43 Log-Likelihood: -99.937
No. Observations: 23 AIC: 205.9
Df Residuals: 20 BIC: 209.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 126.9119 51.029 2.487 0.022 20.466 233.358
C(dose)[T.1] 50.3701 8.604 5.855 0.000 32.423 68.317
expression -9.7296 6.785 -1.434 0.167 -23.883 4.424
Omnibus: 1.248 Durbin-Watson: 1.815
Prob(Omnibus): 0.536 Jarque-Bera (JB): 0.877
Skew: -0.097 Prob(JB): 0.645
Kurtosis: 2.063 Cond. No. 91.9

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: 05:17:43 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: 0.136
Model: OLS Adj. R-squared: 0.095
Method: Least Squares F-statistic: 3.317
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0829
Time: 05:17:43 Log-Likelihood: -111.42
No. Observations: 23 AIC: 226.8
Df Residuals: 21 BIC: 229.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 220.9965 77.864 2.838 0.010 59.070 382.923
expression -19.2830 10.588 -1.821 0.083 -41.302 2.736
Omnibus: 0.716 Durbin-Watson: 2.640
Prob(Omnibus): 0.699 Jarque-Bera (JB): 0.732
Skew: 0.212 Prob(JB): 0.694
Kurtosis: 2.236 Cond. No. 86.9

CP101

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

F-statistic p-value df difference
0.147 0.708 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.331
Method: Least Squares F-statistic: 3.304
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0613
Time: 05:17:43 Log-Likelihood: -70.482
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -57.1312 179.998 -0.317 0.757 -453.304 339.042
C(dose)[T.1] 216.7331 269.260 0.805 0.438 -375.905 809.371
expression 15.7833 22.760 0.693 0.502 -34.310 65.877
expression:C(dose)[T.1] -21.2490 34.127 -0.623 0.546 -96.363 53.865
Omnibus: 2.007 Durbin-Watson: 0.893
Prob(Omnibus): 0.367 Jarque-Bera (JB): 1.520
Skew: -0.721 Prob(JB): 0.468
Kurtosis: 2.407 Cond. No. 349.

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: 05:17:43 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 17.4522 130.880 0.133 0.896 -267.711 302.615
C(dose)[T.1] 49.3796 15.651 3.155 0.008 15.278 83.481
expression 6.3326 16.521 0.383 0.708 -29.663 42.328
Omnibus: 4.353 Durbin-Watson: 0.766
Prob(Omnibus): 0.113 Jarque-Bera (JB): 2.608
Skew: -1.021 Prob(JB): 0.271
Kurtosis: 3.073 Cond. No. 135.

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: 05:17:43 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: 0.004
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.04881
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.829
Time: 05:17:43 Log-Likelihood: -75.272
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 56.3236 169.326 0.333 0.745 -309.483 422.130
expression 4.7411 21.459 0.221 0.829 -41.618 51.101
Omnibus: 0.780 Durbin-Watson: 1.666
Prob(Omnibus): 0.677 Jarque-Bera (JB): 0.641
Skew: 0.034 Prob(JB): 0.726
Kurtosis: 1.989 Cond. No. 134.