LID SPN TRAP

1460348_at

Mad2l2 MAD2 mitotic arrest deficient-like 2 (yeast)

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
0.000 0.987 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000143
Time: 04:41:32 Log-Likelihood: -101.06
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 66.3009 242.083 0.274 0.787 -440.385 572.987
C(dose)[T.1] 38.8483 290.897 0.134 0.895 -570.007 647.703
expression -1.4973 29.964 -0.050 0.961 -64.212 61.218
expression:C(dose)[T.1] 1.8049 36.413 0.050 0.961 -74.409 78.019
Omnibus: 0.332 Durbin-Watson: 1.875
Prob(Omnibus): 0.847 Jarque-Bera (JB): 0.491
Skew: 0.050 Prob(JB): 0.782
Kurtosis: 2.292 Cond. No. 732.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.49
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.83e-05
Time: 04:41:32 Log-Likelihood: -101.06
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 56.4304 134.175 0.421 0.679 -223.454 336.314
C(dose)[T.1] 53.2581 9.982 5.336 0.000 32.437 74.079
expression -0.2751 16.596 -0.017 0.987 -34.894 34.343
Omnibus: 0.313 Durbin-Watson: 1.887
Prob(Omnibus): 0.855 Jarque-Bera (JB): 0.480
Skew: 0.058 Prob(JB): 0.787
Kurtosis: 2.302 Cond. No. 248.

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: 04:41:32 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: 0.150
Model: OLS Adj. R-squared: 0.109
Method: Least Squares F-statistic: 3.692
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0684
Time: 04:41:33 Log-Likelihood: -111.24
No. Observations: 23 AIC: 226.5
Df Residuals: 21 BIC: 228.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 417.6362 175.995 2.373 0.027 51.636 783.637
expression -42.5636 22.152 -1.921 0.068 -88.631 3.504
Omnibus: 0.717 Durbin-Watson: 2.179
Prob(Omnibus): 0.699 Jarque-Bera (JB): 0.661
Skew: 0.363 Prob(JB): 0.719
Kurtosis: 2.597 Cond. No. 213.

CP101

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

F-statistic p-value df difference
1.467 0.249 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.566
Model: OLS Adj. R-squared: 0.447
Method: Least Squares F-statistic: 4.777
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0228
Time: 04:41:33 Log-Likelihood: -69.044
No. Observations: 15 AIC: 146.1
Df Residuals: 11 BIC: 148.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 374.6390 500.671 0.748 0.470 -727.330 1476.608
C(dose)[T.1] -592.1830 542.160 -1.092 0.298 -1785.470 601.104
expression -37.8026 61.594 -0.614 0.552 -173.370 97.765
expression:C(dose)[T.1] 80.5843 67.092 1.201 0.255 -67.085 228.253
Omnibus: 0.401 Durbin-Watson: 1.593
Prob(Omnibus): 0.819 Jarque-Bera (JB): 0.091
Skew: 0.176 Prob(JB): 0.955
Kurtosis: 2.853 Cond. No. 926.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.509
Model: OLS Adj. R-squared: 0.427
Method: Least Squares F-statistic: 6.215
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0140
Time: 04:41:33 Log-Likelihood: -69.968
No. Observations: 15 AIC: 145.9
Df Residuals: 12 BIC: 148.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -177.3119 202.368 -0.876 0.398 -618.234 263.610
C(dose)[T.1] 58.7033 16.804 3.493 0.004 22.091 95.316
expression 30.1156 24.866 1.211 0.249 -24.062 84.293
Omnibus: 0.278 Durbin-Watson: 1.050
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.413
Skew: -0.241 Prob(JB): 0.814
Kurtosis: 2.346 Cond. No. 221.

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: 04:41:33 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: 0.009
Model: OLS Adj. R-squared: -0.067
Method: Least Squares F-statistic: 0.1216
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.733
Time: 04:41:33 Log-Likelihood: -75.230
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 176.9349 238.963 0.740 0.472 -339.314 693.184
expression -10.4630 30.000 -0.349 0.733 -75.274 54.348
Omnibus: 0.944 Durbin-Watson: 1.528
Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.691
Skew: 0.023 Prob(JB): 0.708
Kurtosis: 1.949 Cond. No. 191.