LID SPN TRAP

1440192_at

Ttc39b tetratricopeptide repeat domain 39B

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
2.895 0.104 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.700
Model: OLS Adj. R-squared: 0.653
Method: Least Squares F-statistic: 14.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.33e-05
Time: 04:14:13 Log-Likelihood: -99.261
No. Observations: 23 AIC: 206.5
Df Residuals: 19 BIC: 211.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 147.4519 111.913 1.318 0.203 -86.784 381.688
C(dose)[T.1] 156.4544 166.812 0.938 0.360 -192.688 505.597
expression -12.2530 14.687 -0.834 0.414 -42.993 18.487
expression:C(dose)[T.1] -14.2625 22.228 -0.642 0.529 -60.785 32.260
Omnibus: 1.611 Durbin-Watson: 1.287
Prob(Omnibus): 0.447 Jarque-Bera (JB): 1.053
Skew: 0.199 Prob(JB): 0.591
Kurtosis: 2.030 Cond. No. 387.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.693
Model: OLS Adj. R-squared: 0.663
Method: Least Squares F-statistic: 22.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.33e-06
Time: 04:14:14 Log-Likelihood: -99.508
No. Observations: 23 AIC: 205.0
Df Residuals: 20 BIC: 208.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 194.8373 82.843 2.352 0.029 22.031 367.644
C(dose)[T.1] 49.5605 8.492 5.836 0.000 31.847 67.274
expression -18.4798 10.861 -1.702 0.104 -41.135 4.175
Omnibus: 2.105 Durbin-Watson: 1.237
Prob(Omnibus): 0.349 Jarque-Bera (JB): 1.139
Skew: 0.143 Prob(JB): 0.566
Kurtosis: 1.948 Cond. No. 155.

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: 04:14:14 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: 0.171
Model: OLS Adj. R-squared: 0.132
Method: Least Squares F-statistic: 4.342
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0496
Time: 04:14:14 Log-Likelihood: -110.94
No. Observations: 23 AIC: 225.9
Df Residuals: 21 BIC: 228.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 342.9971 126.525 2.711 0.013 79.873 606.121
expression -35.0472 16.820 -2.084 0.050 -70.026 -0.068
Omnibus: 1.897 Durbin-Watson: 1.942
Prob(Omnibus): 0.387 Jarque-Bera (JB): 1.100
Skew: 0.160 Prob(JB): 0.577
Kurtosis: 1.978 Cond. No. 147.

CP101

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

F-statistic p-value df difference
0.320 0.582 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.559
Model: OLS Adj. R-squared: 0.439
Method: Least Squares F-statistic: 4.646
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0248
Time: 04:14:14 Log-Likelihood: -69.162
No. Observations: 15 AIC: 146.3
Df Residuals: 11 BIC: 149.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 170.1557 170.096 1.000 0.339 -204.224 544.535
C(dose)[T.1] -333.0993 248.746 -1.339 0.208 -880.586 214.388
expression -13.8947 22.961 -0.605 0.557 -64.432 36.642
expression:C(dose)[T.1] 52.4682 33.947 1.546 0.150 -22.249 127.185
Omnibus: 1.420 Durbin-Watson: 1.017
Prob(Omnibus): 0.492 Jarque-Bera (JB): 0.858
Skew: -0.570 Prob(JB): 0.651
Kurtosis: 2.733 Cond. No. 330.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.374
Method: Least Squares F-statistic: 5.175
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0240
Time: 04:14:14 Log-Likelihood: -70.635
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -7.3078 132.559 -0.055 0.957 -296.130 281.514
C(dose)[T.1] 50.6681 15.750 3.217 0.007 16.352 84.984
expression 10.1087 17.864 0.566 0.582 -28.814 49.031
Omnibus: 2.613 Durbin-Watson: 0.784
Prob(Omnibus): 0.271 Jarque-Bera (JB): 1.770
Skew: -0.823 Prob(JB): 0.413
Kurtosis: 2.646 Cond. No. 128.

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: 04:14:14 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.0007186
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.979
Time: 04:14:14 Log-Likelihood: -75.300
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 89.1362 169.305 0.526 0.607 -276.625 454.898
expression 0.6193 23.101 0.027 0.979 -49.288 50.527
Omnibus: 0.549 Durbin-Watson: 1.619
Prob(Omnibus): 0.760 Jarque-Bera (JB): 0.560
Skew: 0.038 Prob(JB): 0.756
Kurtosis: 2.056 Cond. No. 124.