LID SPN TRAP

1458963_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
3.750 0.067 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.707
Model: OLS Adj. R-squared: 0.661
Method: Least Squares F-statistic: 15.27
Date: Tue, 21 May 2024 Prob (F-statistic): 2.68e-05
Time: 00:05:03 Log-Likelihood: -98.993
No. Observations: 23 AIC: 206.0
Df Residuals: 19 BIC: 210.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -56.2264 64.574 -0.871 0.395 -191.382 78.929
C(dose)[T.1] 93.5268 101.868 0.918 0.370 -119.686 306.740
expression 20.3810 11.871 1.717 0.102 -4.465 45.227
expression:C(dose)[T.1] -7.3965 18.757 -0.394 0.698 -46.655 31.862
Omnibus: 0.345 Durbin-Watson: 2.215
Prob(Omnibus): 0.842 Jarque-Bera (JB): 0.423
Skew: -0.248 Prob(JB): 0.809
Kurtosis: 2.559 Cond. No. 173.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.704
Model: OLS Adj. R-squared: 0.675
Method: Least Squares F-statistic: 23.84
Date: Tue, 21 May 2024 Prob (F-statistic): 5.08e-06
Time: 00:05:03 Log-Likelihood: -99.086
No. Observations: 23 AIC: 204.2
Df Residuals: 20 BIC: 207.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -40.1726 49.055 -0.819 0.422 -142.500 62.155
C(dose)[T.1] 53.4870 8.048 6.646 0.000 36.699 70.275
expression 17.4183 8.995 1.936 0.067 -1.345 36.181
Omnibus: 0.206 Durbin-Watson: 2.214
Prob(Omnibus): 0.902 Jarque-Bera (JB): 0.277
Skew: -0.192 Prob(JB): 0.871
Kurtosis: 2.624 Cond. No. 68.8

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: Tue, 21 May 2024 Prob (F-statistic): 3.51e-06
Time: 00:05:03 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: 0.052
Model: OLS Adj. R-squared: 0.007
Method: Least Squares F-statistic: 1.148
Date: Tue, 21 May 2024 Prob (F-statistic): 0.296
Time: 00:05:03 Log-Likelihood: -112.49
No. Observations: 23 AIC: 229.0
Df Residuals: 21 BIC: 231.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -11.4799 85.417 -0.134 0.894 -189.115 166.155
expression 16.8435 15.722 1.071 0.296 -15.853 49.540
Omnibus: 3.536 Durbin-Watson: 2.531
Prob(Omnibus): 0.171 Jarque-Bera (JB): 1.721
Skew: 0.349 Prob(JB): 0.423
Kurtosis: 1.856 Cond. No. 68.3

CP101

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

F-statistic p-value df difference
1.578 0.233 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.513
Model: OLS Adj. R-squared: 0.380
Method: Least Squares F-statistic: 3.865
Date: Tue, 21 May 2024 Prob (F-statistic): 0.0412
Time: 00:05:03 Log-Likelihood: -69.902
No. Observations: 15 AIC: 147.8
Df Residuals: 11 BIC: 150.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -46.9621 114.283 -0.411 0.689 -298.496 204.572
C(dose)[T.1] 69.5047 182.131 0.382 0.710 -331.364 470.373
expression 23.1607 23.026 1.006 0.336 -27.519 73.840
expression:C(dose)[T.1] -3.1230 37.913 -0.082 0.936 -86.569 80.323
Omnibus: 2.456 Durbin-Watson: 0.722
Prob(Omnibus): 0.293 Jarque-Bera (JB): 1.720
Skew: -0.802 Prob(JB): 0.423
Kurtosis: 2.576 Cond. No. 149.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.513
Model: OLS Adj. R-squared: 0.432
Method: Least Squares F-statistic: 6.316
Date: Tue, 21 May 2024 Prob (F-statistic): 0.0134
Time: 00:05:03 Log-Likelihood: -69.906
No. Observations: 15 AIC: 145.8
Df Residuals: 12 BIC: 147.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -41.2727 87.200 -0.473 0.644 -231.266 148.721
C(dose)[T.1] 54.5606 15.400 3.543 0.004 21.006 88.115
expression 22.0088 17.519 1.256 0.233 -16.163 60.180
Omnibus: 2.504 Durbin-Watson: 0.708
Prob(Omnibus): 0.286 Jarque-Bera (JB): 1.748
Skew: -0.810 Prob(JB): 0.417
Kurtosis: 2.584 Cond. No. 59.8

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: Tue, 21 May 2024 Prob (F-statistic): 0.00629
Time: 00:05:03 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: 0.003
Model: OLS Adj. R-squared: -0.073
Method: Least Squares F-statistic: 0.04305
Date: Tue, 21 May 2024 Prob (F-statistic): 0.839
Time: 00:05:03 Log-Likelihood: -75.275
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 70.5845 111.704 0.632 0.538 -170.738 311.907
expression 4.7998 23.132 0.207 0.839 -45.174 54.774
Omnibus: 0.859 Durbin-Watson: 1.640
Prob(Omnibus): 0.651 Jarque-Bera (JB): 0.675
Skew: 0.092 Prob(JB): 0.713
Kurtosis: 1.977 Cond. No. 55.4