LID SPN TRAP

1459840_s_at

Ccdc28b coiled coil domain containing 28B

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
0.339 0.567 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.613
Method: Least Squares F-statistic: 12.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.12e-05
Time: 04:41:02 Log-Likelihood: -100.51
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.3935 187.907 0.274 0.787 -341.900 444.687
C(dose)[T.1] -186.9366 313.277 -0.597 0.558 -842.634 468.761
expression 0.2934 19.576 0.015 0.988 -40.679 41.266
expression:C(dose)[T.1] 26.2633 33.655 0.780 0.445 -44.177 96.704
Omnibus: 2.919 Durbin-Watson: 1.761
Prob(Omnibus): 0.232 Jarque-Bera (JB): 1.488
Skew: 0.288 Prob(JB): 0.475
Kurtosis: 1.895 Cond. No. 820.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.40e-05
Time: 04:41:02 Log-Likelihood: -100.87
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -33.8555 151.388 -0.224 0.825 -349.644 281.933
C(dose)[T.1] 57.3788 11.128 5.156 0.000 34.167 80.591
expression 9.1792 15.767 0.582 0.567 -23.710 42.069
Omnibus: 1.462 Durbin-Watson: 1.857
Prob(Omnibus): 0.482 Jarque-Bera (JB): 0.964
Skew: 0.140 Prob(JB): 0.617
Kurtosis: 2.037 Cond. No. 332.

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: 04:41:02 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: 0.196
Model: OLS Adj. R-squared: 0.158
Method: Least Squares F-statistic: 5.124
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0343
Time: 04:41:02 Log-Likelihood: -110.59
No. Observations: 23 AIC: 225.2
Df Residuals: 21 BIC: 227.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 469.5354 172.337 2.725 0.013 111.140 827.930
expression -41.5438 18.353 -2.264 0.034 -79.712 -3.376
Omnibus: 3.331 Durbin-Watson: 2.226
Prob(Omnibus): 0.189 Jarque-Bera (JB): 1.383
Skew: 0.121 Prob(JB): 0.501
Kurtosis: 1.823 Cond. No. 253.

CP101

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

F-statistic p-value df difference
0.004 0.949 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.494
Model: OLS Adj. R-squared: 0.356
Method: Least Squares F-statistic: 3.575
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0504
Time: 04:41:02 Log-Likelihood: -70.196
No. Observations: 15 AIC: 148.4
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 712.8116 803.393 0.887 0.394 -1055.445 2481.068
C(dose)[T.1] -881.6243 944.646 -0.933 0.371 -2960.775 1197.527
expression -61.6748 76.767 -0.803 0.439 -230.638 107.288
expression:C(dose)[T.1] 89.0342 90.336 0.986 0.346 -109.794 287.862
Omnibus: 1.255 Durbin-Watson: 1.061
Prob(Omnibus): 0.534 Jarque-Bera (JB): 0.943
Skew: -0.563 Prob(JB): 0.624
Kurtosis: 2.509 Cond. No. 1.86e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.889
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:41:02 Log-Likelihood: -70.830
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 40.0002 423.089 0.095 0.926 -881.831 961.831
C(dose)[T.1] 49.2787 15.788 3.121 0.009 14.880 83.678
expression 2.6211 40.417 0.065 0.949 -85.439 90.682
Omnibus: 2.616 Durbin-Watson: 0.811
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.806
Skew: -0.827 Prob(JB): 0.405
Kurtosis: 2.609 Cond. No. 569.

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: 04:41:02 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: 0.002
Model: OLS Adj. R-squared: -0.075
Method: Least Squares F-statistic: 0.02079
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.888
Time: 04:41:02 Log-Likelihood: -75.288
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 172.1507 544.413 0.316 0.757 -1003.982 1348.284
expression -7.5122 52.100 -0.144 0.888 -120.068 105.043
Omnibus: 0.796 Durbin-Watson: 1.623
Prob(Omnibus): 0.672 Jarque-Bera (JB): 0.649
Skew: 0.059 Prob(JB): 0.723
Kurtosis: 1.988 Cond. No. 565.