LID SPN TRAP

1460656_a_at

Sft2d1 SFT2 domain containing 1

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
0.022 0.882 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.597
Method: Least Squares F-statistic: 11.88
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000131
Time: 04:06:46 Log-Likelihood: -100.95
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.6354 269.884 -0.177 0.862 -612.509 517.238
C(dose)[T.1] 212.2397 401.900 0.528 0.604 -628.947 1053.426
expression 11.6392 30.836 0.377 0.710 -52.900 76.179
expression:C(dose)[T.1] -18.0167 45.368 -0.397 0.696 -112.972 76.939
Omnibus: 0.472 Durbin-Watson: 1.985
Prob(Omnibus): 0.790 Jarque-Bera (JB): 0.567
Skew: 0.067 Prob(JB): 0.753
Kurtosis: 2.242 Cond. No. 1.03e+03

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: 18.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-05
Time: 04:06:46 Log-Likelihood: -101.05
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 25.1926 193.792 0.130 0.898 -379.050 429.435
C(dose)[T.1] 52.6840 9.789 5.382 0.000 32.264 73.104
expression 3.3161 22.137 0.150 0.882 -42.860 49.492
Omnibus: 0.297 Durbin-Watson: 1.905
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.468
Skew: 0.017 Prob(JB): 0.791
Kurtosis: 2.302 Cond. No. 397.

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: 04:06:46 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: 0.142
Model: OLS Adj. R-squared: 0.101
Method: Least Squares F-statistic: 3.470
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0766
Time: 04:06:46 Log-Likelihood: -111.35
No. Observations: 23 AIC: 226.7
Df Residuals: 21 BIC: 229.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -418.8627 267.750 -1.564 0.133 -975.678 137.953
expression 56.3735 30.264 1.863 0.077 -6.565 119.312
Omnibus: 2.341 Durbin-Watson: 2.370
Prob(Omnibus): 0.310 Jarque-Bera (JB): 1.181
Skew: 0.122 Prob(JB): 0.554
Kurtosis: 1.917 Cond. No. 359.

CP101

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

F-statistic p-value df difference
0.213 0.652 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.508
Model: OLS Adj. R-squared: 0.373
Method: Least Squares F-statistic: 3.782
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0436
Time: 04:06:46 Log-Likelihood: -69.984
No. Observations: 15 AIC: 148.0
Df Residuals: 11 BIC: 150.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -193.4281 248.832 -0.777 0.453 -741.104 354.248
C(dose)[T.1] 434.6577 365.658 1.189 0.260 -370.151 1239.466
expression 30.9488 29.491 1.049 0.316 -33.962 95.859
expression:C(dose)[T.1] -46.0628 43.867 -1.050 0.316 -142.613 50.487
Omnibus: 2.904 Durbin-Watson: 1.348
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.600
Skew: -0.800 Prob(JB): 0.449
Kurtosis: 3.002 Cond. No. 519.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.368
Method: Least Squares F-statistic: 5.078
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0252
Time: 04:06:46 Log-Likelihood: -70.701
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.9470 185.151 -0.097 0.924 -421.356 385.462
C(dose)[T.1] 51.0639 16.117 3.168 0.008 15.949 86.179
expression 10.1292 21.925 0.462 0.652 -37.642 57.900
Omnibus: 2.354 Durbin-Watson: 0.928
Prob(Omnibus): 0.308 Jarque-Bera (JB): 1.747
Skew: -0.788 Prob(JB): 0.418
Kurtosis: 2.440 Cond. No. 202.

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: 04:06:46 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: 0.005
Model: OLS Adj. R-squared: -0.071
Method: Least Squares F-statistic: 0.06967
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.796
Time: 04:06:46 Log-Likelihood: -75.260
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 154.4315 230.429 0.670 0.514 -343.381 652.244
expression -7.2944 27.635 -0.264 0.796 -66.996 52.407
Omnibus: 0.194 Durbin-Watson: 1.586
Prob(Omnibus): 0.907 Jarque-Bera (JB): 0.392
Skew: -0.055 Prob(JB): 0.822
Kurtosis: 2.215 Cond. No. 192.