LID SPN TRAP

1454829_at

Rundc1 RUN 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
0.939 0.344 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.612
Method: Least Squares F-statistic: 12.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.32e-05
Time: 03:51:41 Log-Likelihood: -100.53
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -210.1400 414.976 -0.506 0.618 -1078.694 658.414
C(dose)[T.1] 27.1273 587.570 0.046 0.964 -1202.671 1256.925
expression 28.6163 44.917 0.637 0.532 -65.397 122.629
expression:C(dose)[T.1] 2.3811 63.138 0.038 0.970 -129.768 134.531
Omnibus: 0.021 Durbin-Watson: 1.846
Prob(Omnibus): 0.990 Jarque-Bera (JB): 0.154
Skew: -0.059 Prob(JB): 0.926
Kurtosis: 2.618 Cond. No. 1.64e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 19.83
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.79e-05
Time: 03:51:41 Log-Likelihood: -100.53
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -221.2724 284.292 -0.778 0.445 -814.295 371.750
C(dose)[T.1] 49.2832 9.537 5.168 0.000 29.389 69.177
expression 29.8214 30.769 0.969 0.344 -34.361 94.003
Omnibus: 0.015 Durbin-Watson: 1.844
Prob(Omnibus): 0.993 Jarque-Bera (JB): 0.158
Skew: -0.051 Prob(JB): 0.924
Kurtosis: 2.607 Cond. No. 626.

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: 03:51:41 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: 0.217
Model: OLS Adj. R-squared: 0.180
Method: Least Squares F-statistic: 5.828
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0250
Time: 03:51:41 Log-Likelihood: -110.29
No. Observations: 23 AIC: 224.6
Df Residuals: 21 BIC: 226.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -846.4028 383.667 -2.206 0.039 -1644.283 -48.523
expression 99.5539 41.237 2.414 0.025 13.797 185.311
Omnibus: 3.827 Durbin-Watson: 2.356
Prob(Omnibus): 0.148 Jarque-Bera (JB): 1.463
Skew: 0.110 Prob(JB): 0.481
Kurtosis: 1.784 Cond. No. 566.

CP101

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

F-statistic p-value df difference
1.358 0.267 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.624
Model: OLS Adj. R-squared: 0.521
Method: Least Squares F-statistic: 6.085
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0107
Time: 03:51:41 Log-Likelihood: -67.964
No. Observations: 15 AIC: 143.9
Df Residuals: 11 BIC: 146.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -153.0309 472.507 -0.324 0.752 -1193.012 886.950
C(dose)[T.1] 1257.4372 649.734 1.935 0.079 -172.617 2687.492
expression 25.0491 53.675 0.467 0.650 -93.090 143.188
expression:C(dose)[T.1] -138.5441 74.200 -1.867 0.089 -301.857 24.769
Omnibus: 2.266 Durbin-Watson: 0.951
Prob(Omnibus): 0.322 Jarque-Bera (JB): 1.418
Skew: -0.743 Prob(JB): 0.492
Kurtosis: 2.751 Cond. No. 1.14e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.505
Model: OLS Adj. R-squared: 0.422
Method: Least Squares F-statistic: 6.117
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0147
Time: 03:51:41 Log-Likelihood: -70.029
No. Observations: 15 AIC: 146.1
Df Residuals: 12 BIC: 148.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 485.0402 358.532 1.353 0.201 -296.134 1266.214
C(dose)[T.1] 44.5561 15.440 2.886 0.014 10.914 78.198
expression -47.4500 40.718 -1.165 0.267 -136.168 41.268
Omnibus: 2.045 Durbin-Watson: 0.687
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.591
Skew: -0.693 Prob(JB): 0.451
Kurtosis: 2.209 Cond. No. 428.

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: 03:51:41 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: 0.161
Model: OLS Adj. R-squared: 0.097
Method: Least Squares F-statistic: 2.498
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.138
Time: 03:51:41 Log-Likelihood: -73.982
No. Observations: 15 AIC: 152.0
Df Residuals: 13 BIC: 153.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 773.9203 430.495 1.798 0.095 -156.107 1703.948
expression -77.7527 49.194 -1.581 0.138 -184.030 28.524
Omnibus: 0.667 Durbin-Watson: 1.334
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.618
Skew: -0.405 Prob(JB): 0.734
Kurtosis: 2.424 Cond. No. 410.