LID SPN TRAP

1460552_at

Snrnp200 small nuclear ribonucleoprotein 200 (U5)

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
5.210 0.034 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.723
Model: OLS Adj. R-squared: 0.679
Method: Least Squares F-statistic: 16.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.59e-05
Time: 04:35:30 Log-Likelihood: -98.348
No. Observations: 23 AIC: 204.7
Df Residuals: 19 BIC: 209.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 426.4564 251.562 1.695 0.106 -100.069 952.982
C(dose)[T.1] 149.5403 373.909 0.400 0.694 -633.060 932.140
expression -35.9543 24.292 -1.480 0.155 -86.798 14.889
expression:C(dose)[T.1] -10.8691 36.801 -0.295 0.771 -87.894 66.156
Omnibus: 0.136 Durbin-Watson: 2.171
Prob(Omnibus): 0.934 Jarque-Bera (JB): 0.120
Skew: 0.123 Prob(JB): 0.942
Kurtosis: 2.746 Cond. No. 1.21e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.722
Model: OLS Adj. R-squared: 0.694
Method: Least Squares F-statistic: 25.92
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.80e-06
Time: 04:35:30 Log-Likelihood: -98.400
No. Observations: 23 AIC: 202.8
Df Residuals: 20 BIC: 206.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 475.4886 184.642 2.575 0.018 90.332 860.645
C(dose)[T.1] 39.1472 9.983 3.921 0.001 18.323 59.972
expression -40.6902 17.826 -2.283 0.034 -77.875 -3.505
Omnibus: 0.072 Durbin-Watson: 2.175
Prob(Omnibus): 0.965 Jarque-Bera (JB): 0.074
Skew: 0.058 Prob(JB): 0.964
Kurtosis: 2.748 Cond. No. 488.

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: 04:35:30 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: 0.508
Model: OLS Adj. R-squared: 0.484
Method: Least Squares F-statistic: 21.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000137
Time: 04:35:30 Log-Likelihood: -104.96
No. Observations: 23 AIC: 213.9
Df Residuals: 21 BIC: 216.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 937.6348 184.486 5.082 0.000 553.975 1321.294
expression -84.2204 18.104 -4.652 0.000 -121.869 -46.571
Omnibus: 0.325 Durbin-Watson: 2.116
Prob(Omnibus): 0.850 Jarque-Bera (JB): 0.369
Skew: 0.242 Prob(JB): 0.831
Kurtosis: 2.612 Cond. No. 375.

CP101

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

F-statistic p-value df difference
0.446 0.517 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.331
Method: Least Squares F-statistic: 3.305
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0612
Time: 04:35:30 Log-Likelihood: -70.480
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 152.0946 470.659 0.323 0.753 -883.819 1188.008
C(dose)[T.1] 272.2198 641.937 0.424 0.680 -1140.673 1685.113
expression -8.1423 45.249 -0.180 0.860 -107.735 91.450
expression:C(dose)[T.1] -20.9099 61.199 -0.342 0.739 -155.608 113.788
Omnibus: 2.483 Durbin-Watson: 0.807
Prob(Omnibus): 0.289 Jarque-Bera (JB): 1.862
Skew: -0.741 Prob(JB): 0.394
Kurtosis: 2.114 Cond. No. 1.15e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.469
Model: OLS Adj. R-squared: 0.380
Method: Least Squares F-statistic: 5.289
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0225
Time: 04:35:30 Log-Likelihood: -70.560
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 270.9569 305.120 0.888 0.392 -393.842 935.756
C(dose)[T.1] 52.9673 16.455 3.219 0.007 17.114 88.821
expression -19.5733 29.323 -0.668 0.517 -83.463 44.317
Omnibus: 2.480 Durbin-Watson: 0.948
Prob(Omnibus): 0.289 Jarque-Bera (JB): 1.892
Skew: -0.770 Prob(JB): 0.388
Kurtosis: 2.190 Cond. No. 420.

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: 04:35:30 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: 0.010
Model: OLS Adj. R-squared: -0.067
Method: Least Squares F-statistic: 0.1262
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.728
Time: 04:35:31 Log-Likelihood: -75.228
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 -41.0626 379.437 -0.108 0.915 -860.786 778.661
expression 12.8301 36.121 0.355 0.728 -65.204 90.864
Omnibus: 0.247 Durbin-Watson: 1.509
Prob(Omnibus): 0.884 Jarque-Bera (JB): 0.423
Skew: 0.033 Prob(JB): 0.809
Kurtosis: 2.180 Cond. No. 398.