LID SPN TRAP

1423795_at

Sfpq splicing factor proline/glutamine rich (polypyrimidine tract binding protein associated)

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
0.866 0.363 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.611
Method: Least Squares F-statistic: 12.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.49e-05
Time: 05:05:02 Log-Likelihood: -100.56
No. Observations: 23 AIC: 209.1
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 256.8551 326.773 0.786 0.442 -427.089 940.799
C(dose)[T.1] 144.5059 538.973 0.268 0.792 -983.578 1272.590
expression -19.0465 30.708 -0.620 0.542 -83.318 45.225
expression:C(dose)[T.1] -8.8993 51.035 -0.174 0.863 -115.716 97.918
Omnibus: 0.439 Durbin-Watson: 1.940
Prob(Omnibus): 0.803 Jarque-Bera (JB): 0.547
Skew: 0.040 Prob(JB): 0.761
Kurtosis: 2.248 Cond. No. 1.60e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.664
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 19.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.85e-05
Time: 05:05:02 Log-Likelihood: -100.58
No. Observations: 23 AIC: 207.2
Df Residuals: 20 BIC: 210.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 291.1351 254.621 1.143 0.266 -239.995 822.265
C(dose)[T.1] 50.5352 9.098 5.554 0.000 31.556 69.514
expression -22.2684 23.925 -0.931 0.363 -72.175 27.638
Omnibus: 0.464 Durbin-Watson: 1.904
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.559
Skew: 0.019 Prob(JB): 0.756
Kurtosis: 2.238 Cond. No. 634.

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: 05:05:03 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: 0.145
Model: OLS Adj. R-squared: 0.104
Method: Least Squares F-statistic: 3.554
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0733
Time: 05:05:03 Log-Likelihood: -111.31
No. Observations: 23 AIC: 226.6
Df Residuals: 21 BIC: 228.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 780.4620 371.745 2.099 0.048 7.376 1553.548
expression -66.2367 35.133 -1.885 0.073 -139.300 6.826
Omnibus: 0.806 Durbin-Watson: 2.266
Prob(Omnibus): 0.668 Jarque-Bera (JB): 0.778
Skew: 0.218 Prob(JB): 0.678
Kurtosis: 2.212 Cond. No. 595.

CP101

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

F-statistic p-value df difference
0.885 0.365 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.488
Model: OLS Adj. R-squared: 0.349
Method: Least Squares F-statistic: 3.498
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0533
Time: 05:05:03 Log-Likelihood: -70.276
No. Observations: 15 AIC: 148.6
Df Residuals: 11 BIC: 151.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 477.1341 570.036 0.837 0.420 -777.507 1731.775
C(dose)[T.1] 296.0268 1275.396 0.232 0.821 -2511.102 3103.155
expression -41.7017 58.009 -0.719 0.487 -169.379 85.975
expression:C(dose)[T.1] -23.3641 127.078 -0.184 0.857 -303.061 256.333
Omnibus: 1.303 Durbin-Watson: 1.246
Prob(Omnibus): 0.521 Jarque-Bera (JB): 0.758
Skew: -0.537 Prob(JB): 0.685
Kurtosis: 2.757 Cond. No. 1.94e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.487
Model: OLS Adj. R-squared: 0.401
Method: Least Squares F-statistic: 5.688
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0183
Time: 05:05:03 Log-Likelihood: -70.299
No. Observations: 15 AIC: 146.6
Df Residuals: 12 BIC: 148.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 524.9659 486.359 1.079 0.302 -534.720 1584.652
C(dose)[T.1] 61.5689 20.090 3.065 0.010 17.797 105.341
expression -46.5703 49.491 -0.941 0.365 -154.402 61.261
Omnibus: 1.514 Durbin-Watson: 1.208
Prob(Omnibus): 0.469 Jarque-Bera (JB): 0.996
Skew: -0.607 Prob(JB): 0.608
Kurtosis: 2.653 Cond. No. 647.

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: 05:05:03 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: 0.085
Model: OLS Adj. R-squared: 0.014
Method: Least Squares F-statistic: 1.205
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.292
Time: 05:05:03 Log-Likelihood: -74.635
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -431.5565 478.491 -0.902 0.384 -1465.273 602.160
expression 52.6996 48.001 1.098 0.292 -51.000 156.399
Omnibus: 0.467 Durbin-Watson: 1.134
Prob(Omnibus): 0.792 Jarque-Bera (JB): 0.528
Skew: -0.326 Prob(JB): 0.768
Kurtosis: 2.353 Cond. No. 496.