LID SPN TRAP

1454993_a_at

Srsf3 serine/arginine-rich splicing factor 3

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
0.773 0.390 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: 0.614
Method: Least Squares F-statistic: 12.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.95e-05
Time: 05:17:55 Log-Likelihood: -100.48
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 404.1603 364.309 1.109 0.281 -358.347 1166.667
C(dose)[T.1] -205.4266 526.842 -0.390 0.701 -1308.120 897.266
expression -34.0705 35.463 -0.961 0.349 -108.296 40.155
expression:C(dose)[T.1] 25.1093 51.537 0.487 0.632 -82.758 132.977
Omnibus: 0.303 Durbin-Watson: 1.825
Prob(Omnibus): 0.859 Jarque-Bera (JB): 0.473
Skew: -0.026 Prob(JB): 0.790
Kurtosis: 2.300 Cond. No. 1.59e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.662
Model: OLS Adj. R-squared: 0.628
Method: Least Squares F-statistic: 19.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.94e-05
Time: 05:17:55 Log-Likelihood: -100.63
No. Observations: 23 AIC: 207.3
Df Residuals: 20 BIC: 210.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 282.0398 259.286 1.088 0.290 -258.821 822.901
C(dose)[T.1] 51.2183 8.937 5.731 0.000 32.577 69.860
expression -22.1811 25.237 -0.879 0.390 -74.824 30.462
Omnibus: 0.008 Durbin-Watson: 1.857
Prob(Omnibus): 0.996 Jarque-Bera (JB): 0.142
Skew: -0.034 Prob(JB): 0.931
Kurtosis: 2.621 Cond. No. 623.

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: 05:17:55 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: 0.107
Model: OLS Adj. R-squared: 0.065
Method: Least Squares F-statistic: 2.520
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.127
Time: 05:17:55 Log-Likelihood: -111.80
No. Observations: 23 AIC: 227.6
Df Residuals: 21 BIC: 229.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 705.5277 394.269 1.789 0.088 -114.399 1525.454
expression -61.1996 38.551 -1.588 0.127 -141.370 18.971
Omnibus: 1.495 Durbin-Watson: 2.557
Prob(Omnibus): 0.474 Jarque-Bera (JB): 1.177
Skew: 0.345 Prob(JB): 0.555
Kurtosis: 2.133 Cond. No. 597.

CP101

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

F-statistic p-value df difference
0.050 0.827 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.469
Model: OLS Adj. R-squared: 0.324
Method: Least Squares F-statistic: 3.238
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0643
Time: 05:17:55 Log-Likelihood: -70.553
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 358.4919 528.500 0.678 0.512 -804.728 1521.712
C(dose)[T.1] -469.1441 852.528 -0.550 0.593 -2345.545 1407.256
expression -28.6187 51.952 -0.551 0.593 -142.964 85.726
expression:C(dose)[T.1] 50.6858 83.165 0.609 0.555 -132.358 233.730
Omnibus: 2.808 Durbin-Watson: 0.843
Prob(Omnibus): 0.246 Jarque-Bera (JB): 1.963
Skew: -0.862 Prob(JB): 0.375
Kurtosis: 2.591 Cond. No. 1.39e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.451
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.930
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0274
Time: 05:17:55 Log-Likelihood: -70.802
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 157.3303 401.803 0.392 0.702 -718.124 1032.785
C(dose)[T.1] 50.3369 16.513 3.048 0.010 14.359 86.315
expression -8.8396 39.491 -0.224 0.827 -94.883 77.204
Omnibus: 3.080 Durbin-Watson: 0.862
Prob(Omnibus): 0.214 Jarque-Bera (JB): 2.041
Skew: -0.893 Prob(JB): 0.360
Kurtosis: 2.719 Cond. No. 531.

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: 05:17:55 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: 0.026
Model: OLS Adj. R-squared: -0.049
Method: Least Squares F-statistic: 0.3467
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.566
Time: 05:17:55 Log-Likelihood: -75.103
No. Observations: 15 AIC: 154.2
Df Residuals: 13 BIC: 155.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -196.1684 492.348 -0.398 0.697 -1259.822 867.485
expression 28.3064 48.075 0.589 0.566 -75.553 132.166
Omnibus: 0.888 Durbin-Watson: 1.362
Prob(Omnibus): 0.642 Jarque-Bera (JB): 0.674
Skew: -0.001 Prob(JB): 0.714
Kurtosis: 1.962 Cond. No. 508.