LID SPN TRAP

1424033_at

Srsf7 serine/arginine-rich splicing factor 7

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
1.919 0.181 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.629
Method: Least Squares F-statistic: 13.45
Date: Thu, 30 Oct 2025 Prob (F-statistic): 6.10e-05
Time: 22:24:20 Log-Likelihood: -100.01
No. Observations: 23 AIC: 208.0
Df Residuals: 19 BIC: 212.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 348.5411 321.389 1.084 0.292 -324.133 1021.215
C(dose)[T.1] 65.2570 445.658 0.146 0.885 -867.516 998.030
expression -30.9889 33.832 -0.916 0.371 -101.799 39.822
expression:C(dose)[T.1] -0.7354 46.551 -0.016 0.988 -98.168 96.697
Omnibus: 0.284 Durbin-Watson: 1.857
Prob(Omnibus): 0.868 Jarque-Bera (JB): 0.463
Skew: -0.087 Prob(JB): 0.793
Kurtosis: 2.327 Cond. No. 1.32e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.680
Model: OLS Adj. R-squared: 0.648
Method: Least Squares F-statistic: 21.23
Date: Thu, 30 Oct 2025 Prob (F-statistic): 1.13e-05
Time: 22:24:20 Log-Likelihood: -100.01
No. Observations: 23 AIC: 206.0
Df Residuals: 20 BIC: 209.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 352.2302 215.211 1.637 0.117 -96.692 801.152
C(dose)[T.1] 58.2185 9.088 6.406 0.000 39.261 77.176
expression -31.3773 22.650 -1.385 0.181 -78.625 15.870
Omnibus: 0.291 Durbin-Watson: 1.860
Prob(Omnibus): 0.865 Jarque-Bera (JB): 0.467
Skew: -0.087 Prob(JB): 0.792
Kurtosis: 2.323 Cond. No. 499.

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, 30 Oct 2025 Prob (F-statistic): 3.51e-06
Time: 22:24:20 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: 0.023
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.4886
Date: Thu, 30 Oct 2025 Prob (F-statistic): 0.492
Time: 22:24:20 Log-Likelihood: -112.84
No. Observations: 23 AIC: 229.7
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -158.4589 340.802 -0.465 0.647 -867.196 550.279
expression 24.8815 35.595 0.699 0.492 -49.142 98.905
Omnibus: 1.544 Durbin-Watson: 2.465
Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.179
Skew: 0.333 Prob(JB): 0.555
Kurtosis: 2.113 Cond. No. 462.

CP101

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

F-statistic p-value df difference
0.801 0.388 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.484
Model: OLS Adj. R-squared: 0.343
Method: Least Squares F-statistic: 3.433
Date: Thu, 30 Oct 2025 Prob (F-statistic): 0.0558
Time: 22:24:20 Log-Likelihood: -70.345
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 498.7019 644.721 0.774 0.456 -920.320 1917.724
C(dose)[T.1] -10.8870 941.851 -0.012 0.991 -2083.886 2062.112
expression -47.2760 70.663 -0.669 0.517 -202.803 108.251
expression:C(dose)[T.1] 7.5136 101.987 0.074 0.943 -216.958 231.985
Omnibus: 4.539 Durbin-Watson: 1.088
Prob(Omnibus): 0.103 Jarque-Bera (JB): 2.373
Skew: -0.953 Prob(JB): 0.305
Kurtosis: 3.402 Cond. No. 1.46e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.483
Model: OLS Adj. R-squared: 0.397
Method: Least Squares F-statistic: 5.611
Date: Thu, 30 Oct 2025 Prob (F-statistic): 0.0190
Time: 22:24:20 Log-Likelihood: -70.348
No. Observations: 15 AIC: 146.7
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 465.7978 445.276 1.046 0.316 -504.376 1435.971
C(dose)[T.1] 58.4868 18.439 3.172 0.008 18.311 98.662
expression -43.6690 48.796 -0.895 0.388 -149.986 62.648
Omnibus: 4.924 Durbin-Watson: 1.081
Prob(Omnibus): 0.085 Jarque-Bera (JB): 2.561
Skew: -0.980 Prob(JB): 0.278
Kurtosis: 3.507 Cond. No. 549.

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, 30 Oct 2025 Prob (F-statistic): 0.00629
Time: 22:24:20 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: 0.050
Model: OLS Adj. R-squared: -0.023
Method: Least Squares F-statistic: 0.6846
Date: Thu, 30 Oct 2025 Prob (F-statistic): 0.423
Time: 22:24:20 Log-Likelihood: -74.915
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -307.7927 485.306 -0.634 0.537 -1356.233 740.648
expression 43.4671 52.535 0.827 0.423 -70.027 156.961
Omnibus: 0.248 Durbin-Watson: 1.328
Prob(Omnibus): 0.883 Jarque-Bera (JB): 0.425
Skew: -0.087 Prob(JB): 0.809
Kurtosis: 2.194 Cond. No. 458.