LID SPN TRAP

1424883_s_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
0.862 0.364 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.682
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 13.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.75e-05
Time: 04:30:58 Log-Likelihood: -99.937
No. Observations: 23 AIC: 207.9
Df Residuals: 19 BIC: 212.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 151.5963 392.744 0.386 0.704 -670.427 973.619
C(dose)[T.1] 853.5251 761.467 1.121 0.276 -740.244 2447.295
expression -8.2438 33.242 -0.248 0.807 -77.819 61.332
expression:C(dose)[T.1] -66.3652 63.602 -1.043 0.310 -199.486 66.756
Omnibus: 0.233 Durbin-Watson: 1.808
Prob(Omnibus): 0.890 Jarque-Bera (JB): 0.428
Skew: 0.081 Prob(JB): 0.807
Kurtosis: 2.352 Cond. No. 2.53e+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: 19.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.86e-05
Time: 04:30:58 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 365.7545 335.590 1.090 0.289 -334.275 1065.784
C(dose)[T.1] 59.0569 10.568 5.588 0.000 37.013 81.101
expression -26.3720 28.403 -0.928 0.364 -85.619 32.875
Omnibus: 1.593 Durbin-Watson: 2.159
Prob(Omnibus): 0.451 Jarque-Bera (JB): 0.959
Skew: -0.011 Prob(JB): 0.619
Kurtosis: 2.000 Cond. No. 940.

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: 04:30:58 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: 0.138
Model: OLS Adj. R-squared: 0.097
Method: Least Squares F-statistic: 3.368
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0807
Time: 04:30:58 Log-Likelihood: -111.39
No. Observations: 23 AIC: 226.8
Df Residuals: 21 BIC: 229.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -708.6285 429.616 -1.649 0.114 -1602.064 184.807
expression 66.1517 36.046 1.835 0.081 -8.809 141.112
Omnibus: 0.905 Durbin-Watson: 1.957
Prob(Omnibus): 0.636 Jarque-Bera (JB): 0.899
Skew: 0.379 Prob(JB): 0.638
Kurtosis: 2.397 Cond. No. 770.

CP101

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

F-statistic p-value df difference
0.026 0.875 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.317
Method: Least Squares F-statistic: 3.164
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0680
Time: 04:30:58 Log-Likelihood: -70.634
No. Observations: 15 AIC: 149.3
Df Residuals: 11 BIC: 152.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -382.1073 869.816 -0.439 0.669 -2296.560 1532.346
C(dose)[T.1] 601.8306 1064.338 0.565 0.583 -1740.763 2944.424
expression 39.5190 76.459 0.517 0.615 -128.766 207.804
expression:C(dose)[T.1] -48.3915 92.906 -0.521 0.613 -252.876 156.093
Omnibus: 2.916 Durbin-Watson: 0.870
Prob(Omnibus): 0.233 Jarque-Bera (JB): 1.898
Skew: -0.862 Prob(JB): 0.387
Kurtosis: 2.739 Cond. No. 2.20e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.908
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0277
Time: 04:30:58 Log-Likelihood: -70.817
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -9.2892 478.984 -0.019 0.985 -1052.905 1034.327
C(dose)[T.1] 47.5460 18.797 2.529 0.026 6.591 88.501
expression 6.7443 42.096 0.160 0.875 -84.974 98.463
Omnibus: 2.682 Durbin-Watson: 0.821
Prob(Omnibus): 0.262 Jarque-Bera (JB): 1.915
Skew: -0.844 Prob(JB): 0.384
Kurtosis: 2.539 Cond. No. 709.

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: 04:30:58 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: 0.157
Model: OLS Adj. R-squared: 0.092
Method: Least Squares F-statistic: 2.415
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.144
Time: 04:30:58 Log-Likelihood: -74.022
No. Observations: 15 AIC: 152.0
Df Residuals: 13 BIC: 153.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -655.3434 482.042 -1.360 0.197 -1696.731 386.044
expression 65.0990 41.888 1.554 0.144 -25.395 155.593
Omnibus: 0.979 Durbin-Watson: 1.741
Prob(Omnibus): 0.613 Jarque-Bera (JB): 0.748
Skew: 0.195 Prob(JB): 0.688
Kurtosis: 1.977 Cond. No. 599.