LID SPN TRAP

1429372_at

Sox11 SRY-box containing gene 11

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
0.139 0.713 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.600
Method: Least Squares F-statistic: 11.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000124
Time: 03:36:27 Log-Likelihood: -100.89
No. Observations: 23 AIC: 209.8
Df Residuals: 19 BIC: 214.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.4059 159.099 0.386 0.704 -271.592 394.403
C(dose)[T.1] 149.8329 251.907 0.595 0.559 -377.415 677.081
expression -0.9918 21.906 -0.045 0.964 -46.843 44.859
expression:C(dose)[T.1] -13.7536 35.361 -0.389 0.702 -87.765 60.258
Omnibus: 0.363 Durbin-Watson: 1.845
Prob(Omnibus): 0.834 Jarque-Bera (JB): 0.507
Skew: 0.031 Prob(JB): 0.776
Kurtosis: 2.276 Cond. No. 508.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.69
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.64e-05
Time: 03:36:27 Log-Likelihood: -100.98
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 99.7132 122.269 0.816 0.424 -155.336 354.762
C(dose)[T.1] 51.9270 9.524 5.452 0.000 32.061 71.793
expression -6.2703 16.827 -0.373 0.713 -41.372 28.831
Omnibus: 0.294 Durbin-Watson: 1.848
Prob(Omnibus): 0.863 Jarque-Bera (JB): 0.468
Skew: 0.057 Prob(JB): 0.791
Kurtosis: 2.310 Cond. No. 205.

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: 03:36:27 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: 0.133
Model: OLS Adj. R-squared: 0.092
Method: Least Squares F-statistic: 3.233
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0866
Time: 03:36:27 Log-Likelihood: -111.46
No. Observations: 23 AIC: 226.9
Df Residuals: 21 BIC: 229.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 385.1976 170.029 2.265 0.034 31.603 738.792
expression -42.7268 23.763 -1.798 0.087 -92.145 6.691
Omnibus: 0.775 Durbin-Watson: 2.309
Prob(Omnibus): 0.679 Jarque-Bera (JB): 0.713
Skew: 0.110 Prob(JB): 0.700
Kurtosis: 2.166 Cond. No. 185.

CP101

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

F-statistic p-value df difference
0.022 0.886 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.614
Model: OLS Adj. R-squared: 0.509
Method: Least Squares F-statistic: 5.830
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0123
Time: 03:36:27 Log-Likelihood: -68.163
No. Observations: 15 AIC: 144.3
Df Residuals: 11 BIC: 147.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 232.7122 128.111 1.816 0.097 -49.258 514.682
C(dose)[T.1] -381.9259 199.578 -1.914 0.082 -821.195 57.343
expression -23.8861 18.457 -1.294 0.222 -64.510 16.737
expression:C(dose)[T.1] 60.7366 28.089 2.162 0.053 -1.086 122.559
Omnibus: 0.935 Durbin-Watson: 1.132
Prob(Omnibus): 0.627 Jarque-Bera (JB): 0.745
Skew: -0.216 Prob(JB): 0.689
Kurtosis: 1.998 Cond. No. 271.

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: 4.904
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0278
Time: 03:36:27 Log-Likelihood: -70.819
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 51.2463 110.632 0.463 0.651 -189.799 292.292
C(dose)[T.1] 48.5082 16.407 2.957 0.012 12.760 84.256
expression 2.3386 15.902 0.147 0.886 -32.308 36.985
Omnibus: 2.393 Durbin-Watson: 0.793
Prob(Omnibus): 0.302 Jarque-Bera (JB): 1.746
Skew: -0.795 Prob(JB): 0.418
Kurtosis: 2.484 Cond. No. 102.

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: 03:36:27 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: 0.049
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.6692
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.428
Time: 03:36:27 Log-Likelihood: -74.924
No. Observations: 15 AIC: 153.8
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.7813 136.594 -0.130 0.898 -312.874 277.312
expression 15.7487 19.251 0.818 0.428 -25.841 57.338
Omnibus: 0.210 Durbin-Watson: 1.383
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.375
Skew: -0.201 Prob(JB): 0.829
Kurtosis: 2.337 Cond. No. 99.7