LID SPN TRAP

1458448_at

Diexf digestive organ expansion factor homolog (zebrafish)

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
0.426 0.521 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.610
Method: Least Squares F-statistic: 12.48
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.73e-05
Time: 04:03:59 Log-Likelihood: -100.59
No. Observations: 23 AIC: 209.2
Df Residuals: 19 BIC: 213.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.5758 50.036 1.051 0.307 -52.150 157.302
C(dose)[T.1] 98.8706 70.351 1.405 0.176 -48.375 246.116
expression 0.3071 9.343 0.033 0.974 -19.247 19.862
expression:C(dose)[T.1] -7.8547 12.585 -0.624 0.540 -34.195 18.485
Omnibus: 0.759 Durbin-Watson: 1.873
Prob(Omnibus): 0.684 Jarque-Bera (JB): 0.797
Skew: 0.320 Prob(JB): 0.671
Kurtosis: 2.350 Cond. No. 123.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.30e-05
Time: 04:03:59 Log-Likelihood: -100.82
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 75.5874 33.307 2.269 0.034 6.111 145.064
C(dose)[T.1] 55.3514 9.211 6.010 0.000 36.139 74.564
expression -4.0218 6.163 -0.653 0.521 -16.878 8.834
Omnibus: 0.471 Durbin-Watson: 1.981
Prob(Omnibus): 0.790 Jarque-Bera (JB): 0.571
Skew: 0.272 Prob(JB): 0.752
Kurtosis: 2.453 Cond. No. 44.7

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: 04:03:59 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: 0.036
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.7813
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.387
Time: 04:03:59 Log-Likelihood: -112.68
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.1077 53.205 0.622 0.540 -77.538 143.753
expression 8.3902 9.492 0.884 0.387 -11.350 28.130
Omnibus: 3.621 Durbin-Watson: 2.346
Prob(Omnibus): 0.164 Jarque-Bera (JB): 1.633
Skew: 0.291 Prob(JB): 0.442
Kurtosis: 1.831 Cond. No. 43.4

CP101

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

F-statistic p-value df difference
4.144 0.064 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: 0.508
Method: Least Squares F-statistic: 5.828
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0124
Time: 04:03:59 Log-Likelihood: -68.165
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 13.4660 72.274 0.186 0.856 -145.607 172.539
C(dose)[T.1] -23.8144 95.988 -0.248 0.809 -235.083 187.454
expression 12.6714 16.806 0.754 0.467 -24.319 49.662
expression:C(dose)[T.1] 18.6678 22.807 0.819 0.430 -31.530 68.865
Omnibus: 0.435 Durbin-Watson: 1.331
Prob(Omnibus): 0.805 Jarque-Bera (JB): 0.480
Skew: 0.323 Prob(JB): 0.787
Kurtosis: 2.409 Cond. No. 83.8

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.590
Model: OLS Adj. R-squared: 0.522
Method: Least Squares F-statistic: 8.644
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00473
Time: 04:03:59 Log-Likelihood: -68.608
No. Observations: 15 AIC: 143.2
Df Residuals: 12 BIC: 145.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -29.7033 48.731 -0.610 0.554 -135.879 76.472
C(dose)[T.1] 53.9183 13.767 3.917 0.002 23.923 83.913
expression 22.8083 11.204 2.036 0.064 -1.603 47.219
Omnibus: 0.505 Durbin-Watson: 1.142
Prob(Omnibus): 0.777 Jarque-Bera (JB): 0.582
Skew: 0.267 Prob(JB): 0.747
Kurtosis: 2.195 Cond. No. 32.1

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: 04:03:59 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: 0.067
Model: OLS Adj. R-squared: -0.005
Method: Least Squares F-statistic: 0.9265
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.353
Time: 04:03:59 Log-Likelihood: -74.784
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 29.7214 67.156 0.443 0.665 -115.361 174.804
expression 15.4152 16.015 0.963 0.353 -19.184 50.014
Omnibus: 1.090 Durbin-Watson: 1.551
Prob(Omnibus): 0.580 Jarque-Bera (JB): 0.793
Skew: 0.215 Prob(JB): 0.673
Kurtosis: 1.959 Cond. No. 30.3