LID SPN TRAP

1460733_at

Unc119b unc-119 homolog B (C. elegans)

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
0.853 0.367 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.672
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 12.97
Date: Sun, 09 Nov 2025 Prob (F-statistic): 7.64e-05
Time: 19:46:54 Log-Likelihood: -100.29
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 207.0086 133.241 1.554 0.137 -71.868 485.885
C(dose)[T.1] -113.5160 237.200 -0.479 0.638 -609.982 382.950
expression -20.3413 17.719 -1.148 0.265 -57.428 16.746
expression:C(dose)[T.1] 22.2252 31.708 0.701 0.492 -44.141 88.591
Omnibus: 0.337 Durbin-Watson: 2.003
Prob(Omnibus): 0.845 Jarque-Bera (JB): 0.494
Skew: -0.063 Prob(JB): 0.781
Kurtosis: 2.293 Cond. No. 499.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.663
Model: OLS Adj. R-squared: 0.630
Method: Least Squares F-statistic: 19.71
Date: Sun, 09 Nov 2025 Prob (F-statistic): 1.87e-05
Time: 19:46:54 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 154.8719 109.131 1.419 0.171 -72.772 382.516
C(dose)[T.1] 52.6314 8.622 6.104 0.000 34.645 70.618
expression -13.4007 14.506 -0.924 0.367 -43.660 16.859
Omnibus: 0.269 Durbin-Watson: 1.924
Prob(Omnibus): 0.874 Jarque-Bera (JB): 0.453
Skew: 0.116 Prob(JB): 0.797
Kurtosis: 2.353 Cond. No. 194.

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: Sun, 09 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 19:46:54 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: 0.7929
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.383
Time: 19:46:54 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 238.7753 178.767 1.336 0.196 -132.992 610.542
expression -21.2455 23.859 -0.890 0.383 -70.864 28.373
Omnibus: 1.891 Durbin-Watson: 2.402
Prob(Omnibus): 0.388 Jarque-Bera (JB): 1.415
Skew: 0.410 Prob(JB): 0.493
Kurtosis: 2.103 Cond. No. 192.

CP101

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

F-statistic p-value df difference
0.328 0.577 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.518
Model: OLS Adj. R-squared: 0.386
Method: Least Squares F-statistic: 3.934
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0393
Time: 19:46:54 Log-Likelihood: -69.833
No. Observations: 15 AIC: 147.7
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -122.2196 218.957 -0.558 0.588 -604.142 359.702
C(dose)[T.1] 878.9090 741.144 1.186 0.261 -752.338 2510.156
expression 23.8895 27.545 0.867 0.404 -36.737 84.516
expression:C(dose)[T.1] -108.9724 98.060 -1.111 0.290 -324.800 106.855
Omnibus: 2.890 Durbin-Watson: 1.019
Prob(Omnibus): 0.236 Jarque-Bera (JB): 1.697
Skew: -0.823 Prob(JB): 0.428
Kurtosis: 2.906 Cond. No. 874.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.463
Model: OLS Adj. R-squared: 0.374
Method: Least Squares F-statistic: 5.182
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0239
Time: 19:46:54 Log-Likelihood: -70.631
No. Observations: 15 AIC: 147.3
Df Residuals: 12 BIC: 149.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -53.9591 212.213 -0.254 0.804 -516.331 408.412
C(dose)[T.1] 55.5532 19.086 2.911 0.013 13.968 97.139
expression 15.2909 26.694 0.573 0.577 -42.870 73.451
Omnibus: 2.577 Durbin-Watson: 0.971
Prob(Omnibus): 0.276 Jarque-Bera (JB): 1.734
Skew: -0.815 Prob(JB): 0.420
Kurtosis: 2.657 Cond. No. 216.

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: Sun, 09 Nov 2025 Prob (F-statistic): 0.00629
Time: 19:46:55 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: 0.085
Model: OLS Adj. R-squared: 0.014
Method: Least Squares F-statistic: 1.202
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.293
Time: 19:46:55 Log-Likelihood: -74.637
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 324.2656 210.538 1.540 0.147 -130.573 779.105
expression -29.8826 27.254 -1.096 0.293 -88.761 28.996
Omnibus: 0.729 Durbin-Watson: 1.357
Prob(Omnibus): 0.694 Jarque-Bera (JB): 0.722
Skew: -0.359 Prob(JB): 0.697
Kurtosis: 2.200 Cond. No. 170.