LID SPN TRAP

1460688_s_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
0.442 0.514 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.659
Model: OLS Adj. R-squared: 0.606
Method: Least Squares F-statistic: 12.26
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000108
Time: 04:00:53 Log-Likelihood: -100.72
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 168.1391 158.195 1.063 0.301 -162.967 499.245
C(dose)[T.1] -28.9336 206.634 -0.140 0.890 -461.424 403.556
expression -16.6977 23.168 -0.721 0.480 -65.188 31.793
expression:C(dose)[T.1] 11.9572 30.529 0.392 0.700 -51.940 75.855
Omnibus: 0.858 Durbin-Watson: 1.944
Prob(Omnibus): 0.651 Jarque-Bera (JB): 0.726
Skew: 0.012 Prob(JB): 0.696
Kurtosis: 2.130 Cond. No. 432.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.12
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.28e-05
Time: 04:00:53 Log-Likelihood: -100.81
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 121.1545 100.921 1.200 0.244 -89.363 331.672
C(dose)[T.1] 51.9193 8.933 5.812 0.000 33.285 70.553
expression -9.8116 14.765 -0.665 0.514 -40.610 20.987
Omnibus: 0.893 Durbin-Watson: 1.873
Prob(Omnibus): 0.640 Jarque-Bera (JB): 0.757
Skew: 0.102 Prob(JB): 0.685
Kurtosis: 2.135 Cond. No. 161.

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: 04:00:53 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: 0.077
Model: OLS Adj. R-squared: 0.033
Method: Least Squares F-statistic: 1.745
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.201
Time: 04:00:53 Log-Likelihood: -112.19
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 284.4125 155.121 1.833 0.081 -38.178 607.003
expression -30.3070 22.944 -1.321 0.201 -78.022 17.408
Omnibus: 1.268 Durbin-Watson: 2.311
Prob(Omnibus): 0.530 Jarque-Bera (JB): 1.153
Skew: 0.416 Prob(JB): 0.562
Kurtosis: 2.284 Cond. No. 155.

CP101

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

F-statistic p-value df difference
0.114 0.742 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.557
Method: Least Squares F-statistic: 6.868
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00714
Time: 04:00:53 Log-Likelihood: -67.385
No. Observations: 15 AIC: 142.8
Df Residuals: 11 BIC: 145.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -102.0763 175.402 -0.582 0.572 -488.134 283.981
C(dose)[T.1] 862.9313 326.371 2.644 0.023 144.593 1581.270
expression 23.8177 24.610 0.968 0.354 -30.348 77.984
expression:C(dose)[T.1] -117.3330 46.906 -2.501 0.029 -220.572 -14.094
Omnibus: 1.739 Durbin-Watson: 1.062
Prob(Omnibus): 0.419 Jarque-Bera (JB): 1.323
Skew: -0.559 Prob(JB): 0.516
Kurtosis: 2.070 Cond. No. 433.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.454
Model: OLS Adj. R-squared: 0.363
Method: Least Squares F-statistic: 4.988
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0265
Time: 04:00:53 Log-Likelihood: -70.762
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 127.7874 179.168 0.713 0.489 -262.587 518.162
C(dose)[T.1] 47.2650 16.678 2.834 0.015 10.928 83.602
expression -8.4812 25.124 -0.338 0.742 -63.222 46.260
Omnibus: 2.225 Durbin-Watson: 0.784
Prob(Omnibus): 0.329 Jarque-Bera (JB): 1.701
Skew: -0.756 Prob(JB): 0.427
Kurtosis: 2.341 Cond. No. 164.

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: 04:00:53 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: 0.088
Model: OLS Adj. R-squared: 0.018
Method: Least Squares F-statistic: 1.262
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.282
Time: 04:00:53 Log-Likelihood: -74.605
No. Observations: 15 AIC: 153.2
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 323.8732 205.155 1.579 0.138 -119.337 767.083
expression -32.9087 29.295 -1.123 0.282 -96.196 30.379
Omnibus: 0.500 Durbin-Watson: 1.556
Prob(Omnibus): 0.779 Jarque-Bera (JB): 0.559
Skew: -0.331 Prob(JB): 0.756
Kurtosis: 2.325 Cond. No. 151.