LID SPN TRAP

1450431_a_at

Nedd4 neural precursor cell expressed, developmentally down-regulated 4

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
3.237 0.087 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.771
Model: OLS Adj. R-squared: 0.735
Method: Least Squares F-statistic: 21.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.67e-06
Time: 04:00:04 Log-Likelihood: -96.156
No. Observations: 23 AIC: 200.3
Df Residuals: 19 BIC: 204.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 179.8080 198.617 0.905 0.377 -235.903 595.519
C(dose)[T.1] 1133.6816 435.022 2.606 0.017 223.171 2044.192
expression -11.8689 18.763 -0.633 0.535 -51.140 27.402
expression:C(dose)[T.1] -98.9455 40.208 -2.461 0.024 -183.101 -14.790
Omnibus: 0.243 Durbin-Watson: 1.877
Prob(Omnibus): 0.885 Jarque-Bera (JB): 0.427
Skew: 0.153 Prob(JB): 0.808
Kurtosis: 2.407 Cond. No. 1.52e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.698
Model: OLS Adj. R-squared: 0.668
Method: Least Squares F-statistic: 23.11
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.32e-06
Time: 04:00:04 Log-Likelihood: -99.338
No. Observations: 23 AIC: 204.7
Df Residuals: 20 BIC: 208.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 407.8197 196.637 2.074 0.051 -2.358 817.997
C(dose)[T.1] 63.3717 9.865 6.424 0.000 42.795 83.949
expression -33.4155 18.574 -1.799 0.087 -72.160 5.329
Omnibus: 0.949 Durbin-Watson: 1.819
Prob(Omnibus): 0.622 Jarque-Bera (JB): 0.913
Skew: -0.322 Prob(JB): 0.633
Kurtosis: 2.266 Cond. No. 524.

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: 04:00:05 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: 0.075
Model: OLS Adj. R-squared: 0.031
Method: Least Squares F-statistic: 1.694
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.207
Time: 04:00:05 Log-Likelihood: -112.21
No. Observations: 23 AIC: 228.4
Df Residuals: 21 BIC: 230.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -285.5472 280.759 -1.017 0.321 -869.418 298.323
expression 34.0545 26.168 1.301 0.207 -20.365 88.473
Omnibus: 2.891 Durbin-Watson: 2.462
Prob(Omnibus): 0.236 Jarque-Bera (JB): 1.596
Skew: 0.355 Prob(JB): 0.450
Kurtosis: 1.923 Cond. No. 438.

CP101

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

F-statistic p-value df difference
1.083 0.319 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.495
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 3.587
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0500
Time: 04:00:05 Log-Likelihood: -70.183
No. Observations: 15 AIC: 148.4
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 387.8492 389.023 0.997 0.340 -468.385 1244.083
C(dose)[T.1] 29.8280 661.663 0.045 0.965 -1426.483 1486.139
expression -30.6033 37.139 -0.824 0.427 -112.346 51.140
expression:C(dose)[T.1] 3.2879 61.128 0.054 0.958 -131.254 137.830
Omnibus: 3.440 Durbin-Watson: 1.098
Prob(Omnibus): 0.179 Jarque-Bera (JB): 2.240
Skew: -0.941 Prob(JB): 0.326
Kurtosis: 2.786 Cond. No. 1.15e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.494
Model: OLS Adj. R-squared: 0.410
Method: Least Squares F-statistic: 5.867
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0167
Time: 04:00:05 Log-Likelihood: -70.185
No. Observations: 15 AIC: 146.4
Df Residuals: 12 BIC: 148.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 375.1420 295.950 1.268 0.229 -269.677 1019.961
C(dose)[T.1] 65.3957 21.671 3.018 0.011 18.178 112.613
expression -29.3896 28.247 -1.040 0.319 -90.933 32.154
Omnibus: 3.476 Durbin-Watson: 1.081
Prob(Omnibus): 0.176 Jarque-Bera (JB): 2.250
Skew: -0.943 Prob(JB): 0.325
Kurtosis: 2.802 Cond. No. 429.

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: 04:00:05 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: 0.111
Model: OLS Adj. R-squared: 0.042
Method: Least Squares F-statistic: 1.618
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.226
Time: 04:00:05 Log-Likelihood: -74.420
No. Observations: 15 AIC: 152.8
Df Residuals: 13 BIC: 154.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -249.1494 269.654 -0.924 0.372 -831.702 333.404
expression 31.8481 25.035 1.272 0.226 -22.238 85.934
Omnibus: 0.074 Durbin-Watson: 1.042
Prob(Omnibus): 0.964 Jarque-Bera (JB): 0.300
Skew: -0.055 Prob(JB): 0.861
Kurtosis: 2.316 Cond. No. 306.