LID SPN TRAP

1450520_at

Cacng3 calcium channel, voltage-dependent, gamma subunit 3

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
0.283 0.600 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.86e-05
Time: 05:26:10 Log-Likelihood: -100.32
No. Observations: 23 AIC: 208.6
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -8.0565 413.376 -0.019 0.985 -873.262 857.149
C(dose)[T.1] 755.2011 707.883 1.067 0.299 -726.415 2236.817
expression 5.8284 38.690 0.151 0.882 -75.152 86.808
expression:C(dose)[T.1] -65.1347 65.846 -0.989 0.335 -202.953 72.684
Omnibus: 2.905 Durbin-Watson: 1.736
Prob(Omnibus): 0.234 Jarque-Bera (JB): 1.546
Skew: 0.326 Prob(JB): 0.462
Kurtosis: 1.910 Cond. No. 2.14e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.46e-05
Time: 05:26:10 Log-Likelihood: -100.90
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 232.1858 334.327 0.694 0.495 -465.208 929.579
C(dose)[T.1] 55.0302 9.271 5.936 0.000 35.692 74.369
expression -16.6598 31.290 -0.532 0.600 -81.930 48.610
Omnibus: 1.084 Durbin-Watson: 1.979
Prob(Omnibus): 0.581 Jarque-Bera (JB): 0.843
Skew: 0.140 Prob(JB): 0.656
Kurtosis: 2.105 Cond. No. 833.

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: 05:26:10 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: 0.044
Model: OLS Adj. R-squared: -0.001
Method: Least Squares F-statistic: 0.9742
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.335
Time: 05:26:10 Log-Likelihood: -112.58
No. Observations: 23 AIC: 229.2
Df Residuals: 21 BIC: 231.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -425.1904 511.598 -0.831 0.415 -1489.116 638.735
expression 47.0485 47.667 0.987 0.335 -52.081 146.178
Omnibus: 2.712 Durbin-Watson: 2.246
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.378
Skew: 0.242 Prob(JB): 0.502
Kurtosis: 1.903 Cond. No. 785.

CP101

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

F-statistic p-value df difference
6.256 0.028 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.569
Method: Least Squares F-statistic: 7.159
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00618
Time: 05:26:10 Log-Likelihood: -67.180
No. Observations: 15 AIC: 142.4
Df Residuals: 11 BIC: 145.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 262.9199 321.502 0.818 0.431 -444.701 970.541
C(dose)[T.1] 384.0175 382.746 1.003 0.337 -458.400 1226.435
expression -21.4697 35.294 -0.608 0.555 -99.150 56.211
expression:C(dose)[T.1] -36.8055 42.018 -0.876 0.400 -129.287 55.676
Omnibus: 0.637 Durbin-Watson: 1.608
Prob(Omnibus): 0.727 Jarque-Bera (JB): 0.239
Skew: 0.300 Prob(JB): 0.888
Kurtosis: 2.851 Cond. No. 800.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.638
Model: OLS Adj. R-squared: 0.577
Method: Least Squares F-statistic: 10.56
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00226
Time: 05:26:10 Log-Likelihood: -67.686
No. Observations: 15 AIC: 141.4
Df Residuals: 12 BIC: 143.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 499.3657 172.939 2.888 0.014 122.563 876.168
C(dose)[T.1] 48.9446 12.761 3.835 0.002 21.140 76.749
expression -47.4372 18.965 -2.501 0.028 -88.759 -6.115
Omnibus: 0.633 Durbin-Watson: 1.329
Prob(Omnibus): 0.729 Jarque-Bera (JB): 0.661
Skew: 0.313 Prob(JB): 0.719
Kurtosis: 2.185 Cond. No. 251.

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: 05:26:10 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: 0.194
Model: OLS Adj. R-squared: 0.131
Method: Least Squares F-statistic: 3.119
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.101
Time: 05:26:10 Log-Likelihood: -73.687
No. Observations: 15 AIC: 151.4
Df Residuals: 13 BIC: 152.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 530.6939 247.615 2.143 0.052 -4.246 1065.634
expression -48.0112 27.184 -1.766 0.101 -106.739 10.717
Omnibus: 0.065 Durbin-Watson: 1.958
Prob(Omnibus): 0.968 Jarque-Bera (JB): 0.163
Skew: 0.113 Prob(JB): 0.922
Kurtosis: 2.543 Cond. No. 250.