LID SPN TRAP

1449129_a_at

Kcnip3 Kv channel interacting protein 3, calsenilin

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
5.733 0.027 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.728
Model: OLS Adj. R-squared: 0.685
Method: Least Squares F-statistic: 16.94
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.34e-05
Time: 05:00:26 Log-Likelihood: -98.137
No. Observations: 23 AIC: 204.3
Df Residuals: 19 BIC: 208.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 338.3359 213.806 1.582 0.130 -109.166 785.838
C(dose)[T.1] 96.0662 272.627 0.352 0.728 -474.549 666.681
expression -31.4341 23.646 -1.329 0.199 -80.927 18.058
expression:C(dose)[T.1] -6.6006 30.759 -0.215 0.832 -70.981 57.780
Omnibus: 0.918 Durbin-Watson: 2.239
Prob(Omnibus): 0.632 Jarque-Bera (JB): 0.903
Skew: 0.334 Prob(JB): 0.637
Kurtosis: 2.297 Cond. No. 832.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.727
Model: OLS Adj. R-squared: 0.700
Method: Least Squares F-statistic: 26.66
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.28e-06
Time: 05:00:26 Log-Likelihood: -98.164
No. Observations: 23 AIC: 202.3
Df Residuals: 20 BIC: 205.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 373.5947 133.501 2.798 0.011 95.115 652.074
C(dose)[T.1] 37.6065 10.146 3.707 0.001 16.442 58.771
expression -35.3349 14.758 -2.394 0.027 -66.119 -4.550
Omnibus: 0.831 Durbin-Watson: 2.277
Prob(Omnibus): 0.660 Jarque-Bera (JB): 0.832
Skew: 0.291 Prob(JB): 0.660
Kurtosis: 2.272 Cond. No. 310.

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: 05:00:26 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: 0.540
Model: OLS Adj. R-squared: 0.518
Method: Least Squares F-statistic: 24.64
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.53e-05
Time: 05:00:26 Log-Likelihood: -104.18
No. Observations: 23 AIC: 212.4
Df Residuals: 21 BIC: 214.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 704.2057 125.903 5.593 0.000 442.377 966.034
expression -70.7562 14.254 -4.964 0.000 -100.400 -41.113
Omnibus: 0.639 Durbin-Watson: 2.667
Prob(Omnibus): 0.726 Jarque-Bera (JB): 0.582
Skew: 0.342 Prob(JB): 0.748
Kurtosis: 2.626 Cond. No. 230.

CP101

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

F-statistic p-value df difference
4.944 0.046 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.616
Model: OLS Adj. R-squared: 0.511
Method: Least Squares F-statistic: 5.881
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0120
Time: 05:00:26 Log-Likelihood: -68.123
No. Observations: 15 AIC: 144.2
Df Residuals: 11 BIC: 147.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 336.0245 252.131 1.333 0.210 -218.912 890.961
C(dose)[T.1] 190.1340 331.039 0.574 0.577 -538.477 918.745
expression -30.6175 28.718 -1.066 0.309 -93.825 32.590
expression:C(dose)[T.1] -16.0742 37.706 -0.426 0.678 -99.064 66.916
Omnibus: 1.837 Durbin-Watson: 1.379
Prob(Omnibus): 0.399 Jarque-Bera (JB): 1.419
Skew: -0.685 Prob(JB): 0.492
Kurtosis: 2.372 Cond. No. 592.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.610
Model: OLS Adj. R-squared: 0.545
Method: Least Squares F-statistic: 9.369
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00354
Time: 05:00:26 Log-Likelihood: -68.246
No. Observations: 15 AIC: 142.5
Df Residuals: 12 BIC: 144.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 417.8235 157.887 2.646 0.021 73.817 761.830
C(dose)[T.1] 49.1317 13.246 3.709 0.003 20.271 77.992
expression -39.9418 17.964 -2.223 0.046 -79.082 -0.802
Omnibus: 2.216 Durbin-Watson: 1.571
Prob(Omnibus): 0.330 Jarque-Bera (JB): 1.574
Skew: -0.617 Prob(JB): 0.455
Kurtosis: 2.002 Cond. No. 213.

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: 05:00:26 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: 0.162
Model: OLS Adj. R-squared: 0.098
Method: Least Squares F-statistic: 2.513
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.137
Time: 05:00:26 Log-Likelihood: -73.974
No. Observations: 15 AIC: 151.9
Df Residuals: 13 BIC: 153.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 445.3124 222.000 2.006 0.066 -34.290 924.915
expression -40.0883 25.286 -1.585 0.137 -94.716 14.539
Omnibus: 1.663 Durbin-Watson: 2.090
Prob(Omnibus): 0.435 Jarque-Bera (JB): 0.891
Skew: 0.107 Prob(JB): 0.640
Kurtosis: 1.825 Cond. No. 212.