LID SPN TRAP

1429155_at

4933411K20Rik RIKEN cDNA 4933411K20 gene

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
1.362 0.257 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.700
Model: OLS Adj. R-squared: 0.652
Method: Least Squares F-statistic: 14.77
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.34e-05
Time: 03:43:23 Log-Likelihood: -99.265
No. Observations: 23 AIC: 206.5
Df Residuals: 19 BIC: 211.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 86.8790 159.463 0.545 0.592 -246.880 420.638
C(dose)[T.1] 427.2751 278.495 1.534 0.141 -155.622 1010.172
expression -3.7665 18.372 -0.205 0.840 -42.219 34.686
expression:C(dose)[T.1] -42.9369 32.013 -1.341 0.196 -109.940 24.066
Omnibus: 0.020 Durbin-Watson: 1.778
Prob(Omnibus): 0.990 Jarque-Bera (JB): 0.129
Skew: 0.050 Prob(JB): 0.938
Kurtosis: 2.647 Cond. No. 712.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.639
Method: Least Squares F-statistic: 20.44
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.47e-05
Time: 03:43:23 Log-Likelihood: -100.30
No. Observations: 23 AIC: 206.6
Df Residuals: 20 BIC: 210.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 209.5418 133.214 1.573 0.131 -68.339 487.422
C(dose)[T.1] 53.9118 8.500 6.343 0.000 36.181 71.642
expression -17.9077 15.343 -1.167 0.257 -49.912 14.097
Omnibus: 0.862 Durbin-Watson: 1.783
Prob(Omnibus): 0.650 Jarque-Bera (JB): 0.318
Skew: 0.287 Prob(JB): 0.853
Kurtosis: 3.056 Cond. No. 277.

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: 03:43:23 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: 0.011
Model: OLS Adj. R-squared: -0.037
Method: Least Squares F-statistic: 0.2238
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.641
Time: 03:43:23 Log-Likelihood: -112.98
No. Observations: 23 AIC: 230.0
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 186.3453 225.518 0.826 0.418 -282.645 655.336
expression -12.2710 25.940 -0.473 0.641 -66.216 41.674
Omnibus: 4.395 Durbin-Watson: 2.488
Prob(Omnibus): 0.111 Jarque-Bera (JB): 1.714
Skew: 0.257 Prob(JB): 0.424
Kurtosis: 1.765 Cond. No. 277.

CP101

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

F-statistic p-value df difference
0.036 0.852 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.313
Method: Least Squares F-statistic: 3.125
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0700
Time: 03:43:23 Log-Likelihood: -70.677
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -65.0162 288.055 -0.226 0.826 -699.020 568.988
C(dose)[T.1] 215.7989 379.288 0.569 0.581 -619.009 1050.607
expression 15.6350 33.976 0.460 0.654 -59.145 90.415
expression:C(dose)[T.1] -19.4880 43.903 -0.444 0.666 -116.117 77.141
Omnibus: 3.055 Durbin-Watson: 0.834
Prob(Omnibus): 0.217 Jarque-Bera (JB): 2.008
Skew: -0.886 Prob(JB): 0.366
Kurtosis: 2.732 Cond. No. 572.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.359
Method: Least Squares F-statistic: 4.918
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0275
Time: 03:43:23 Log-Likelihood: -70.810
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.8515 176.448 0.192 0.851 -350.595 418.298
C(dose)[T.1] 47.6341 17.723 2.688 0.020 9.019 86.249
expression 3.9637 20.785 0.191 0.852 -41.324 49.251
Omnibus: 2.701 Durbin-Watson: 0.775
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.904
Skew: -0.846 Prob(JB): 0.386
Kurtosis: 2.569 Cond. No. 199.

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: 03:43:23 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: 0.120
Model: OLS Adj. R-squared: 0.052
Method: Least Squares F-statistic: 1.766
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.207
Time: 03:43:23 Log-Likelihood: -74.345
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -164.9294 194.808 -0.847 0.413 -585.786 255.927
expression 29.7878 22.413 1.329 0.207 -18.633 78.208
Omnibus: 0.332 Durbin-Watson: 1.357
Prob(Omnibus): 0.847 Jarque-Bera (JB): 0.466
Skew: 0.244 Prob(JB): 0.792
Kurtosis: 2.288 Cond. No. 180.