LID SPN TRAP

1457318_at

A330008L17Rik RIKEN cDNA A330008L17 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
0.426 0.522 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.16
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.98e-05
Time: 04:00:52 Log-Likelihood: -100.18
No. Observations: 23 AIC: 208.4
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -8.5025 161.722 -0.053 0.959 -346.991 329.985
C(dose)[T.1] 266.1330 206.426 1.289 0.213 -165.922 698.188
expression 9.6300 24.817 0.388 0.702 -42.313 61.573
expression:C(dose)[T.1] -33.7345 32.240 -1.046 0.309 -101.213 33.744
Omnibus: 4.426 Durbin-Watson: 2.064
Prob(Omnibus): 0.109 Jarque-Bera (JB): 1.566
Skew: 0.120 Prob(JB): 0.457
Kurtosis: 1.745 Cond. No. 422.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.10
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.30e-05
Time: 04:00:52 Log-Likelihood: -100.82
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 121.6689 103.578 1.175 0.254 -94.390 337.728
C(dose)[T.1] 50.3787 9.791 5.145 0.000 29.954 70.803
expression -10.3594 15.879 -0.652 0.522 -43.482 22.763
Omnibus: 1.096 Durbin-Watson: 1.908
Prob(Omnibus): 0.578 Jarque-Bera (JB): 0.919
Skew: 0.244 Prob(JB): 0.632
Kurtosis: 2.151 Cond. No. 157.

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: 04:00:52 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: 0.202
Model: OLS Adj. R-squared: 0.163
Method: Least Squares F-statistic: 5.300
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0317
Time: 04:00:52 Log-Likelihood: -110.52
No. Observations: 23 AIC: 225.0
Df Residuals: 21 BIC: 227.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 386.9966 133.630 2.896 0.009 109.098 664.895
expression -48.1974 20.936 -2.302 0.032 -91.735 -4.659
Omnibus: 3.134 Durbin-Watson: 2.112
Prob(Omnibus): 0.209 Jarque-Bera (JB): 1.436
Skew: 0.218 Prob(JB): 0.488
Kurtosis: 1.856 Cond. No. 136.

CP101

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

F-statistic p-value df difference
1.849 0.199 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.533
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 4.192
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0331
Time: 04:00:52 Log-Likelihood: -69.583
No. Observations: 15 AIC: 147.2
Df Residuals: 11 BIC: 150.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -101.1490 119.902 -0.844 0.417 -365.051 162.753
C(dose)[T.1] 233.3184 390.444 0.598 0.562 -626.043 1092.680
expression 30.3144 21.470 1.412 0.186 -16.940 77.569
expression:C(dose)[T.1] -32.8559 64.411 -0.510 0.620 -174.623 108.911
Omnibus: 2.975 Durbin-Watson: 1.425
Prob(Omnibus): 0.226 Jarque-Bera (JB): 1.699
Skew: -0.824 Prob(JB): 0.428
Kurtosis: 2.959 Cond. No. 372.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.522
Model: OLS Adj. R-squared: 0.443
Method: Least Squares F-statistic: 6.562
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0119
Time: 04:00:52 Log-Likelihood: -69.758
No. Observations: 15 AIC: 145.5
Df Residuals: 12 BIC: 147.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -80.8490 109.563 -0.738 0.475 -319.566 157.868
C(dose)[T.1] 34.3850 18.256 1.883 0.084 -5.392 74.162
expression 26.6640 19.608 1.360 0.199 -16.058 69.386
Omnibus: 1.879 Durbin-Watson: 1.489
Prob(Omnibus): 0.391 Jarque-Bera (JB): 1.144
Skew: -0.665 Prob(JB): 0.564
Kurtosis: 2.756 Cond. No. 91.5

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: 0.381
Model: OLS Adj. R-squared: 0.334
Method: Least Squares F-statistic: 8.008
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0142
Time: 04:00:53 Log-Likelihood: -71.700
No. Observations: 15 AIC: 147.4
Df Residuals: 13 BIC: 148.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -191.5647 101.112 -1.895 0.081 -410.004 26.875
expression 48.6974 17.209 2.830 0.014 11.520 85.875
Omnibus: 0.442 Durbin-Watson: 2.334
Prob(Omnibus): 0.802 Jarque-Bera (JB): 0.526
Skew: 0.121 Prob(JB): 0.769
Kurtosis: 2.115 Cond. No. 76.5