LID SPN TRAP

1456799_at

E130110O22Rik RIKEN cDNA E130110O22 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
0.357 0.557 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 12.07
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000119
Time: 04:34:19 Log-Likelihood: -100.84
No. Observations: 23 AIC: 209.7
Df Residuals: 19 BIC: 214.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 41.2180 91.266 0.452 0.657 -149.804 232.240
C(dose)[T.1] 38.4537 102.861 0.374 0.713 -176.836 253.743
expression 2.5144 17.625 0.143 0.888 -34.375 39.404
expression:C(dose)[T.1] 3.6750 20.484 0.179 0.860 -39.198 46.548
Omnibus: 1.164 Durbin-Watson: 1.891
Prob(Omnibus): 0.559 Jarque-Bera (JB): 0.865
Skew: 0.131 Prob(JB): 0.649
Kurtosis: 2.087 Cond. No. 168.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.655
Model: OLS Adj. R-squared: 0.621
Method: Least Squares F-statistic: 19.00
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.37e-05
Time: 04:34:19 Log-Likelihood: -100.86
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 27.1611 45.659 0.595 0.559 -68.083 122.405
C(dose)[T.1] 56.8078 10.454 5.434 0.000 35.000 78.615
expression 5.2352 8.761 0.598 0.557 -13.039 23.510
Omnibus: 1.181 Durbin-Watson: 1.860
Prob(Omnibus): 0.554 Jarque-Bera (JB): 0.849
Skew: 0.080 Prob(JB): 0.654
Kurtosis: 2.072 Cond. No. 54.1

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: 04:34:19 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: 0.146
Model: OLS Adj. R-squared: 0.106
Method: Least Squares F-statistic: 3.596
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0718
Time: 04:34:19 Log-Likelihood: -111.29
No. Observations: 23 AIC: 226.6
Df Residuals: 21 BIC: 228.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 182.5828 54.657 3.341 0.003 68.917 296.248
expression -21.2122 11.187 -1.896 0.072 -44.476 2.052
Omnibus: 5.176 Durbin-Watson: 2.327
Prob(Omnibus): 0.075 Jarque-Bera (JB): 1.786
Skew: 0.225 Prob(JB): 0.410
Kurtosis: 1.711 Cond. No. 41.7

CP101

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

F-statistic p-value df difference
2.051 0.178 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: 0.405
Method: Least Squares F-statistic: 4.181
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0334
Time: 04:34:19 Log-Likelihood: -69.593
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 150.9906 82.400 1.832 0.094 -30.371 332.353
C(dose)[T.1] 101.1535 163.239 0.620 0.548 -258.134 460.441
expression -15.8640 15.502 -1.023 0.328 -49.984 18.256
expression:C(dose)[T.1] -8.5492 29.686 -0.288 0.779 -73.888 56.789
Omnibus: 1.851 Durbin-Watson: 0.919
Prob(Omnibus): 0.396 Jarque-Bera (JB): 0.888
Skew: -0.596 Prob(JB): 0.642
Kurtosis: 2.985 Cond. No. 148.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.529
Model: OLS Adj. R-squared: 0.451
Method: Least Squares F-statistic: 6.745
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0109
Time: 04:34:19 Log-Likelihood: -69.650
No. Observations: 15 AIC: 145.3
Df Residuals: 12 BIC: 147.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 163.2706 67.762 2.409 0.033 15.630 310.911
C(dose)[T.1] 54.3575 14.985 3.627 0.003 21.707 87.008
expression -18.1953 12.705 -1.432 0.178 -45.878 9.487
Omnibus: 1.751 Durbin-Watson: 0.988
Prob(Omnibus): 0.417 Jarque-Bera (JB): 0.857
Skew: -0.585 Prob(JB): 0.652
Kurtosis: 2.950 Cond. No. 52.9

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: 04:34:19 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: 0.013
Model: OLS Adj. R-squared: -0.063
Method: Least Squares F-statistic: 0.1719
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.685
Time: 04:34:19 Log-Likelihood: -75.202
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 132.2040 93.509 1.414 0.181 -69.809 334.217
expression -7.1119 17.156 -0.415 0.685 -44.175 29.951
Omnibus: 2.035 Durbin-Watson: 1.682
Prob(Omnibus): 0.361 Jarque-Bera (JB): 0.992
Skew: 0.157 Prob(JB): 0.609
Kurtosis: 1.780 Cond. No. 52.2