LID SPN TRAP

1441491_at

A330068G13Rik RIKEN cDNA A330068G13 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
0.007 0.933 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.72
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000142
Time: 04:03:56 Log-Likelihood: -101.06
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 51.5660 46.735 1.103 0.284 -46.251 149.383
C(dose)[T.1] 51.1790 88.123 0.581 0.568 -133.265 235.623
expression 0.4744 8.317 0.057 0.955 -16.932 17.881
expression:C(dose)[T.1] 0.3945 15.832 0.025 0.980 -32.743 33.532
Omnibus: 0.262 Durbin-Watson: 1.887
Prob(Omnibus): 0.877 Jarque-Bera (JB): 0.447
Skew: 0.035 Prob(JB): 0.800
Kurtosis: 2.320 Cond. No. 134.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.50
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 04:03:56 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 50.9597 38.892 1.310 0.205 -30.168 132.087
C(dose)[T.1] 53.3633 8.774 6.082 0.000 35.062 71.665
expression 0.5833 6.898 0.085 0.933 -13.805 14.972
Omnibus: 0.279 Durbin-Watson: 1.887
Prob(Omnibus): 0.870 Jarque-Bera (JB): 0.458
Skew: 0.039 Prob(JB): 0.795
Kurtosis: 2.313 Cond. No. 51.3

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: 04:03:56 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.006269
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.938
Time: 04:03:56 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 84.7058 63.416 1.336 0.196 -47.174 216.586
expression -0.8991 11.356 -0.079 0.938 -24.516 22.717
Omnibus: 3.316 Durbin-Watson: 2.488
Prob(Omnibus): 0.190 Jarque-Bera (JB): 1.581
Skew: 0.295 Prob(JB): 0.454
Kurtosis: 1.859 Cond. No. 50.6

CP101

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

F-statistic p-value df difference
3.798 0.075 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.583
Model: OLS Adj. R-squared: 0.469
Method: Least Squares F-statistic: 5.128
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0185
Time: 04:03:56 Log-Likelihood: -68.739
No. Observations: 15 AIC: 145.5
Df Residuals: 11 BIC: 148.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 235.8458 109.055 2.163 0.053 -4.182 475.874
C(dose)[T.1] 15.2464 167.034 0.091 0.929 -352.394 382.887
expression -28.5268 18.387 -1.551 0.149 -68.996 11.943
expression:C(dose)[T.1] 6.0655 27.965 0.217 0.832 -55.486 67.617
Omnibus: 5.846 Durbin-Watson: 1.247
Prob(Omnibus): 0.054 Jarque-Bera (JB): 2.825
Skew: -0.878 Prob(JB): 0.243
Kurtosis: 4.199 Cond. No. 185.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.581
Model: OLS Adj. R-squared: 0.512
Method: Least Squares F-statistic: 8.330
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00539
Time: 04:03:56 Log-Likelihood: -68.771
No. Observations: 15 AIC: 143.5
Df Residuals: 12 BIC: 145.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 220.3655 79.113 2.785 0.016 47.992 392.739
C(dose)[T.1] 51.3410 13.762 3.731 0.003 21.356 81.326
expression -25.9047 13.292 -1.949 0.075 -54.866 3.057
Omnibus: 6.068 Durbin-Watson: 1.198
Prob(Omnibus): 0.048 Jarque-Bera (JB): 2.972
Skew: -0.853 Prob(JB): 0.226
Kurtosis: 4.358 Cond. No. 71.2

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: 04:03:56 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: 0.096
Model: OLS Adj. R-squared: 0.026
Method: Least Squares F-statistic: 1.375
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.262
Time: 04:03:57 Log-Likelihood: -74.546
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 224.1620 111.697 2.007 0.066 -17.145 465.469
expression -21.9394 18.709 -1.173 0.262 -62.357 18.478
Omnibus: 1.621 Durbin-Watson: 2.138
Prob(Omnibus): 0.445 Jarque-Bera (JB): 1.007
Skew: 0.307 Prob(JB): 0.604
Kurtosis: 1.889 Cond. No. 70.9