LID SPN TRAP

1460553_at

1700025K23Rik RIKEN cDNA 1700025K23 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
0.075 0.786 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.631
Method: Least Squares F-statistic: 13.53
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.85e-05
Time: 04:50:34 Log-Likelihood: -99.957
No. Observations: 23 AIC: 207.9
Df Residuals: 19 BIC: 212.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -126.7984 167.382 -0.758 0.458 -477.132 223.535
C(dose)[T.1] 410.5576 263.969 1.555 0.136 -141.936 963.052
expression 22.3452 20.650 1.082 0.293 -20.876 65.567
expression:C(dose)[T.1] -43.8133 32.315 -1.356 0.191 -111.449 23.822
Omnibus: 0.070 Durbin-Watson: 1.997
Prob(Omnibus): 0.966 Jarque-Bera (JB): 0.295
Skew: -0.009 Prob(JB): 0.863
Kurtosis: 2.446 Cond. No. 636.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.73e-05
Time: 04:50:34 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.1333 131.474 0.138 0.892 -256.116 292.383
C(dose)[T.1] 52.8574 8.926 5.922 0.000 34.238 71.476
expression 4.4535 16.213 0.275 0.786 -29.367 38.274
Omnibus: 0.235 Durbin-Watson: 1.945
Prob(Omnibus): 0.889 Jarque-Bera (JB): 0.429
Skew: 0.082 Prob(JB): 0.807
Kurtosis: 2.351 Cond. No. 249.

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: 04:50:34 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: 0.037
Model: OLS Adj. R-squared: -0.008
Method: Least Squares F-statistic: 0.8148
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.377
Time: 04:50:34 Log-Likelihood: -112.67
No. Observations: 23 AIC: 229.3
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -109.7389 210.010 -0.523 0.607 -546.478 327.000
expression 23.2405 25.747 0.903 0.377 -30.304 76.785
Omnibus: 2.385 Durbin-Watson: 2.593
Prob(Omnibus): 0.303 Jarque-Bera (JB): 1.697
Skew: 0.473 Prob(JB): 0.428
Kurtosis: 2.064 Cond. No. 246.

CP101

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

F-statistic p-value df difference
0.027 0.871 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.467
Model: OLS Adj. R-squared: 0.322
Method: Least Squares F-statistic: 3.218
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0653
Time: 04:50:34 Log-Likelihood: -70.575
No. Observations: 15 AIC: 149.2
Df Residuals: 11 BIC: 152.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 297.2827 430.748 0.690 0.504 -650.787 1245.353
C(dose)[T.1] -324.3943 626.592 -0.518 0.615 -1703.514 1054.725
expression -30.7693 57.640 -0.534 0.604 -157.634 96.096
expression:C(dose)[T.1] 49.4236 82.511 0.599 0.561 -132.182 231.030
Omnibus: 2.158 Durbin-Watson: 0.809
Prob(Omnibus): 0.340 Jarque-Bera (JB): 1.658
Skew: -0.743 Prob(JB): 0.437
Kurtosis: 2.332 Cond. No. 794.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.910
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0277
Time: 04:50:34 Log-Likelihood: -70.816
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 117.1084 299.976 0.390 0.703 -536.484 770.701
C(dose)[T.1] 50.7594 18.333 2.769 0.017 10.815 90.704
expression -6.6504 40.127 -0.166 0.871 -94.079 80.778
Omnibus: 3.203 Durbin-Watson: 0.812
Prob(Omnibus): 0.202 Jarque-Bera (JB): 2.073
Skew: -0.904 Prob(JB): 0.355
Kurtosis: 2.775 Cond. No. 297.

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: 04:50:34 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: 0.099
Model: OLS Adj. R-squared: 0.029
Method: Least Squares F-statistic: 1.424
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.254
Time: 04:50:34 Log-Likelihood: -74.521
No. Observations: 15 AIC: 153.0
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -289.9054 321.613 -0.901 0.384 -984.708 404.897
expression 50.4993 42.323 1.193 0.254 -40.934 141.933
Omnibus: 0.670 Durbin-Watson: 1.458
Prob(Omnibus): 0.715 Jarque-Bera (JB): 0.605
Skew: 0.044 Prob(JB): 0.739
Kurtosis: 2.020 Cond. No. 258.