LID SPN TRAP

1460225_at

Mrp63 mitochondrial ribosomal protein 63

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
0.022 0.883 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: 0.630
Method: Least Squares F-statistic: 13.49
Date: Thu, 03 Apr 2025 Prob (F-statistic): 5.96e-05
Time: 22:58:20 Log-Likelihood: -99.981
No. Observations: 23 AIC: 208.0
Df Residuals: 19 BIC: 212.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 131.8382 82.341 1.601 0.126 -40.503 304.180
C(dose)[T.1] -132.0350 136.594 -0.967 0.346 -417.930 153.860
expression -11.2388 11.890 -0.945 0.356 -36.124 13.647
expression:C(dose)[T.1] 26.7026 19.628 1.360 0.190 -14.379 67.784
Omnibus: 1.457 Durbin-Watson: 2.012
Prob(Omnibus): 0.483 Jarque-Bera (JB): 1.210
Skew: 0.383 Prob(JB): 0.546
Kurtosis: 2.178 Cond. No. 277.

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: 18.53
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.80e-05
Time: 22:58:21 Log-Likelihood: -101.05
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 64.1584 66.994 0.958 0.350 -75.589 203.906
C(dose)[T.1] 53.4236 8.784 6.082 0.000 35.100 71.747
expression -1.4405 9.659 -0.149 0.883 -21.589 18.708
Omnibus: 0.210 Durbin-Watson: 1.864
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.413
Skew: 0.033 Prob(JB): 0.813
Kurtosis: 2.347 Cond. No. 109.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:58:21 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: 0.001
Model: OLS Adj. R-squared: -0.046
Method: Least Squares F-statistic: 0.02360
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.879
Time: 22:58:21 Log-Likelihood: -113.09
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 62.8007 110.362 0.569 0.575 -166.709 292.311
expression 2.4389 15.877 0.154 0.879 -30.580 35.458
Omnibus: 2.880 Durbin-Watson: 2.513
Prob(Omnibus): 0.237 Jarque-Bera (JB): 1.450
Skew: 0.268 Prob(JB): 0.484
Kurtosis: 1.893 Cond. No. 109.

CP101

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

F-statistic p-value df difference
2.085 0.174 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.554
Model: OLS Adj. R-squared: 0.432
Method: Least Squares F-statistic: 4.550
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0263
Time: 22:58:21 Log-Likelihood: -69.248
No. Observations: 15 AIC: 146.5
Df Residuals: 11 BIC: 149.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 211.8635 139.447 1.519 0.157 -95.057 518.784
C(dose)[T.1] 352.7207 390.618 0.903 0.386 -507.024 1212.466
expression -20.6843 19.910 -1.039 0.321 -64.506 23.137
expression:C(dose)[T.1] -41.0249 54.046 -0.759 0.464 -159.980 77.930
Omnibus: 0.938 Durbin-Watson: 0.898
Prob(Omnibus): 0.626 Jarque-Bera (JB): 0.734
Skew: -0.191 Prob(JB): 0.693
Kurtosis: 1.986 Cond. No. 455.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.530
Model: OLS Adj. R-squared: 0.452
Method: Least Squares F-statistic: 6.776
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0107
Time: 22:58:21 Log-Likelihood: -69.631
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 250.7403 127.390 1.968 0.073 -26.819 528.299
C(dose)[T.1] 56.4518 15.372 3.672 0.003 22.958 89.945
expression -26.2519 18.180 -1.444 0.174 -65.863 13.359
Omnibus: 1.163 Durbin-Watson: 0.776
Prob(Omnibus): 0.559 Jarque-Bera (JB): 0.995
Skew: -0.476 Prob(JB): 0.608
Kurtosis: 2.172 Cond. No. 128.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:58:21 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: 0.003
Model: OLS Adj. R-squared: -0.074
Method: Least Squares F-statistic: 0.03391
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.857
Time: 22:58:21 Log-Likelihood: -75.281
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 125.2546 171.830 0.729 0.479 -245.961 496.470
expression -4.4302 24.057 -0.184 0.857 -56.402 47.541
Omnibus: 1.182 Durbin-Watson: 1.610
Prob(Omnibus): 0.554 Jarque-Bera (JB): 0.778
Skew: 0.123 Prob(JB): 0.678
Kurtosis: 1.912 Cond. No. 123.