LID SPN TRAP

1421913_at

Mrpl19 mitochondrial ribosomal protein L19

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
0.035 0.853 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000129
Time: 03:38:18 Log-Likelihood: -100.94
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 111.8927 133.997 0.835 0.414 -168.566 392.351
C(dose)[T.1] -21.6375 180.180 -0.120 0.906 -398.758 355.483
expression -7.4757 17.347 -0.431 0.671 -43.784 28.832
expression:C(dose)[T.1] 9.7455 23.457 0.415 0.682 -39.351 58.842
Omnibus: 0.296 Durbin-Watson: 1.798
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.469
Skew: 0.039 Prob(JB): 0.791
Kurtosis: 2.305 Cond. No. 417.

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: 18.54
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.78e-05
Time: 03:38:18 Log-Likelihood: -101.04
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 70.7676 88.426 0.800 0.433 -113.686 255.221
C(dose)[T.1] 53.1257 8.834 6.014 0.000 34.698 71.554
expression -2.1460 11.433 -0.188 0.853 -25.995 21.702
Omnibus: 0.397 Durbin-Watson: 1.856
Prob(Omnibus): 0.820 Jarque-Bera (JB): 0.529
Skew: 0.074 Prob(JB): 0.768
Kurtosis: 2.272 Cond. No. 158.

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: 03:38:18 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: 0.016
Model: OLS Adj. R-squared: -0.031
Method: Least Squares F-statistic: 0.3463
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.563
Time: 03:38:18 Log-Likelihood: -112.92
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 163.4068 142.399 1.148 0.264 -132.727 459.541
expression -10.9125 18.544 -0.588 0.563 -49.478 27.653
Omnibus: 2.732 Durbin-Watson: 2.304
Prob(Omnibus): 0.255 Jarque-Bera (JB): 1.534
Skew: 0.342 Prob(JB): 0.464
Kurtosis: 1.935 Cond. No. 155.

CP101

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

F-statistic p-value df difference
1.740 0.212 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.519
Model: OLS Adj. R-squared: 0.387
Method: Least Squares F-statistic: 3.950
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0389
Time: 03:38:18 Log-Likelihood: -69.818
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 201.5710 169.180 1.191 0.259 -170.792 573.934
C(dose)[T.1] 51.6266 219.297 0.235 0.818 -431.044 534.297
expression -18.8402 23.709 -0.795 0.444 -71.023 33.343
expression:C(dose)[T.1] 0.2897 30.320 0.010 0.993 -66.443 67.023
Omnibus: 0.145 Durbin-Watson: 0.979
Prob(Omnibus): 0.930 Jarque-Bera (JB): 0.361
Skew: 0.030 Prob(JB): 0.835
Kurtosis: 2.243 Cond. No. 296.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.519
Model: OLS Adj. R-squared: 0.438
Method: Least Squares F-statistic: 6.463
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0125
Time: 03:38:18 Log-Likelihood: -69.818
No. Observations: 15 AIC: 145.6
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 200.3098 101.313 1.977 0.071 -20.433 421.053
C(dose)[T.1] 53.7166 15.103 3.557 0.004 20.809 86.624
expression -18.6630 14.149 -1.319 0.212 -49.491 12.165
Omnibus: 0.144 Durbin-Watson: 0.981
Prob(Omnibus): 0.930 Jarque-Bera (JB): 0.360
Skew: 0.032 Prob(JB): 0.835
Kurtosis: 2.244 Cond. No. 103.

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: 03:38:18 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1458
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.709
Time: 03:38:18 Log-Likelihood: -75.216
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 146.1865 137.925 1.060 0.308 -151.783 444.156
expression -7.2449 18.975 -0.382 0.709 -48.238 33.748
Omnibus: 0.844 Durbin-Watson: 1.632
Prob(Omnibus): 0.656 Jarque-Bera (JB): 0.702
Skew: 0.191 Prob(JB): 0.704
Kurtosis: 2.011 Cond. No. 101.