LID SPN TRAP

1426675_at

Tomm70a translocase of outer mitochondrial membrane 70 homolog A (yeast)

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
0.001 0.970 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: 11.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000141
Time: 03:47:52 Log-Likelihood: -101.05
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 123.0927 616.546 0.200 0.844 -1167.352 1413.538
C(dose)[T.1] -112.7444 1134.919 -0.099 0.922 -2488.157 2262.668
expression -5.7458 51.425 -0.112 0.912 -113.379 101.888
expression:C(dose)[T.1] 13.6881 93.308 0.147 0.885 -181.607 208.984
Omnibus: 0.381 Durbin-Watson: 1.858
Prob(Omnibus): 0.826 Jarque-Bera (JB): 0.520
Skew: 0.071 Prob(JB): 0.771
Kurtosis: 2.277 Cond. No. 3.69e+03

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): 2.83e-05
Time: 03:47:52 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 73.2477 501.726 0.146 0.885 -973.334 1119.830
C(dose)[T.1] 53.7331 13.630 3.942 0.001 25.302 82.164
expression -1.5881 41.847 -0.038 0.970 -88.879 85.703
Omnibus: 0.302 Durbin-Watson: 1.876
Prob(Omnibus): 0.860 Jarque-Bera (JB): 0.473
Skew: 0.053 Prob(JB): 0.789
Kurtosis: 2.306 Cond. No. 1.40e+03

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: 03:47:52 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: 0.376
Model: OLS Adj. R-squared: 0.347
Method: Least Squares F-statistic: 12.67
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00185
Time: 03:47:52 Log-Likelihood: -107.67
No. Observations: 23 AIC: 219.3
Df Residuals: 21 BIC: 221.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1430.1895 424.162 -3.372 0.003 -2312.283 -548.096
expression 124.7043 35.029 3.560 0.002 51.858 197.550
Omnibus: 2.710 Durbin-Watson: 2.784
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.295
Skew: 0.165 Prob(JB): 0.523
Kurtosis: 1.886 Cond. No. 907.

CP101

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

F-statistic p-value df difference
0.441 0.519 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.585
Model: OLS Adj. R-squared: 0.472
Method: Least Squares F-statistic: 5.164
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0181
Time: 03:47:52 Log-Likelihood: -68.708
No. Observations: 15 AIC: 145.4
Df Residuals: 11 BIC: 148.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 847.0340 894.972 0.946 0.364 -1122.787 2816.855
C(dose)[T.1] -2026.5811 1179.708 -1.718 0.114 -4623.101 569.939
expression -66.3252 76.135 -0.871 0.402 -233.897 101.247
expression:C(dose)[T.1] 175.6402 99.988 1.757 0.107 -44.432 395.712
Omnibus: 2.486 Durbin-Watson: 1.012
Prob(Omnibus): 0.289 Jarque-Bera (JB): 1.495
Skew: -0.523 Prob(JB): 0.473
Kurtosis: 1.860 Cond. No. 2.72e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.468
Model: OLS Adj. R-squared: 0.380
Method: Least Squares F-statistic: 5.285
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0226
Time: 03:47:52 Log-Likelihood: -70.562
No. Observations: 15 AIC: 147.1
Df Residuals: 12 BIC: 149.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -349.9640 628.608 -0.557 0.588 -1719.584 1019.656
C(dose)[T.1] 45.5412 16.409 2.775 0.017 9.790 81.293
expression 35.5098 53.470 0.664 0.519 -80.992 152.012
Omnibus: 2.510 Durbin-Watson: 0.604
Prob(Omnibus): 0.285 Jarque-Bera (JB): 1.858
Skew: -0.730 Prob(JB): 0.395
Kurtosis: 2.083 Cond. No. 971.

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: 03:47:52 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: 0.127
Model: OLS Adj. R-squared: 0.060
Method: Least Squares F-statistic: 1.891
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.192
Time: 03:47:52 Log-Likelihood: -74.281
No. Observations: 15 AIC: 152.6
Df Residuals: 13 BIC: 154.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -913.5187 732.396 -1.247 0.234 -2495.763 668.726
expression 85.2883 62.014 1.375 0.192 -48.685 219.261
Omnibus: 0.718 Durbin-Watson: 1.376
Prob(Omnibus): 0.698 Jarque-Bera (JB): 0.479
Skew: -0.401 Prob(JB): 0.787
Kurtosis: 2.651 Cond. No. 918.