LID SPN TRAP

1440414_at

Mtf1 metal response element binding transcription factor 1

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
0.419 0.525 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.666
Model: OLS Adj. R-squared: 0.613
Method: Least Squares F-statistic: 12.62
Date: Thu, 21 Nov 2024 Prob (F-statistic): 9.06e-05
Time: 04:58:56 Log-Likelihood: -100.50
No. Observations: 23 AIC: 209.0
Df Residuals: 19 BIC: 213.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 16.1261 232.909 0.069 0.946 -471.359 503.611
C(dose)[T.1] 276.9480 302.294 0.916 0.371 -355.761 909.657
expression 4.1987 25.670 0.164 0.872 -49.529 57.927
expression:C(dose)[T.1] -24.5482 33.245 -0.738 0.469 -94.131 45.034
Omnibus: 0.634 Durbin-Watson: 1.911
Prob(Omnibus): 0.728 Jarque-Bera (JB): 0.664
Skew: 0.144 Prob(JB): 0.717
Kurtosis: 2.219 Cond. No. 859.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.09
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.30e-05
Time: 04:58:56 Log-Likelihood: -100.82
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 148.8759 146.381 1.017 0.321 -156.470 454.221
C(dose)[T.1] 53.8278 8.712 6.178 0.000 35.654 72.002
expression -10.4373 16.125 -0.647 0.525 -44.074 23.199
Omnibus: 0.669 Durbin-Watson: 1.938
Prob(Omnibus): 0.716 Jarque-Bera (JB): 0.662
Skew: 0.089 Prob(JB): 0.718
Kurtosis: 2.188 Cond. No. 311.

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: 04:58:57 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: 0.004374
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.948
Time: 04:58:57 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 95.7945 243.208 0.394 0.698 -409.985 601.574
expression -1.7682 26.736 -0.066 0.948 -57.369 53.833
Omnibus: 3.318 Durbin-Watson: 2.480
Prob(Omnibus): 0.190 Jarque-Bera (JB): 1.563
Skew: 0.283 Prob(JB): 0.458
Kurtosis: 1.855 Cond. No. 310.

CP101

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

F-statistic p-value df difference
0.021 0.887 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.505
Model: OLS Adj. R-squared: 0.369
Method: Least Squares F-statistic: 3.734
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0451
Time: 04:58:57 Log-Likelihood: -70.033
No. Observations: 15 AIC: 148.1
Df Residuals: 11 BIC: 150.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 111.9541 205.410 0.545 0.597 -340.150 564.058
C(dose)[T.1] -638.3933 624.198 -1.023 0.328 -2012.243 735.457
expression -5.0666 23.338 -0.217 0.832 -56.433 46.300
expression:C(dose)[T.1] 79.9246 72.464 1.103 0.294 -79.569 239.418
Omnibus: 1.689 Durbin-Watson: 1.134
Prob(Omnibus): 0.430 Jarque-Bera (JB): 0.586
Skew: -0.476 Prob(JB): 0.746
Kurtosis: 3.180 Cond. No. 822.

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: 4.904
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0278
Time: 04:58:57 Log-Likelihood: -70.820
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 39.1012 196.247 0.199 0.845 -388.483 466.686
C(dose)[T.1] 49.8332 16.331 3.051 0.010 14.251 85.415
expression 3.2234 22.293 0.145 0.887 -45.349 51.795
Omnibus: 2.721 Durbin-Watson: 0.826
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.841
Skew: -0.840 Prob(JB): 0.398
Kurtosis: 2.654 Cond. No. 221.

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: 04:58:57 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: 0.023
Model: OLS Adj. R-squared: -0.052
Method: Least Squares F-statistic: 0.3026
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.592
Time: 04:58:57 Log-Likelihood: -75.127
No. Observations: 15 AIC: 154.3
Df Residuals: 13 BIC: 155.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 224.9536 238.860 0.942 0.363 -291.072 740.980
expression -15.1206 27.486 -0.550 0.592 -74.500 44.259
Omnibus: 1.173 Durbin-Watson: 1.600
Prob(Omnibus): 0.556 Jarque-Bera (JB): 0.759
Skew: 0.058 Prob(JB): 0.684
Kurtosis: 1.904 Cond. No. 209.