LID SPN TRAP

1457316_at

Mtap6 microtubule-associated protein 6

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
0.049 0.828 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.80
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.000137
Time: 20:56:04 Log-Likelihood: -101.01
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.8173 55.908 1.267 0.221 -46.199 187.833
C(dose)[T.1] 35.6532 86.782 0.411 0.686 -145.984 217.290
expression -2.7101 9.066 -0.299 0.768 -21.686 16.266
expression:C(dose)[T.1] 2.8807 13.858 0.208 0.838 -26.124 31.885
Omnibus: 0.340 Durbin-Watson: 1.882
Prob(Omnibus): 0.844 Jarque-Bera (JB): 0.494
Skew: 0.036 Prob(JB): 0.781
Kurtosis: 2.286 Cond. No. 156.

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: 18.56
Date: Mon, 27 Jan 2025 Prob (F-statistic): 2.77e-05
Time: 20:56:04 Log-Likelihood: -101.03
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 63.2608 41.448 1.526 0.143 -23.199 149.721
C(dose)[T.1] 53.5948 8.837 6.065 0.000 35.162 72.028
expression -1.4771 6.691 -0.221 0.828 -15.434 12.479
Omnibus: 0.371 Durbin-Watson: 1.884
Prob(Omnibus): 0.831 Jarque-Bera (JB): 0.512
Skew: 0.043 Prob(JB): 0.774
Kurtosis: 2.274 Cond. No. 60.9

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: Mon, 27 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 20:56:04 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: 0.006
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.1267
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.725
Time: 20:56:04 Log-Likelihood: -113.04
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 55.6045 68.126 0.816 0.424 -86.072 197.281
expression 3.8817 10.906 0.356 0.725 -18.798 26.561
Omnibus: 3.008 Durbin-Watson: 2.408
Prob(Omnibus): 0.222 Jarque-Bera (JB): 1.503
Skew: 0.285 Prob(JB): 0.472
Kurtosis: 1.885 Cond. No. 60.6

CP101

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

F-statistic p-value df difference
0.002 0.969 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.521
Model: OLS Adj. R-squared: 0.390
Method: Least Squares F-statistic: 3.986
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.0380
Time: 20:56:04 Log-Likelihood: -69.782
No. Observations: 15 AIC: 147.6
Df Residuals: 11 BIC: 150.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 121.4247 76.715 1.583 0.142 -47.424 290.273
C(dose)[T.1] -138.3963 146.757 -0.943 0.366 -461.407 184.614
expression -9.5852 13.472 -0.711 0.492 -39.238 20.067
expression:C(dose)[T.1] 32.8900 25.583 1.286 0.225 -23.417 89.197
Omnibus: 2.408 Durbin-Watson: 1.270
Prob(Omnibus): 0.300 Jarque-Bera (JB): 1.574
Skew: -0.779 Prob(JB): 0.455
Kurtosis: 2.697 Cond. No. 138.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.886
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.0280
Time: 20:56:04 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 70.0410 67.239 1.042 0.318 -76.460 216.542
C(dose)[T.1] 49.2425 15.782 3.120 0.009 14.857 83.628
expression -0.4638 11.760 -0.039 0.969 -26.087 25.160
Omnibus: 2.717 Durbin-Watson: 0.814
Prob(Omnibus): 0.257 Jarque-Bera (JB): 1.867
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.624 Cond. No. 50.7

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: Mon, 27 Jan 2025 Prob (F-statistic): 0.00629
Time: 20:56:04 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: 0.002
Model: OLS Adj. R-squared: -0.075
Method: Least Squares F-statistic: 0.02205
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.884
Time: 20:56:04 Log-Likelihood: -75.287
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 80.8608 86.827 0.931 0.369 -106.718 268.439
expression 2.2521 15.165 0.149 0.884 -30.510 35.014
Omnibus: 0.493 Durbin-Watson: 1.637
Prob(Omnibus): 0.782 Jarque-Bera (JB): 0.537
Skew: 0.003 Prob(JB): 0.765
Kurtosis: 2.073 Cond. No. 50.4