LID SPN TRAP

1451037_at

Ptpn9 protein tyrosine phosphatase, non-receptor type 9

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
0.404 0.532 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.678
Model: OLS Adj. R-squared: 0.627
Method: Least Squares F-statistic: 13.34
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.41e-05
Time: 04:31:04 Log-Likelihood: -100.07
No. Observations: 23 AIC: 208.1
Df Residuals: 19 BIC: 212.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -165.0590 180.213 -0.916 0.371 -542.249 212.131
C(dose)[T.1] 374.1484 278.709 1.342 0.195 -209.196 957.493
expression 26.5178 21.783 1.217 0.238 -19.074 72.110
expression:C(dose)[T.1] -39.2550 34.425 -1.140 0.268 -111.307 32.797
Omnibus: 0.589 Durbin-Watson: 2.098
Prob(Omnibus): 0.745 Jarque-Bera (JB): 0.624
Skew: -0.076 Prob(JB): 0.732
Kurtosis: 2.207 Cond. No. 663.

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: 19.07
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.32e-05
Time: 04:31:04 Log-Likelihood: -100.83
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -35.0998 140.643 -0.250 0.805 -328.475 258.276
C(dose)[T.1] 56.5385 10.038 5.633 0.000 35.600 77.477
expression 10.8008 16.994 0.636 0.532 -24.647 46.249
Omnibus: 0.572 Durbin-Watson: 1.924
Prob(Omnibus): 0.751 Jarque-Bera (JB): 0.609
Skew: 0.001 Prob(JB): 0.738
Kurtosis: 2.203 Cond. No. 268.

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: 04:31:04 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: 0.110
Model: OLS Adj. R-squared: 0.068
Method: Least Squares F-statistic: 2.604
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.121
Time: 04:31:04 Log-Likelihood: -111.76
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 382.2811 187.606 2.038 0.054 -7.867 772.429
expression -37.2298 23.069 -1.614 0.121 -85.205 10.745
Omnibus: 1.615 Durbin-Watson: 2.399
Prob(Omnibus): 0.446 Jarque-Bera (JB): 0.967
Skew: 0.037 Prob(JB): 0.617
Kurtosis: 1.998 Cond. No. 228.

CP101

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

F-statistic p-value df difference
0.005 0.943 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.482
Model: OLS Adj. R-squared: 0.341
Method: Least Squares F-statistic: 3.410
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0567
Time: 04:31:04 Log-Likelihood: -70.368
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 157.2215 188.423 0.834 0.422 -257.495 571.938
C(dose)[T.1] -260.8762 371.161 -0.703 0.497 -1077.796 556.044
expression -10.3139 21.602 -0.477 0.642 -57.859 37.231
expression:C(dose)[T.1] 36.3636 43.532 0.835 0.421 -59.449 132.176
Omnibus: 1.749 Durbin-Watson: 0.917
Prob(Omnibus): 0.417 Jarque-Bera (JB): 1.390
Skew: -0.638 Prob(JB): 0.499
Kurtosis: 2.228 Cond. No. 487.

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: 4.890
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 04:31:04 Log-Likelihood: -70.830
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 79.2666 161.615 0.490 0.633 -272.863 431.396
C(dose)[T.1] 48.8567 16.402 2.979 0.012 13.120 84.594
expression -1.3598 18.517 -0.073 0.943 -41.704 38.985
Omnibus: 2.748 Durbin-Watson: 0.833
Prob(Omnibus): 0.253 Jarque-Bera (JB): 1.871
Skew: -0.847 Prob(JB): 0.392
Kurtosis: 2.643 Cond. No. 179.

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: 04:31:04 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: 0.042
Model: OLS Adj. R-squared: -0.032
Method: Least Squares F-statistic: 0.5648
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.466
Time: 04:31:04 Log-Likelihood: -74.981
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 238.6947 193.229 1.235 0.239 -178.751 656.140
expression -16.9174 22.510 -0.752 0.466 -65.547 31.713
Omnibus: 0.988 Durbin-Watson: 1.809
Prob(Omnibus): 0.610 Jarque-Bera (JB): 0.712
Skew: 0.082 Prob(JB): 0.700
Kurtosis: 1.945 Cond. No. 169.