LID SPN TRAP

1455007_s_at

Gpt2 glutamic pyruvate transaminase (alanine aminotransferase) 2

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
2.281 0.147 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.693
Model: OLS Adj. R-squared: 0.644
Method: Least Squares F-statistic: 14.29
Date: Thu, 03 Apr 2025 Prob (F-statistic): 4.13e-05
Time: 23:00:40 Log-Likelihood: -99.526
No. Observations: 23 AIC: 207.1
Df Residuals: 19 BIC: 211.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -187.1354 153.919 -1.216 0.239 -509.291 135.020
C(dose)[T.1] 210.2493 227.344 0.925 0.367 -265.587 686.086
expression 32.0793 20.444 1.569 0.133 -10.711 74.870
expression:C(dose)[T.1] -21.0355 29.938 -0.703 0.491 -83.697 41.626
Omnibus: 0.441 Durbin-Watson: 1.829
Prob(Omnibus): 0.802 Jarque-Bera (JB): 0.573
Skew: -0.206 Prob(JB): 0.751
Kurtosis: 2.347 Cond. No. 533.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.685
Model: OLS Adj. R-squared: 0.653
Method: Least Squares F-statistic: 21.74
Date: Thu, 03 Apr 2025 Prob (F-statistic): 9.62e-06
Time: 23:00:41 Log-Likelihood: -99.821
No. Observations: 23 AIC: 205.6
Df Residuals: 20 BIC: 209.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -113.3352 111.079 -1.020 0.320 -345.042 118.371
C(dose)[T.1] 50.6238 8.501 5.955 0.000 32.891 68.356
expression 22.2698 14.745 1.510 0.147 -8.487 53.027
Omnibus: 0.246 Durbin-Watson: 1.743
Prob(Omnibus): 0.884 Jarque-Bera (JB): 0.420
Skew: -0.173 Prob(JB): 0.810
Kurtosis: 2.435 Cond. No. 207.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 23:00:41 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: 0.126
Model: OLS Adj. R-squared: 0.085
Method: Least Squares F-statistic: 3.038
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0959
Time: 23:00:41 Log-Likelihood: -111.55
No. Observations: 23 AIC: 227.1
Df Residuals: 21 BIC: 229.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -229.8127 177.701 -1.293 0.210 -599.362 139.736
expression 40.8264 23.421 1.743 0.096 -7.881 89.534
Omnibus: 2.074 Durbin-Watson: 2.228
Prob(Omnibus): 0.355 Jarque-Bera (JB): 1.474
Skew: 0.409 Prob(JB): 0.478
Kurtosis: 2.068 Cond. No. 203.

CP101

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

F-statistic p-value df difference
0.813 0.385 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.551
Model: OLS Adj. R-squared: 0.429
Method: Least Squares F-statistic: 4.509
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0270
Time: 23:00:41 Log-Likelihood: -69.286
No. Observations: 15 AIC: 146.6
Df Residuals: 11 BIC: 149.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 92.5869 181.742 0.509 0.621 -307.425 492.599
C(dose)[T.1] -313.0611 280.337 -1.117 0.288 -930.079 303.957
expression -3.4207 24.667 -0.139 0.892 -57.712 50.871
expression:C(dose)[T.1] 48.7264 37.809 1.289 0.224 -34.491 131.944
Omnibus: 0.042 Durbin-Watson: 1.167
Prob(Omnibus): 0.979 Jarque-Bera (JB): 0.243
Skew: -0.085 Prob(JB): 0.886
Kurtosis: 2.400 Cond. No. 368.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.484
Model: OLS Adj. R-squared: 0.398
Method: Least Squares F-statistic: 5.623
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0189
Time: 23:00:41 Log-Likelihood: -70.341
No. Observations: 15 AIC: 146.7
Df Residuals: 12 BIC: 148.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -59.9479 141.665 -0.423 0.680 -368.608 248.713
C(dose)[T.1] 47.7103 15.321 3.114 0.009 14.329 81.091
expression 17.3190 19.202 0.902 0.385 -24.519 59.157
Omnibus: 1.272 Durbin-Watson: 0.716
Prob(Omnibus): 0.529 Jarque-Bera (JB): 1.019
Skew: -0.437 Prob(JB): 0.601
Kurtosis: 2.070 Cond. No. 141.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 23:00:41 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: 0.067
Model: OLS Adj. R-squared: -0.005
Method: Least Squares F-statistic: 0.9273
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.353
Time: 23:00:42 Log-Likelihood: -74.783
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -82.0979 182.788 -0.449 0.661 -476.987 312.791
expression 23.7504 24.664 0.963 0.353 -29.532 77.033
Omnibus: 0.204 Durbin-Watson: 1.495
Prob(Omnibus): 0.903 Jarque-Bera (JB): 0.398
Skew: 0.036 Prob(JB): 0.819
Kurtosis: 2.205 Cond. No. 140.