LID SPN TRAP

1450871_a_at

Bcat1 branched chain aminotransferase 1, cytosolic

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
0.222 0.643 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.92
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.000128
Time: 23:00:00 Log-Likelihood: -100.93
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 124.0748 195.069 0.636 0.532 -284.209 532.359
C(dose)[T.1] 94.3508 417.793 0.226 0.824 -780.101 968.802
expression -7.4705 20.847 -0.358 0.724 -51.105 36.164
expression:C(dose)[T.1] -3.6594 42.539 -0.086 0.932 -92.694 85.375
Omnibus: 0.920 Durbin-Watson: 1.833
Prob(Omnibus): 0.631 Jarque-Bera (JB): 0.748
Skew: 0.007 Prob(JB): 0.688
Kurtosis: 2.116 Cond. No. 1.08e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 18.81
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.54e-05
Time: 23:00:00 Log-Likelihood: -100.94
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 132.2947 165.790 0.798 0.434 -213.537 478.127
C(dose)[T.1] 58.4307 13.888 4.207 0.000 29.462 87.400
expression -8.3495 17.716 -0.471 0.643 -45.303 28.604
Omnibus: 0.875 Durbin-Watson: 1.852
Prob(Omnibus): 0.646 Jarque-Bera (JB): 0.732
Skew: -0.011 Prob(JB): 0.693
Kurtosis: 2.126 Cond. No. 373.

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: 23:00:00 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: 0.346
Model: OLS Adj. R-squared: 0.315
Method: Least Squares F-statistic: 11.10
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00317
Time: 23:00:00 Log-Likelihood: -108.23
No. Observations: 23 AIC: 220.5
Df Residuals: 21 BIC: 222.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -399.1585 143.884 -2.774 0.011 -698.382 -99.936
expression 49.6552 14.907 3.331 0.003 18.654 80.656
Omnibus: 0.602 Durbin-Watson: 2.258
Prob(Omnibus): 0.740 Jarque-Bera (JB): 0.682
Skew: 0.244 Prob(JB): 0.711
Kurtosis: 2.311 Cond. No. 240.

CP101

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

F-statistic p-value df difference
2.479 0.141 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.595
Model: OLS Adj. R-squared: 0.484
Method: Least Squares F-statistic: 5.379
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0159
Time: 23:00:01 Log-Likelihood: -68.527
No. Observations: 15 AIC: 145.1
Df Residuals: 11 BIC: 147.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 18.2587 516.683 0.035 0.972 -1118.954 1155.471
C(dose)[T.1] -738.1314 666.489 -1.107 0.292 -2205.065 728.802
expression 5.0343 52.890 0.095 0.926 -111.376 121.445
expression:C(dose)[T.1] 80.6758 68.244 1.182 0.262 -69.528 230.880
Omnibus: 2.552 Durbin-Watson: 0.916
Prob(Omnibus): 0.279 Jarque-Bera (JB): 1.672
Skew: -0.804 Prob(JB): 0.434
Kurtosis: 2.702 Cond. No. 1.30e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.543
Model: OLS Adj. R-squared: 0.467
Method: Least Squares F-statistic: 7.134
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.00909
Time: 23:00:01 Log-Likelihood: -69.424
No. Observations: 15 AIC: 144.8
Df Residuals: 12 BIC: 147.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -455.0309 331.977 -1.371 0.196 -1178.347 268.285
C(dose)[T.1] 49.5928 14.331 3.461 0.005 18.368 80.818
expression 53.4921 33.973 1.575 0.141 -20.528 127.512
Omnibus: 2.561 Durbin-Watson: 1.196
Prob(Omnibus): 0.278 Jarque-Bera (JB): 1.920
Skew: -0.758 Prob(JB): 0.383
Kurtosis: 2.120 Cond. No. 459.

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: 23:00:01 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: 0.087
Model: OLS Adj. R-squared: 0.017
Method: Least Squares F-statistic: 1.243
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.285
Time: 23:00:01 Log-Likelihood: -74.615
No. Observations: 15 AIC: 153.2
Df Residuals: 13 BIC: 154.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -408.4180 450.462 -0.907 0.381 -1381.583 564.747
expression 51.4269 46.129 1.115 0.285 -48.228 151.082
Omnibus: 0.140 Durbin-Watson: 1.683
Prob(Omnibus): 0.933 Jarque-Bera (JB): 0.330
Skew: -0.155 Prob(JB): 0.848
Kurtosis: 2.343 Cond. No. 458.