LID SPN TRAP

1460367_at

Hbp1 high mobility group box 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
0.081 0.778 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.712
Model: OLS Adj. R-squared: 0.666
Method: Least Squares F-statistic: 15.63
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.30e-05
Time: 04:35:27 Log-Likelihood: -98.804
No. Observations: 23 AIC: 205.6
Df Residuals: 19 BIC: 210.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -187.9137 192.886 -0.974 0.342 -591.629 215.802
C(dose)[T.1] 576.1367 261.129 2.206 0.040 29.588 1122.686
expression 28.0874 22.366 1.256 0.224 -18.726 74.900
expression:C(dose)[T.1] -61.4995 30.640 -2.007 0.059 -125.630 2.631
Omnibus: 0.659 Durbin-Watson: 1.829
Prob(Omnibus): 0.719 Jarque-Bera (JB): 0.652
Skew: 0.059 Prob(JB): 0.722
Kurtosis: 2.184 Cond. No. 728.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.72e-05
Time: 04:35:27 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 94.5752 141.534 0.668 0.512 -200.659 389.809
C(dose)[T.1] 52.3076 9.466 5.526 0.000 32.562 72.053
expression -4.6828 16.404 -0.285 0.778 -38.900 29.535
Omnibus: 0.790 Durbin-Watson: 1.949
Prob(Omnibus): 0.674 Jarque-Bera (JB): 0.714
Skew: 0.093 Prob(JB): 0.700
Kurtosis: 2.157 Cond. No. 280.

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: 04:35:27 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: 0.117
Model: OLS Adj. R-squared: 0.075
Method: Least Squares F-statistic: 2.778
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.110
Time: 04:35:27 Log-Likelihood: -111.68
No. Observations: 23 AIC: 227.4
Df Residuals: 21 BIC: 229.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 413.6462 200.456 2.064 0.052 -3.225 830.517
expression -39.2157 23.528 -1.667 0.110 -88.144 9.712
Omnibus: 1.379 Durbin-Watson: 2.579
Prob(Omnibus): 0.502 Jarque-Bera (JB): 0.928
Skew: -0.120 Prob(JB): 0.629
Kurtosis: 2.045 Cond. No. 255.

CP101

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

F-statistic p-value df difference
5.327 0.040 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.621
Model: OLS Adj. R-squared: 0.517
Method: Least Squares F-statistic: 6.000
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0112
Time: 04:35:27 Log-Likelihood: -68.029
No. Observations: 15 AIC: 144.1
Df Residuals: 11 BIC: 146.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 403.4178 222.050 1.817 0.097 -85.310 892.146
C(dose)[T.1] 120.4972 333.197 0.362 0.724 -612.864 853.858
expression -35.7314 23.591 -1.515 0.158 -87.654 16.191
expression:C(dose)[T.1] -9.7145 36.384 -0.267 0.794 -89.795 70.366
Omnibus: 1.298 Durbin-Watson: 1.295
Prob(Omnibus): 0.522 Jarque-Bera (JB): 0.938
Skew: -0.333 Prob(JB): 0.626
Kurtosis: 1.971 Cond. No. 583.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.618
Model: OLS Adj. R-squared: 0.555
Method: Least Squares F-statistic: 9.717
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00310
Time: 04:35:27 Log-Likelihood: -68.078
No. Observations: 15 AIC: 142.2
Df Residuals: 12 BIC: 144.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 441.8193 162.496 2.719 0.019 87.772 795.867
C(dose)[T.1] 31.6340 15.148 2.088 0.059 -1.372 64.640
expression -39.8153 17.251 -2.308 0.040 -77.402 -2.229
Omnibus: 1.481 Durbin-Watson: 1.415
Prob(Omnibus): 0.477 Jarque-Bera (JB): 1.012
Skew: -0.354 Prob(JB): 0.603
Kurtosis: 1.942 Cond. No. 232.

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: 04:35:27 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: 0.480
Model: OLS Adj. R-squared: 0.439
Method: Least Squares F-statistic: 11.98
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00422
Time: 04:35:27 Log-Likelihood: -70.403
No. Observations: 15 AIC: 144.8
Df Residuals: 13 BIC: 146.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 624.5919 153.592 4.067 0.001 292.777 956.407
expression -57.9111 16.734 -3.461 0.004 -94.063 -21.759
Omnibus: 2.418 Durbin-Watson: 2.390
Prob(Omnibus): 0.298 Jarque-Bera (JB): 1.318
Skew: 0.415 Prob(JB): 0.517
Kurtosis: 1.809 Cond. No. 195.