LID SPN TRAP

1436332_at

Hspb6 heat shock protein, alpha-crystallin-related, B6

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
0.147 0.705 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.32
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000105
Time: 03:31:08 Log-Likelihood: -100.68
No. Observations: 23 AIC: 209.4
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 19.4663 133.504 0.146 0.886 -259.962 298.894
C(dose)[T.1] 182.6360 183.322 0.996 0.332 -201.062 566.334
expression 5.0205 19.272 0.261 0.797 -35.316 45.357
expression:C(dose)[T.1] -18.3727 26.179 -0.702 0.491 -73.165 36.420
Omnibus: 1.719 Durbin-Watson: 1.970
Prob(Omnibus): 0.423 Jarque-Bera (JB): 1.157
Skew: 0.269 Prob(JB): 0.561
Kurtosis: 2.042 Cond. No. 392.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.70
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.63e-05
Time: 03:31:08 Log-Likelihood: -100.98
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 88.3705 89.312 0.989 0.334 -97.931 274.672
C(dose)[T.1] 54.1350 8.982 6.027 0.000 35.398 72.872
expression -4.9367 12.877 -0.383 0.705 -31.797 21.924
Omnibus: 0.623 Durbin-Watson: 1.855
Prob(Omnibus): 0.732 Jarque-Bera (JB): 0.652
Skew: 0.123 Prob(JB): 0.722
Kurtosis: 2.213 Cond. No. 147.

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: 03:31:08 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: 0.019
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.4043
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.532
Time: 03:31:08 Log-Likelihood: -112.89
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -11.5592 143.724 -0.080 0.937 -310.450 287.331
expression 13.0444 20.514 0.636 0.532 -29.617 55.706
Omnibus: 4.097 Durbin-Watson: 2.466
Prob(Omnibus): 0.129 Jarque-Bera (JB): 1.731
Skew: 0.298 Prob(JB): 0.421
Kurtosis: 1.795 Cond. No. 144.

CP101

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

F-statistic p-value df difference
1.714 0.215 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.546
Model: OLS Adj. R-squared: 0.423
Method: Least Squares F-statistic: 4.417
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0286
Time: 03:31:08 Log-Likelihood: -69.371
No. Observations: 15 AIC: 146.7
Df Residuals: 11 BIC: 149.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -34.7384 244.875 -0.142 0.890 -573.704 504.227
C(dose)[T.1] 261.3537 256.093 1.021 0.329 -302.302 825.010
expression 16.5119 39.537 0.418 0.684 -70.508 103.532
expression:C(dose)[T.1] -34.5429 41.369 -0.835 0.421 -125.595 56.509
Omnibus: 4.443 Durbin-Watson: 1.353
Prob(Omnibus): 0.108 Jarque-Bera (JB): 2.122
Skew: -0.873 Prob(JB): 0.346
Kurtosis: 3.589 Cond. No. 346.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.518
Model: OLS Adj. R-squared: 0.437
Method: Least Squares F-statistic: 6.439
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0126
Time: 03:31:08 Log-Likelihood: -69.832
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 160.4820 71.892 2.232 0.045 3.843 317.120
C(dose)[T.1] 47.8820 14.758 3.245 0.007 15.728 80.036
expression -15.0390 11.488 -1.309 0.215 -40.070 9.992
Omnibus: 4.100 Durbin-Watson: 1.081
Prob(Omnibus): 0.129 Jarque-Bera (JB): 1.969
Skew: -0.855 Prob(JB): 0.374
Kurtosis: 3.478 Cond. No. 62.2

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: 03:31:08 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: 0.095
Model: OLS Adj. R-squared: 0.025
Method: Least Squares F-statistic: 1.357
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.265
Time: 03:31:08 Log-Likelihood: -74.555
No. Observations: 15 AIC: 153.1
Df Residuals: 13 BIC: 154.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 201.5928 93.155 2.164 0.050 0.343 402.843
expression -17.5751 15.088 -1.165 0.265 -50.170 15.020
Omnibus: 0.069 Durbin-Watson: 1.852
Prob(Omnibus): 0.966 Jarque-Bera (JB): 0.227
Skew: -0.129 Prob(JB): 0.893
Kurtosis: 2.455 Cond. No. 61.0