LID SPN TRAP

1436994_a_at

Hist1h1c histone cluster 1, H1c

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
0.022 0.884 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.80
Date: Sat, 01 Nov 2025 Prob (F-statistic): 0.000137
Time: 13:23:19 Log-Likelihood: -101.01
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -24.9353 264.700 -0.094 0.926 -578.959 529.088
C(dose)[T.1] 139.7174 328.585 0.425 0.675 -548.019 827.453
expression 9.4309 31.534 0.299 0.768 -56.569 75.431
expression:C(dose)[T.1] -10.2702 38.776 -0.265 0.794 -91.430 70.890
Omnibus: 0.216 Durbin-Watson: 1.962
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.414
Skew: 0.111 Prob(JB): 0.813
Kurtosis: 2.381 Cond. No. 881.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.53
Date: Sat, 01 Nov 2025 Prob (F-statistic): 2.80e-05
Time: 13:23:19 Log-Likelihood: -101.05
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 32.0615 150.503 0.213 0.833 -281.883 346.006
C(dose)[T.1] 52.7291 9.689 5.442 0.000 32.519 72.939
expression 2.6391 17.920 0.147 0.884 -34.741 40.019
Omnibus: 0.331 Durbin-Watson: 1.916
Prob(Omnibus): 0.847 Jarque-Bera (JB): 0.493
Skew: 0.095 Prob(JB): 0.781
Kurtosis: 2.308 Cond. No. 297.

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: Sat, 01 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 13:23:19 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: 0.130
Model: OLS Adj. R-squared: 0.089
Method: Least Squares F-statistic: 3.145
Date: Sat, 01 Nov 2025 Prob (F-statistic): 0.0906
Time: 13:23:19 Log-Likelihood: -111.50
No. Observations: 23 AIC: 227.0
Df Residuals: 21 BIC: 229.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -296.0368 211.975 -1.397 0.177 -736.862 144.788
expression 44.1953 24.919 1.774 0.091 -7.627 96.018
Omnibus: 1.919 Durbin-Watson: 2.482
Prob(Omnibus): 0.383 Jarque-Bera (JB): 1.668
Skew: 0.584 Prob(JB): 0.434
Kurtosis: 2.387 Cond. No. 272.

CP101

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

F-statistic p-value df difference
2.036 0.179 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.583
Model: OLS Adj. R-squared: 0.469
Method: Least Squares F-statistic: 5.118
Date: Sat, 01 Nov 2025 Prob (F-statistic): 0.0186
Time: 13:23:19 Log-Likelihood: -68.747
No. Observations: 15 AIC: 145.5
Df Residuals: 11 BIC: 148.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 0.4416 370.871 0.001 0.999 -815.841 816.724
C(dose)[T.1] -572.1509 522.690 -1.095 0.297 -1722.584 578.282
expression 6.9679 38.562 0.181 0.860 -77.907 91.843
expression:C(dose)[T.1] 64.8840 54.444 1.192 0.258 -54.948 184.715
Omnibus: 1.893 Durbin-Watson: 0.604
Prob(Omnibus): 0.388 Jarque-Bera (JB): 1.434
Skew: -0.700 Prob(JB): 0.488
Kurtosis: 2.420 Cond. No. 945.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.529
Model: OLS Adj. R-squared: 0.450
Method: Least Squares F-statistic: 6.732
Date: Sat, 01 Nov 2025 Prob (F-statistic): 0.0110
Time: 13:23:19 Log-Likelihood: -69.658
No. Observations: 15 AIC: 145.3
Df Residuals: 12 BIC: 147.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -312.4870 266.458 -1.173 0.264 -893.048 268.074
C(dose)[T.1] 50.5282 14.583 3.465 0.005 18.754 82.302
expression 39.5185 27.695 1.427 0.179 -20.823 99.860
Omnibus: 3.363 Durbin-Watson: 0.643
Prob(Omnibus): 0.186 Jarque-Bera (JB): 1.705
Skew: -0.535 Prob(JB): 0.426
Kurtosis: 1.742 Cond. No. 357.

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: Sat, 01 Nov 2025 Prob (F-statistic): 0.00629
Time: 13:23:19 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: 0.057
Model: OLS Adj. R-squared: -0.015
Method: Least Squares F-statistic: 0.7898
Date: Sat, 01 Nov 2025 Prob (F-statistic): 0.390
Time: 13:23:19 Log-Likelihood: -74.858
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -226.6124 360.512 -0.629 0.541 -1005.452 552.227
expression 33.3776 37.556 0.889 0.390 -47.758 114.513
Omnibus: 0.318 Durbin-Watson: 1.721
Prob(Omnibus): 0.853 Jarque-Bera (JB): 0.460
Skew: -0.011 Prob(JB): 0.795
Kurtosis: 2.143 Cond. No. 355.