LID SPN TRAP

1460570_at

Pgbd5 piggyBac transposable element derived 5

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
0.010 0.922 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: 11.83
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000135
Time: 05:01:53 Log-Likelihood: -100.99
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 14.4284 398.169 0.036 0.971 -818.948 847.805
C(dose)[T.1] 288.0875 715.032 0.403 0.692 -1208.491 1784.666
expression 3.9341 39.373 0.100 0.921 -78.474 86.343
expression:C(dose)[T.1] -23.6002 71.684 -0.329 0.746 -173.636 126.435
Omnibus: 0.217 Durbin-Watson: 1.849
Prob(Omnibus): 0.897 Jarque-Bera (JB): 0.418
Skew: 0.020 Prob(JB): 0.811
Kurtosis: 2.341 Cond. No. 1.93e+03

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: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 05:01:53 Log-Likelihood: -101.06
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 86.4210 325.246 0.266 0.793 -592.031 764.873
C(dose)[T.1] 52.7080 10.826 4.869 0.000 30.125 75.291
expression -3.1857 32.160 -0.099 0.922 -70.271 63.899
Omnibus: 0.300 Durbin-Watson: 1.888
Prob(Omnibus): 0.861 Jarque-Bera (JB): 0.471
Skew: 0.052 Prob(JB): 0.790
Kurtosis: 2.306 Cond. No. 752.

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: 05:01:53 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: 0.234
Model: OLS Adj. R-squared: 0.197
Method: Least Squares F-statistic: 6.398
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0195
Time: 05:01:53 Log-Likelihood: -110.05
No. Observations: 23 AIC: 224.1
Df Residuals: 21 BIC: 226.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1031.7093 376.418 2.741 0.012 248.904 1814.514
expression -95.0368 37.572 -2.529 0.019 -173.173 -16.901
Omnibus: 2.042 Durbin-Watson: 2.302
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.130
Skew: 0.151 Prob(JB): 0.568
Kurtosis: 1.957 Cond. No. 603.

CP101

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

F-statistic p-value df difference
0.140 0.715 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.525
Model: OLS Adj. R-squared: 0.396
Method: Least Squares F-statistic: 4.059
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0362
Time: 05:01:53 Log-Likelihood: -69.711
No. Observations: 15 AIC: 147.4
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -1818.5841 1425.706 -1.276 0.228 -4956.543 1319.374
C(dose)[T.1] 2034.8143 1557.469 1.306 0.218 -1393.152 5462.780
expression 175.3912 132.581 1.323 0.213 -116.417 467.199
expression:C(dose)[T.1] -184.5916 144.673 -1.276 0.228 -503.015 133.832
Omnibus: 2.539 Durbin-Watson: 0.730
Prob(Omnibus): 0.281 Jarque-Bera (JB): 1.393
Skew: -0.746 Prob(JB): 0.498
Kurtosis: 2.954 Cond. No. 3.44e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.364
Method: Least Squares F-statistic: 5.012
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0262
Time: 05:01:53 Log-Likelihood: -70.746
No. Observations: 15 AIC: 147.5
Df Residuals: 12 BIC: 149.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -151.5975 585.447 -0.259 0.800 -1427.176 1123.981
C(dose)[T.1] 47.7093 16.145 2.955 0.012 12.532 82.887
expression 20.3685 54.434 0.374 0.715 -98.232 138.969
Omnibus: 2.572 Durbin-Watson: 0.775
Prob(Omnibus): 0.276 Jarque-Bera (JB): 1.872
Skew: -0.827 Prob(JB): 0.392
Kurtosis: 2.489 Cond. No. 817.

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: 05:01:53 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: 0.059
Model: OLS Adj. R-squared: -0.014
Method: Least Squares F-statistic: 0.8100
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.385
Time: 05:01:53 Log-Likelihood: -74.847
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 -553.4487 719.103 -0.770 0.455 -2106.976 1000.079
expression 59.9619 66.626 0.900 0.385 -83.975 203.899
Omnibus: 1.996 Durbin-Watson: 1.568
Prob(Omnibus): 0.369 Jarque-Bera (JB): 1.090
Skew: 0.300 Prob(JB): 0.580
Kurtosis: 1.823 Cond. No. 794.