LID SPN TRAP

1435719_at

AI448984 expressed sequence AI448984

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
0.460 0.506 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.20
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000111
Time: 03:31:08 Log-Likelihood: -100.75
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 107.0582 86.808 1.233 0.233 -74.632 288.749
C(dose)[T.1] 22.4127 104.463 0.215 0.832 -196.231 241.056
expression -10.2877 16.856 -0.610 0.549 -45.567 24.991
expression:C(dose)[T.1] 5.7720 20.630 0.280 0.783 -37.408 48.952
Omnibus: 0.931 Durbin-Watson: 2.022
Prob(Omnibus): 0.628 Jarque-Bera (JB): 0.752
Skew: -0.012 Prob(JB): 0.687
Kurtosis: 2.114 Cond. No. 172.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.657
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.15
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.26e-05
Time: 03:31:08 Log-Likelihood: -100.80
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 87.2643 49.131 1.776 0.091 -15.221 189.749
C(dose)[T.1] 51.5240 9.074 5.678 0.000 32.596 70.452
expression -6.4346 9.492 -0.678 0.506 -26.235 13.366
Omnibus: 0.683 Durbin-Watson: 1.990
Prob(Omnibus): 0.711 Jarque-Bera (JB): 0.657
Skew: 0.011 Prob(JB): 0.720
Kurtosis: 2.172 Cond. No. 59.6

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: 0.104
Model: OLS Adj. R-squared: 0.061
Method: Least Squares F-statistic: 2.434
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.134
Time: 03:31:09 Log-Likelihood: -111.84
No. Observations: 23 AIC: 227.7
Df Residuals: 21 BIC: 230.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 191.3828 71.892 2.662 0.015 41.874 340.891
expression -22.3222 14.306 -1.560 0.134 -52.074 7.430
Omnibus: 2.262 Durbin-Watson: 2.454
Prob(Omnibus): 0.323 Jarque-Bera (JB): 1.396
Skew: 0.330 Prob(JB): 0.498
Kurtosis: 1.990 Cond. No. 55.0

CP101

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

F-statistic p-value df difference
0.241 0.633 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.312
Method: Least Squares F-statistic: 3.119
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0703
Time: 03:31:09 Log-Likelihood: -70.684
No. Observations: 15 AIC: 149.4
Df Residuals: 11 BIC: 152.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 103.1182 152.246 0.677 0.512 -231.972 438.209
C(dose)[T.1] 48.9742 175.819 0.279 0.786 -338.001 435.949
expression -6.1545 26.174 -0.235 0.818 -63.763 51.454
expression:C(dose)[T.1] 0.2123 29.978 0.007 0.994 -65.769 66.194
Omnibus: 2.875 Durbin-Watson: 0.782
Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.076
Skew: -0.878 Prob(JB): 0.354
Kurtosis: 2.513 Cond. No. 197.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.460
Model: OLS Adj. R-squared: 0.370
Method: Least Squares F-statistic: 5.103
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0249
Time: 03:31:09 Log-Likelihood: -70.684
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 102.1798 71.757 1.424 0.180 -54.165 258.525
C(dose)[T.1] 50.2137 15.722 3.194 0.008 15.959 84.468
expression -5.9927 12.217 -0.491 0.633 -32.612 20.627
Omnibus: 2.874 Durbin-Watson: 0.780
Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.077
Skew: -0.878 Prob(JB): 0.354
Kurtosis: 2.513 Cond. No. 56.4

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: 03:31:09 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.002849
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.958
Time: 03:31:09 Log-Likelihood: -75.298
No. Observations: 15 AIC: 154.6
Df Residuals: 13 BIC: 156.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 98.6421 93.762 1.052 0.312 -103.919 301.203
expression -0.8448 15.827 -0.053 0.958 -35.036 33.346
Omnibus: 0.636 Durbin-Watson: 1.636
Prob(Omnibus): 0.728 Jarque-Bera (JB): 0.592
Skew: 0.041 Prob(JB): 0.744
Kurtosis: 2.030 Cond. No. 56.2