LID SPN TRAP

1423285_at

Coch coagulation factor C homolog (Limulus polyphemus)

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
0.049 0.826 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.595
Method: Least Squares F-statistic: 11.78
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000138
Time: 05:22:07 Log-Likelihood: -101.02
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 67.9122 120.311 0.564 0.579 -183.901 319.726
C(dose)[T.1] 85.6744 233.723 0.367 0.718 -403.514 574.863
expression -1.5380 13.484 -0.114 0.910 -29.761 26.685
expression:C(dose)[T.1] -3.5682 25.984 -0.137 0.892 -57.954 50.817
Omnibus: 0.493 Durbin-Watson: 1.799
Prob(Omnibus): 0.781 Jarque-Bera (JB): 0.580
Skew: 0.083 Prob(JB): 0.748
Kurtosis: 2.240 Cond. No. 560.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.57
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.76e-05
Time: 05:22:07 Log-Likelihood: -101.03
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 76.4745 100.337 0.762 0.455 -132.825 285.774
C(dose)[T.1] 53.6031 8.840 6.063 0.000 35.162 72.044
expression -2.4989 11.240 -0.222 0.826 -25.946 20.948
Omnibus: 0.459 Durbin-Watson: 1.803
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.559
Skew: 0.053 Prob(JB): 0.756
Kurtosis: 2.244 Cond. No. 209.

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: 05:22:07 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: 0.006
Model: OLS Adj. R-squared: -0.041
Method: Least Squares F-statistic: 0.1348
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.717
Time: 05:22:07 Log-Likelihood: -113.03
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 19.4640 164.240 0.119 0.907 -322.093 361.021
expression 6.7238 18.310 0.367 0.717 -31.355 44.802
Omnibus: 3.688 Durbin-Watson: 2.549
Prob(Omnibus): 0.158 Jarque-Bera (JB): 1.634
Skew: 0.284 Prob(JB): 0.442
Kurtosis: 1.824 Cond. No. 207.

CP101

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

F-statistic p-value df difference
6.990 0.021 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.697
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 8.436
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00342
Time: 05:22:07 Log-Likelihood: -66.344
No. Observations: 15 AIC: 140.7
Df Residuals: 11 BIC: 143.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -173.3121 184.359 -0.940 0.367 -579.084 232.459
C(dose)[T.1] -332.2164 295.039 -1.126 0.284 -981.593 317.160
expression 23.0265 17.613 1.307 0.218 -15.740 61.793
expression:C(dose)[T.1] 36.0067 28.059 1.283 0.226 -25.751 97.764
Omnibus: 5.872 Durbin-Watson: 0.513
Prob(Omnibus): 0.053 Jarque-Bera (JB): 3.343
Skew: -1.131 Prob(JB): 0.188
Kurtosis: 3.484 Cond. No. 653.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.23
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00179
Time: 05:22:07 Log-Likelihood: -67.390
No. Observations: 15 AIC: 140.8
Df Residuals: 12 BIC: 142.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -321.6437 147.441 -2.182 0.050 -642.889 -0.398
C(dose)[T.1] 46.0662 12.568 3.665 0.003 18.684 73.449
expression 37.2143 14.075 2.644 0.021 6.547 67.882
Omnibus: 2.739 Durbin-Watson: 0.504
Prob(Omnibus): 0.254 Jarque-Bera (JB): 1.746
Skew: -0.627 Prob(JB): 0.418
Kurtosis: 1.895 Cond. No. 251.

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: 05:22:07 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: 0.262
Model: OLS Adj. R-squared: 0.205
Method: Least Squares F-statistic: 4.608
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0513
Time: 05:22:07 Log-Likelihood: -73.025
No. Observations: 15 AIC: 150.0
Df Residuals: 13 BIC: 151.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -348.1071 205.989 -1.690 0.115 -793.120 96.906
expression 42.0745 19.601 2.147 0.051 -0.270 84.419
Omnibus: 1.474 Durbin-Watson: 1.804
Prob(Omnibus): 0.478 Jarque-Bera (JB): 0.844
Skew: -0.095 Prob(JB): 0.656
Kurtosis: 1.854 Cond. No. 250.