LID SPN TRAP

1435209_at

BC057079 cDNA sequence BC057079

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
0.455 0.507 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.671
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 12.89
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.93e-05
Time: 05:16:47 Log-Likelihood: -100.33
No. Observations: 23 AIC: 208.7
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.4039 56.349 1.090 0.289 -56.536 179.344
C(dose)[T.1] -16.1582 79.867 -0.202 0.842 -183.322 151.006
expression -1.2064 9.393 -0.128 0.899 -20.866 18.453
expression:C(dose)[T.1] 12.1165 13.611 0.890 0.384 -16.371 40.605
Omnibus: 0.296 Durbin-Watson: 1.602
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.468
Skew: -0.009 Prob(JB): 0.791
Kurtosis: 2.301 Cond. No. 142.

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: 19.14
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.26e-05
Time: 05:16:47 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 26.9859 40.779 0.662 0.516 -58.078 112.050
C(dose)[T.1] 54.4986 8.841 6.164 0.000 36.057 72.940
expression 4.5639 6.762 0.675 0.507 -9.542 18.670
Omnibus: 0.612 Durbin-Watson: 1.622
Prob(Omnibus): 0.737 Jarque-Bera (JB): 0.627
Skew: 0.026 Prob(JB): 0.731
Kurtosis: 2.193 Cond. No. 57.2

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: 05:16:47 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: 0.005
Model: OLS Adj. R-squared: -0.042
Method: Least Squares F-statistic: 0.1038
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.751
Time: 05:16:47 Log-Likelihood: -113.05
No. Observations: 23 AIC: 230.1
Df Residuals: 21 BIC: 232.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 100.4676 64.811 1.550 0.136 -34.315 235.250
expression -3.5513 11.023 -0.322 0.751 -26.476 19.373
Omnibus: 2.774 Durbin-Watson: 2.542
Prob(Omnibus): 0.250 Jarque-Bera (JB): 1.515
Skew: 0.325 Prob(JB): 0.469
Kurtosis: 1.924 Cond. No. 54.4

CP101

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

F-statistic p-value df difference
5.184 0.042 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.679
Model: OLS Adj. R-squared: 0.591
Method: Least Squares F-statistic: 7.757
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00465
Time: 05:16:47 Log-Likelihood: -66.777
No. Observations: 15 AIC: 141.6
Df Residuals: 11 BIC: 144.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 282.5813 220.195 1.283 0.226 -202.065 767.228
C(dose)[T.1] 613.2408 368.942 1.662 0.125 -198.795 1425.277
expression -32.3173 33.046 -0.978 0.349 -105.051 40.417
expression:C(dose)[T.1] -79.7951 53.895 -1.481 0.167 -198.418 38.827
Omnibus: 1.746 Durbin-Watson: 1.263
Prob(Omnibus): 0.418 Jarque-Bera (JB): 1.220
Skew: -0.471 Prob(JB): 0.543
Kurtosis: 1.969 Cond. No. 518.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.615
Model: OLS Adj. R-squared: 0.551
Method: Least Squares F-statistic: 9.587
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00325
Time: 05:16:47 Log-Likelihood: -68.140
No. Observations: 15 AIC: 142.3
Df Residuals: 12 BIC: 144.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 482.3044 182.476 2.643 0.021 84.723 879.886
C(dose)[T.1] 67.4327 15.400 4.379 0.001 33.879 100.986
expression -62.3170 27.371 -2.277 0.042 -121.954 -2.680
Omnibus: 1.295 Durbin-Watson: 1.013
Prob(Omnibus): 0.523 Jarque-Bera (JB): 0.944
Skew: -0.341 Prob(JB): 0.624
Kurtosis: 1.977 Cond. No. 195.

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: 05:16:47 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: 2.797e-07
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.00
Time: 05:16:47 Log-Likelihood: -75.300
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 93.5362 246.867 0.379 0.711 -439.787 626.859
expression 0.0191 36.201 0.001 1.000 -78.188 78.226
Omnibus: 0.582 Durbin-Watson: 1.622
Prob(Omnibus): 0.747 Jarque-Bera (JB): 0.573
Skew: 0.045 Prob(JB): 0.751
Kurtosis: 2.047 Cond. No. 169.