LID SPN TRAP

1433657_at

Fam78a family with sequence similarity 78, member A

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
0.956 0.340 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.668
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.76
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.45e-05
Time: 04:42:14 Log-Likelihood: -100.41
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.2937 105.580 0.041 0.968 -216.688 225.275
C(dose)[T.1] -17.2190 166.287 -0.104 0.919 -365.263 330.825
expression 6.0426 12.760 0.474 0.641 -20.665 32.750
expression:C(dose)[T.1] 8.8299 20.341 0.434 0.669 -33.744 51.404
Omnibus: 0.564 Durbin-Watson: 2.065
Prob(Omnibus): 0.754 Jarque-Bera (JB): 0.612
Skew: 0.074 Prob(JB): 0.736
Kurtosis: 2.215 Cond. No. 392.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.665
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 19.86
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.78e-05
Time: 04:42:14 Log-Likelihood: -100.53
No. Observations: 23 AIC: 207.1
Df Residuals: 20 BIC: 210.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -24.4102 80.622 -0.303 0.765 -192.584 143.764
C(dose)[T.1] 54.8619 8.708 6.300 0.000 36.697 73.027
expression 9.5175 9.734 0.978 0.340 -10.786 29.821
Omnibus: 1.218 Durbin-Watson: 2.058
Prob(Omnibus): 0.544 Jarque-Bera (JB): 0.849
Skew: -0.021 Prob(JB): 0.654
Kurtosis: 2.060 Cond. No. 157.

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: 04:42:14 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: 0.000
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.008219
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.929
Time: 04:42:15 Log-Likelihood: -113.10
No. Observations: 23 AIC: 230.2
Df Residuals: 21 BIC: 232.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 91.6958 132.324 0.693 0.496 -183.487 366.879
expression -1.4637 16.145 -0.091 0.929 -35.039 32.112
Omnibus: 3.497 Durbin-Watson: 2.470
Prob(Omnibus): 0.174 Jarque-Bera (JB): 1.613
Skew: 0.292 Prob(JB): 0.447
Kurtosis: 1.842 Cond. No. 153.

CP101

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

F-statistic p-value df difference
0.156 0.700 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.471
Model: OLS Adj. R-squared: 0.327
Method: Least Squares F-statistic: 3.265
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0631
Time: 04:42:15 Log-Likelihood: -70.524
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 49.0026 293.704 0.167 0.871 -597.436 695.441
C(dose)[T.1] -269.4526 578.051 -0.466 0.650 -1541.735 1002.830
expression 1.8864 30.045 0.063 0.951 -64.242 68.015
expression:C(dose)[T.1] 34.7931 61.939 0.562 0.586 -101.534 171.120
Omnibus: 1.073 Durbin-Watson: 1.081
Prob(Omnibus): 0.585 Jarque-Bera (JB): 0.913
Skew: -0.512 Prob(JB): 0.634
Kurtosis: 2.357 Cond. No. 821.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.456
Model: OLS Adj. R-squared: 0.365
Method: Least Squares F-statistic: 5.026
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0260
Time: 04:42:15 Log-Likelihood: -70.736
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 -30.9616 249.466 -0.124 0.903 -574.501 512.578
C(dose)[T.1] 55.0176 21.493 2.560 0.025 8.189 101.847
expression 10.0731 25.513 0.395 0.700 -45.516 65.662
Omnibus: 1.959 Durbin-Watson: 0.928
Prob(Omnibus): 0.376 Jarque-Bera (JB): 1.458
Skew: -0.714 Prob(JB): 0.482
Kurtosis: 2.456 Cond. No. 307.

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: 04:42:15 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: 0.159
Model: OLS Adj. R-squared: 0.094
Method: Least Squares F-statistic: 2.452
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.141
Time: 04:42:15 Log-Likelihood: -74.004
No. Observations: 15 AIC: 152.0
Df Residuals: 13 BIC: 153.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 422.1777 209.984 2.011 0.066 -31.466 875.821
expression -34.7285 22.177 -1.566 0.141 -82.638 13.181
Omnibus: 0.804 Durbin-Watson: 1.272
Prob(Omnibus): 0.669 Jarque-Bera (JB): 0.757
Skew: -0.341 Prob(JB): 0.685
Kurtosis: 2.136 Cond. No. 216.