LID SPN TRAP

1427975_at

Rasl10a RAS-like, family 10, 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
0.075 0.787 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: 05:10:21 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 25.4676 126.384 0.202 0.842 -239.058 289.993
C(dose)[T.1] 65.1272 168.537 0.386 0.703 -287.626 417.880
expression 4.3333 19.032 0.228 0.822 -35.501 44.168
expression:C(dose)[T.1] -1.6258 26.044 -0.062 0.951 -56.137 52.886
Omnibus: 0.464 Durbin-Watson: 1.914
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.559
Skew: 0.023 Prob(JB): 0.756
Kurtosis: 2.238 Cond. No. 328.

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: 18.60
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.73e-05
Time: 05:10:21 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 31.2261 84.215 0.371 0.715 -144.443 206.895
C(dose)[T.1] 54.6253 9.939 5.496 0.000 33.892 75.358
expression 3.4651 12.664 0.274 0.787 -22.952 29.882
Omnibus: 0.413 Durbin-Watson: 1.911
Prob(Omnibus): 0.813 Jarque-Bera (JB): 0.534
Skew: 0.032 Prob(JB): 0.766
Kurtosis: 2.256 Cond. No. 128.

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: 05:10:21 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: 0.122
Model: OLS Adj. R-squared: 0.081
Method: Least Squares F-statistic: 2.927
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.102
Time: 05:10:21 Log-Likelihood: -111.60
No. Observations: 23 AIC: 227.2
Df Residuals: 21 BIC: 229.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 270.1632 111.519 2.423 0.025 38.248 502.079
expression -29.5047 17.245 -1.711 0.102 -65.368 6.359
Omnibus: 5.054 Durbin-Watson: 2.231
Prob(Omnibus): 0.080 Jarque-Bera (JB): 1.955
Skew: 0.335 Prob(JB): 0.376
Kurtosis: 1.739 Cond. No. 109.

CP101

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

F-statistic p-value df difference
4.512 0.055 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.639
Model: OLS Adj. R-squared: 0.540
Method: Least Squares F-statistic: 6.477
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00872
Time: 05:10:21 Log-Likelihood: -67.669
No. Observations: 15 AIC: 143.3
Df Residuals: 11 BIC: 146.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -156.5737 234.652 -0.667 0.518 -673.040 359.892
C(dose)[T.1] -340.9121 364.317 -0.936 0.369 -1142.769 460.945
expression 31.2876 32.747 0.955 0.360 -40.788 103.363
expression:C(dose)[T.1] 56.0861 51.409 1.091 0.299 -57.064 169.236
Omnibus: 0.964 Durbin-Watson: 1.058
Prob(Omnibus): 0.617 Jarque-Bera (JB): 0.845
Skew: -0.482 Prob(JB): 0.655
Kurtosis: 2.349 Cond. No. 506.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.599
Model: OLS Adj. R-squared: 0.533
Method: Least Squares F-statistic: 8.978
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00413
Time: 05:10:21 Log-Likelihood: -68.439
No. Observations: 15 AIC: 142.9
Df Residuals: 12 BIC: 145.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -319.5054 182.421 -1.751 0.105 -716.967 77.956
C(dose)[T.1] 56.2712 13.825 4.070 0.002 26.149 86.393
expression 54.0451 25.443 2.124 0.055 -1.390 109.481
Omnibus: 1.921 Durbin-Watson: 1.157
Prob(Omnibus): 0.383 Jarque-Bera (JB): 1.006
Skew: -0.219 Prob(JB): 0.605
Kurtosis: 1.809 Cond. No. 198.

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: 05:10:21 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: 0.046
Model: OLS Adj. R-squared: -0.027
Method: Least Squares F-statistic: 0.6318
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.441
Time: 05:10:21 Log-Likelihood: -74.944
No. Observations: 15 AIC: 153.9
Df Residuals: 13 BIC: 155.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -112.6195 259.708 -0.434 0.672 -673.684 448.445
expression 29.0968 36.605 0.795 0.441 -49.984 108.178
Omnibus: 2.147 Durbin-Watson: 1.898
Prob(Omnibus): 0.342 Jarque-Bera (JB): 1.022
Skew: 0.170 Prob(JB): 0.600
Kurtosis: 1.767 Cond. No. 189.