LID SPN TRAP

1459941_at

Clvs1 clavesin 1

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
1.228 0.281 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 12.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.21e-05
Time: 03:53:08 Log-Likelihood: -100.38
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -47.0757 135.633 -0.347 0.732 -330.958 236.807
C(dose)[T.1] 44.9288 195.409 0.230 0.821 -364.067 453.924
expression 10.9270 14.618 0.747 0.464 -19.669 41.523
expression:C(dose)[T.1] 0.7127 20.882 0.034 0.973 -42.994 44.419
Omnibus: 2.691 Durbin-Watson: 2.149
Prob(Omnibus): 0.260 Jarque-Bera (JB): 1.314
Skew: 0.190 Prob(JB): 0.518
Kurtosis: 1.892 Cond. No. 547.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.636
Method: Least Squares F-statistic: 20.24
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.56e-05
Time: 03:53:08 Log-Likelihood: -100.38
No. Observations: 23 AIC: 206.8
Df Residuals: 20 BIC: 210.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -50.3130 94.496 -0.532 0.600 -247.429 146.803
C(dose)[T.1] 51.5911 8.657 5.960 0.000 33.533 69.649
expression 11.2763 10.175 1.108 0.281 -9.948 32.501
Omnibus: 2.672 Durbin-Watson: 2.150
Prob(Omnibus): 0.263 Jarque-Bera (JB): 1.307
Skew: 0.186 Prob(JB): 0.520
Kurtosis: 1.894 Cond. No. 211.

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: 03:53:08 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: 0.082
Model: OLS Adj. R-squared: 0.039
Method: Least Squares F-statistic: 1.881
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.185
Time: 03:53:08 Log-Likelihood: -112.12
No. Observations: 23 AIC: 228.2
Df Residuals: 21 BIC: 230.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -128.7447 152.146 -0.846 0.407 -445.149 187.660
expression 22.3117 16.267 1.372 0.185 -11.518 56.142
Omnibus: 2.220 Durbin-Watson: 2.711
Prob(Omnibus): 0.330 Jarque-Bera (JB): 1.339
Skew: 0.300 Prob(JB): 0.512
Kurtosis: 1.982 Cond. No. 208.

CP101

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

F-statistic p-value df difference
0.009 0.925 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.302
Method: Least Squares F-statistic: 3.021
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0756
Time: 03:53:08 Log-Likelihood: -70.793
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 115.2603 212.417 0.543 0.598 -352.266 582.786
C(dose)[T.1] -16.4217 287.859 -0.057 0.956 -649.995 617.152
expression -6.2967 27.918 -0.226 0.826 -67.745 55.151
expression:C(dose)[T.1] 8.7934 38.996 0.225 0.826 -77.036 94.623
Omnibus: 3.095 Durbin-Watson: 0.768
Prob(Omnibus): 0.213 Jarque-Bera (JB): 2.091
Skew: -0.901 Prob(JB): 0.352
Kurtosis: 2.681 Cond. No. 352.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.893
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 03:53:08 Log-Likelihood: -70.827
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 81.0222 142.554 0.568 0.580 -229.577 391.621
C(dose)[T.1] 48.3507 18.047 2.679 0.020 9.030 87.671
expression -1.7895 18.705 -0.096 0.925 -42.544 38.965
Omnibus: 2.704 Durbin-Watson: 0.808
Prob(Omnibus): 0.259 Jarque-Bera (JB): 1.857
Skew: -0.841 Prob(JB): 0.395
Kurtosis: 2.625 Cond. No. 137.

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: 03:53:08 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: 0.120
Model: OLS Adj. R-squared: 0.052
Method: Least Squares F-statistic: 1.768
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.206
Time: 03:53:08 Log-Likelihood: -74.344
No. Observations: 15 AIC: 152.7
Df Residuals: 13 BIC: 154.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 287.0899 145.781 1.969 0.071 -27.851 602.030
expression -26.3364 19.807 -1.330 0.206 -69.127 16.454
Omnibus: 0.199 Durbin-Watson: 1.331
Prob(Omnibus): 0.905 Jarque-Bera (JB): 0.116
Skew: 0.152 Prob(JB): 0.944
Kurtosis: 2.696 Cond. No. 115.