LID SPN TRAP

1460444_at

Arrb1 arrestin, beta 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
0.076 0.786 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.653
Model: OLS Adj. R-squared: 0.598
Method: Least Squares F-statistic: 11.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000130
Time: 04:31:03 Log-Likelihood: -100.95
No. Observations: 23 AIC: 209.9
Df Residuals: 19 BIC: 214.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 267.2841 486.554 0.549 0.589 -751.086 1285.654
C(dose)[T.1] -175.7128 654.983 -0.268 0.791 -1546.608 1195.182
expression -19.3341 44.145 -0.438 0.666 -111.732 73.063
expression:C(dose)[T.1] 20.8103 59.921 0.347 0.732 -104.606 146.226
Omnibus: 0.210 Durbin-Watson: 1.966
Prob(Omnibus): 0.900 Jarque-Bera (JB): 0.413
Skew: 0.030 Prob(JB): 0.814
Kurtosis: 2.346 Cond. No. 2.13e+03

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: 04:31:03 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 142.8030 321.720 0.444 0.662 -528.292 813.898
C(dose)[T.1] 51.7297 10.520 4.917 0.000 29.785 73.675
expression -8.0389 29.187 -0.275 0.786 -68.922 52.844
Omnibus: 0.251 Durbin-Watson: 1.920
Prob(Omnibus): 0.882 Jarque-Bera (JB): 0.440
Skew: 0.050 Prob(JB): 0.802
Kurtosis: 2.329 Cond. No. 812.

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: 04:31:03 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: 0.228
Model: OLS Adj. R-squared: 0.191
Method: Least Squares F-statistic: 6.193
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0213
Time: 04:31:03 Log-Likelihood: -110.13
No. Observations: 23 AIC: 224.3
Df Residuals: 21 BIC: 226.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1037.3234 384.860 2.695 0.014 236.962 1837.685
expression -87.6519 35.222 -2.489 0.021 -160.901 -14.403
Omnibus: 1.479 Durbin-Watson: 2.397
Prob(Omnibus): 0.477 Jarque-Bera (JB): 0.983
Skew: 0.160 Prob(JB): 0.612
Kurtosis: 2.039 Cond. No. 669.

CP101

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

F-statistic p-value df difference
0.196 0.666 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.587
Model: OLS Adj. R-squared: 0.475
Method: Least Squares F-statistic: 5.219
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0175
Time: 04:31:03 Log-Likelihood: -68.661
No. Observations: 15 AIC: 145.3
Df Residuals: 11 BIC: 148.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1361.6097 1162.183 1.172 0.266 -1196.338 3919.557
C(dose)[T.1] -2763.6681 1513.471 -1.826 0.095 -6094.795 567.459
expression -109.7334 98.537 -1.114 0.289 -326.613 107.146
expression:C(dose)[T.1] 238.9590 128.508 1.859 0.090 -43.886 521.804
Omnibus: 0.780 Durbin-Watson: 1.327
Prob(Omnibus): 0.677 Jarque-Bera (JB): 0.679
Skew: -0.440 Prob(JB): 0.712
Kurtosis: 2.442 Cond. No. 3.50e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.458
Model: OLS Adj. R-squared: 0.367
Method: Least Squares F-statistic: 5.063
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0255
Time: 04:31:03 Log-Likelihood: -70.711
No. Observations: 15 AIC: 147.4
Df Residuals: 12 BIC: 149.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -295.3799 818.904 -0.361 0.725 -2079.618 1488.858
C(dose)[T.1] 50.4783 15.878 3.179 0.008 15.883 85.074
expression 30.7625 69.428 0.443 0.666 -120.508 182.033
Omnibus: 1.831 Durbin-Watson: 0.727
Prob(Omnibus): 0.400 Jarque-Bera (JB): 1.364
Skew: -0.688 Prob(JB): 0.506
Kurtosis: 2.465 Cond. No. 1.25e+03

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: 04:31:03 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: 0.001
Model: OLS Adj. R-squared: -0.076
Method: Least Squares F-statistic: 0.01128
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.917
Time: 04:31:03 Log-Likelihood: -75.294
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 204.9474 1047.973 0.196 0.848 -2059.060 2468.955
expression -9.4533 89.021 -0.106 0.917 -201.772 182.865
Omnibus: 0.686 Durbin-Watson: 1.651
Prob(Omnibus): 0.710 Jarque-Bera (JB): 0.613
Skew: 0.065 Prob(JB): 0.736
Kurtosis: 2.018 Cond. No. 1.22e+03