LID SPN TRAP

1460041_at

Flrt1 fibronectin leucine rich transmembrane protein 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
0.013 0.910 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.594
Method: Least Squares F-statistic: 11.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000141
Time: 05:06:36 Log-Likelihood: -101.05
No. Observations: 23 AIC: 210.1
Df Residuals: 19 BIC: 214.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 59.9082 39.729 1.508 0.148 -23.245 143.061
C(dose)[T.1] 47.7346 56.452 0.846 0.408 -70.420 165.889
expression -1.1507 7.921 -0.145 0.886 -17.730 15.429
expression:C(dose)[T.1] 1.1280 12.140 0.093 0.927 -24.281 26.537
Omnibus: 0.258 Durbin-Watson: 1.924
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.446
Skew: 0.098 Prob(JB): 0.800
Kurtosis: 2.347 Cond. No. 77.7

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.51
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.82e-05
Time: 05:06:36 Log-Likelihood: -101.06
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 57.5292 29.616 1.943 0.066 -4.248 119.306
C(dose)[T.1] 52.9003 9.560 5.533 0.000 32.958 72.843
expression -0.6704 5.852 -0.115 0.910 -12.878 11.537
Omnibus: 0.203 Durbin-Watson: 1.904
Prob(Omnibus): 0.904 Jarque-Bera (JB): 0.407
Skew: 0.055 Prob(JB): 0.816
Kurtosis: 2.357 Cond. No. 33.8

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: 05:06:36 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: 0.112
Model: OLS Adj. R-squared: 0.070
Method: Least Squares F-statistic: 2.659
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.118
Time: 05:06:36 Log-Likelihood: -111.73
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 142.7815 39.267 3.636 0.002 61.121 224.442
expression -13.5858 8.331 -1.631 0.118 -30.912 3.740
Omnibus: 6.074 Durbin-Watson: 2.402
Prob(Omnibus): 0.048 Jarque-Bera (JB): 1.924
Skew: 0.238 Prob(JB): 0.382
Kurtosis: 1.665 Cond. No. 28.4

CP101

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

F-statistic p-value df difference
0.658 0.433 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.512
Model: OLS Adj. R-squared: 0.379
Method: Least Squares F-statistic: 3.852
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0416
Time: 05:06:36 Log-Likelihood: -69.915
No. Observations: 15 AIC: 147.8
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -161.0318 202.626 -0.795 0.444 -607.010 284.946
C(dose)[T.1] 243.1526 220.712 1.102 0.294 -242.632 728.937
expression 41.4977 36.748 1.129 0.283 -39.384 122.380
expression:C(dose)[T.1] -35.3568 39.867 -0.887 0.394 -123.104 52.391
Omnibus: 2.045 Durbin-Watson: 1.092
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.576
Skew: -0.722 Prob(JB): 0.455
Kurtosis: 2.340 Cond. No. 256.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.477
Model: OLS Adj. R-squared: 0.390
Method: Least Squares F-statistic: 5.482
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0204
Time: 05:06:36 Log-Likelihood: -70.433
No. Observations: 15 AIC: 146.9
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.3515 78.551 0.055 0.957 -166.795 175.498
C(dose)[T.1] 47.8979 15.408 3.109 0.009 14.326 81.470
expression 11.4574 14.122 0.811 0.433 -19.313 42.227
Omnibus: 2.155 Durbin-Watson: 0.718
Prob(Omnibus): 0.341 Jarque-Bera (JB): 1.659
Skew: -0.701 Prob(JB): 0.436
Kurtosis: 2.172 Cond. No. 59.5

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: 05:06:36 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: 0.057
Model: OLS Adj. R-squared: -0.016
Method: Least Squares F-statistic: 0.7804
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.393
Time: 05:06:36 Log-Likelihood: -74.863
No. Observations: 15 AIC: 153.7
Df Residuals: 13 BIC: 155.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 4.5161 101.400 0.045 0.965 -214.546 223.578
expression 16.0175 18.132 0.883 0.393 -23.154 55.189
Omnibus: 1.459 Durbin-Watson: 1.552
Prob(Omnibus): 0.482 Jarque-Bera (JB): 0.932
Skew: 0.268 Prob(JB): 0.628
Kurtosis: 1.903 Cond. No. 59.3