LID SPN TRAP

1453102_at

Flrt3 fibronectin leucine rich transmembrane protein 3

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
0.415 0.527 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.608
Method: Least Squares F-statistic: 12.35
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.000103
Time: 22:46:21 Log-Likelihood: -100.66
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 85.3938 219.411 0.389 0.701 -373.839 544.626
C(dose)[T.1] -79.2186 252.811 -0.313 0.757 -608.359 449.922
expression -3.5558 25.007 -0.142 0.888 -55.897 48.785
expression:C(dose)[T.1] 15.0562 28.772 0.523 0.607 -45.165 75.278
Omnibus: 0.237 Durbin-Watson: 2.023
Prob(Omnibus): 0.888 Jarque-Bera (JB): 0.431
Skew: -0.085 Prob(JB): 0.806
Kurtosis: 2.351 Cond. No. 741.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.656
Model: OLS Adj. R-squared: 0.622
Method: Least Squares F-statistic: 19.09
Date: Mon, 27 Jan 2025 Prob (F-statistic): 2.31e-05
Time: 22:46:21 Log-Likelihood: -100.83
No. Observations: 23 AIC: 207.7
Df Residuals: 20 BIC: 211.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -14.3582 106.651 -0.135 0.894 -236.827 208.111
C(dose)[T.1] 52.9929 8.697 6.093 0.000 34.852 71.134
expression 7.8179 12.141 0.644 0.527 -17.508 33.144
Omnibus: 0.061 Durbin-Watson: 1.838
Prob(Omnibus): 0.970 Jarque-Bera (JB): 0.284
Skew: 0.002 Prob(JB): 0.868
Kurtosis: 2.455 Cond. No. 220.

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: Mon, 27 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 22:46:21 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: 0.018
Model: OLS Adj. R-squared: -0.029
Method: Least Squares F-statistic: 0.3827
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.543
Time: 22:46:21 Log-Likelihood: -112.90
No. Observations: 23 AIC: 229.8
Df Residuals: 21 BIC: 232.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -28.9922 175.863 -0.165 0.871 -394.718 336.734
expression 12.3653 19.987 0.619 0.543 -29.200 53.931
Omnibus: 4.652 Durbin-Watson: 2.453
Prob(Omnibus): 0.098 Jarque-Bera (JB): 1.714
Skew: 0.229 Prob(JB): 0.424
Kurtosis: 1.744 Cond. No. 219.

CP101

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

F-statistic p-value df difference
6.297 0.027 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: 6.476
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.00872
Time: 22:46:21 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 497.4783 423.658 1.174 0.265 -434.988 1429.944
C(dose)[T.1] 54.8809 468.621 0.117 0.909 -976.546 1086.308
expression -56.1101 55.262 -1.015 0.332 -177.740 65.520
expression:C(dose)[T.1] 1.6400 60.659 0.027 0.979 -131.869 135.149
Omnibus: 2.298 Durbin-Watson: 0.999
Prob(Omnibus): 0.317 Jarque-Bera (JB): 1.033
Skew: -0.639 Prob(JB): 0.597
Kurtosis: 3.136 Cond. No. 869.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.638
Model: OLS Adj. R-squared: 0.578
Method: Least Squares F-statistic: 10.60
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.00223
Time: 22:46:21 Log-Likelihood: -67.669
No. Observations: 15 AIC: 141.3
Df Residuals: 12 BIC: 143.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 487.0459 167.480 2.908 0.013 122.139 851.953
C(dose)[T.1] 67.5440 14.695 4.596 0.001 35.527 99.561
expression -54.7490 21.818 -2.509 0.027 -102.286 -7.212
Omnibus: 2.377 Durbin-Watson: 0.995
Prob(Omnibus): 0.305 Jarque-Bera (JB): 1.064
Skew: -0.648 Prob(JB): 0.587
Kurtosis: 3.158 Cond. No. 211.

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: Mon, 27 Jan 2025 Prob (F-statistic): 0.00629
Time: 22:46:21 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: 0.002
Model: OLS Adj. R-squared: -0.075
Method: Least Squares F-statistic: 0.02578
Date: Mon, 27 Jan 2025 Prob (F-statistic): 0.875
Time: 22:46:21 Log-Likelihood: -75.285
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 131.7110 237.167 0.555 0.588 -380.657 644.079
expression -4.8507 30.211 -0.161 0.875 -70.118 60.417
Omnibus: 0.502 Durbin-Watson: 1.658
Prob(Omnibus): 0.778 Jarque-Bera (JB): 0.544
Skew: 0.067 Prob(JB): 0.762
Kurtosis: 2.076 Cond. No. 186.