LID SPN TRAP

1457040_at

Lgi2 leucine-rich repeat LGI family, member 2

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
0.295 0.593 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000113
Time: 03:39:33 Log-Likelihood: -100.77
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 58.7538 144.137 0.408 0.688 -242.928 360.436
C(dose)[T.1] 151.0507 205.644 0.735 0.472 -279.367 581.469
expression -0.5551 17.587 -0.032 0.975 -37.366 36.255
expression:C(dose)[T.1] -10.8756 24.034 -0.453 0.656 -61.179 39.428
Omnibus: 0.912 Durbin-Watson: 1.719
Prob(Omnibus): 0.634 Jarque-Bera (JB): 0.801
Skew: 0.181 Prob(JB): 0.670
Kurtosis: 2.160 Cond. No. 530.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.620
Method: Least Squares F-statistic: 18.91
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.45e-05
Time: 03:39:33 Log-Likelihood: -100.89
No. Observations: 23 AIC: 207.8
Df Residuals: 20 BIC: 211.2
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 106.4397 96.364 1.105 0.282 -94.573 307.452
C(dose)[T.1] 58.1715 12.451 4.672 0.000 32.199 84.144
expression -6.3790 11.746 -0.543 0.593 -30.880 18.122
Omnibus: 0.451 Durbin-Watson: 1.682
Prob(Omnibus): 0.798 Jarque-Bera (JB): 0.571
Skew: 0.151 Prob(JB): 0.751
Kurtosis: 2.290 Cond. No. 194.

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: 03:39:33 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: 0.277
Model: OLS Adj. R-squared: 0.242
Method: Least Squares F-statistic: 8.034
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00993
Time: 03:39:33 Log-Likelihood: -109.38
No. Observations: 23 AIC: 222.8
Df Residuals: 21 BIC: 225.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -201.1964 99.296 -2.026 0.056 -407.693 5.300
expression 32.8534 11.591 2.834 0.010 8.749 56.957
Omnibus: 3.844 Durbin-Watson: 2.615
Prob(Omnibus): 0.146 Jarque-Bera (JB): 1.441
Skew: 0.060 Prob(JB): 0.487
Kurtosis: 1.780 Cond. No. 140.

CP101

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

F-statistic p-value df difference
1.938 0.189 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.531
Model: OLS Adj. R-squared: 0.402
Method: Least Squares F-statistic: 4.143
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0342
Time: 03:39:33 Log-Likelihood: -69.629
No. Observations: 15 AIC: 147.3
Df Residuals: 11 BIC: 150.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -217.1455 236.251 -0.919 0.378 -737.131 302.840
C(dose)[T.1] 157.3983 351.329 0.448 0.663 -615.871 930.668
expression 32.6092 27.042 1.206 0.253 -26.910 92.129
expression:C(dose)[T.1] -13.4878 39.048 -0.345 0.736 -99.433 72.457
Omnibus: 2.334 Durbin-Watson: 0.833
Prob(Omnibus): 0.311 Jarque-Bera (JB): 1.795
Skew: -0.754 Prob(JB): 0.408
Kurtosis: 2.228 Cond. No. 557.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.525
Model: OLS Adj. R-squared: 0.446
Method: Least Squares F-statistic: 6.643
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0114
Time: 03:39:33 Log-Likelihood: -69.710
No. Observations: 15 AIC: 145.4
Df Residuals: 12 BIC: 147.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -160.6943 164.222 -0.979 0.347 -518.504 197.115
C(dose)[T.1] 36.2040 17.332 2.089 0.059 -1.559 73.968
expression 26.1405 18.778 1.392 0.189 -14.774 67.055
Omnibus: 2.492 Durbin-Watson: 0.793
Prob(Omnibus): 0.288 Jarque-Bera (JB): 1.907
Skew: -0.785 Prob(JB): 0.385
Kurtosis: 2.232 Cond. No. 206.

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: 03:39:33 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: 0.353
Model: OLS Adj. R-squared: 0.303
Method: Least Squares F-statistic: 7.088
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0195
Time: 03:39:33 Log-Likelihood: -72.036
No. Observations: 15 AIC: 148.1
Df Residuals: 13 BIC: 149.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -331.3167 159.837 -2.073 0.059 -676.624 13.990
expression 47.2631 17.752 2.662 0.020 8.911 85.615
Omnibus: 1.373 Durbin-Watson: 1.322
Prob(Omnibus): 0.503 Jarque-Bera (JB): 0.806
Skew: 0.006 Prob(JB): 0.668
Kurtosis: 1.865 Cond. No. 178.