LID SPN TRAP

1426241_a_at

Scmh1 sex comb on midleg homolog 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
2.676 0.118 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.691
Model: OLS Adj. R-squared: 0.643
Method: Least Squares F-statistic: 14.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.34e-05
Time: 05:01:28 Log-Likelihood: -99.590
No. Observations: 23 AIC: 207.2
Df Residuals: 19 BIC: 211.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 183.3973 96.192 1.907 0.072 -17.934 384.729
C(dose)[T.1] 14.5648 140.317 0.104 0.918 -279.122 308.251
expression -19.4192 14.432 -1.346 0.194 -49.627 10.788
expression:C(dose)[T.1] 4.7941 21.917 0.219 0.829 -41.079 50.668
Omnibus: 2.178 Durbin-Watson: 2.012
Prob(Omnibus): 0.336 Jarque-Bera (JB): 1.109
Skew: -0.032 Prob(JB): 0.574
Kurtosis: 1.926 Cond. No. 277.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.690
Model: OLS Adj. R-squared: 0.660
Method: Least Squares F-statistic: 22.31
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.07e-06
Time: 05:01:28 Log-Likelihood: -99.619
No. Observations: 23 AIC: 205.2
Df Residuals: 20 BIC: 208.6
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 169.5679 70.748 2.397 0.026 21.991 317.145
C(dose)[T.1] 45.1808 9.628 4.693 0.000 25.098 65.264
expression -17.3404 10.600 -1.636 0.118 -39.452 4.771
Omnibus: 2.146 Durbin-Watson: 2.023
Prob(Omnibus): 0.342 Jarque-Bera (JB): 1.101
Skew: -0.033 Prob(JB): 0.577
Kurtosis: 1.930 Cond. No. 114.

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: 05:01:28 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: 0.350
Model: OLS Adj. R-squared: 0.319
Method: Least Squares F-statistic: 11.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00296
Time: 05:01:28 Log-Likelihood: -108.16
No. Observations: 23 AIC: 220.3
Df Residuals: 21 BIC: 222.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 356.7567 82.655 4.316 0.000 184.866 528.648
expression -43.1009 12.827 -3.360 0.003 -69.777 -16.425
Omnibus: 1.606 Durbin-Watson: 2.385
Prob(Omnibus): 0.448 Jarque-Bera (JB): 1.044
Skew: 0.191 Prob(JB): 0.593
Kurtosis: 2.028 Cond. No. 93.7

CP101

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

F-statistic p-value df difference
1.794 0.205 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.716
Model: OLS Adj. R-squared: 0.638
Method: Least Squares F-statistic: 9.235
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00243
Time: 05:01:28 Log-Likelihood: -65.864
No. Observations: 15 AIC: 139.7
Df Residuals: 11 BIC: 142.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 180.5040 130.818 1.380 0.195 -107.424 468.432
C(dose)[T.1] -444.3667 179.673 -2.473 0.031 -839.824 -48.909
expression -14.9944 17.309 -0.866 0.405 -53.092 23.103
expression:C(dose)[T.1] 65.2450 23.729 2.750 0.019 13.018 117.472
Omnibus: 0.281 Durbin-Watson: 1.286
Prob(Omnibus): 0.869 Jarque-Bera (JB): 0.445
Skew: 0.142 Prob(JB): 0.800
Kurtosis: 2.205 Cond. No. 317.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.520
Model: OLS Adj. R-squared: 0.441
Method: Least Squares F-statistic: 6.512
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0122
Time: 05:01:28 Log-Likelihood: -69.788
No. Observations: 15 AIC: 145.6
Df Residuals: 12 BIC: 147.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -81.3117 111.559 -0.729 0.480 -324.379 161.755
C(dose)[T.1] 48.5922 14.687 3.308 0.006 16.591 80.593
expression 19.7238 14.725 1.339 0.205 -12.359 51.807
Omnibus: 2.429 Durbin-Watson: 1.027
Prob(Omnibus): 0.297 Jarque-Bera (JB): 1.319
Skew: -0.726 Prob(JB): 0.517
Kurtosis: 2.951 Cond. No. 118.

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: 05:01:28 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: 0.083
Model: OLS Adj. R-squared: 0.013
Method: Least Squares F-statistic: 1.178
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.298
Time: 05:01:28 Log-Likelihood: -74.650
No. Observations: 15 AIC: 153.3
Df Residuals: 13 BIC: 154.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -66.7031 148.097 -0.450 0.660 -386.647 253.241
expression 21.2199 19.554 1.085 0.298 -21.023 63.463
Omnibus: 1.015 Durbin-Watson: 1.733
Prob(Omnibus): 0.602 Jarque-Bera (JB): 0.410
Skew: -0.403 Prob(JB): 0.814
Kurtosis: 2.918 Cond. No. 117.