LID SPN TRAP

1423551_at

Cdh13 cadherin 13

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
5.700 0.027 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.785
Model: OLS Adj. R-squared: 0.751
Method: Least Squares F-statistic: 23.11
Date: Sun, 09 Nov 2025 Prob (F-statistic): 1.48e-06
Time: 19:55:12 Log-Likelihood: -95.433
No. Observations: 23 AIC: 198.9
Df Residuals: 19 BIC: 203.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -108.1715 436.360 -0.248 0.807 -1021.483 805.140
C(dose)[T.1] 1231.1765 526.664 2.338 0.030 128.856 2333.497
expression 15.7942 42.441 0.372 0.714 -73.035 104.624
expression:C(dose)[T.1] -116.7618 51.582 -2.264 0.035 -224.725 -8.799
Omnibus: 0.597 Durbin-Watson: 1.771
Prob(Omnibus): 0.742 Jarque-Bera (JB): 0.077
Skew: 0.130 Prob(JB): 0.962
Kurtosis: 3.113 Cond. No. 2.16e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.727
Model: OLS Adj. R-squared: 0.700
Method: Least Squares F-statistic: 26.62
Date: Sun, 09 Nov 2025 Prob (F-statistic): 2.31e-06
Time: 19:55:12 Log-Likelihood: -98.179
No. Observations: 23 AIC: 202.4
Df Residuals: 20 BIC: 205.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 704.4722 272.419 2.586 0.018 136.216 1272.728
C(dose)[T.1] 39.1872 9.746 4.021 0.001 18.858 59.516
expression -63.2491 26.492 -2.387 0.027 -118.511 -7.987
Omnibus: 0.518 Durbin-Watson: 2.131
Prob(Omnibus): 0.772 Jarque-Bera (JB): 0.407
Skew: -0.295 Prob(JB): 0.816
Kurtosis: 2.725 Cond. No. 725.

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: Sun, 09 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 19:55:12 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: 0.506
Model: OLS Adj. R-squared: 0.483
Method: Least Squares F-statistic: 21.52
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.000141
Time: 19:55:12 Log-Likelihood: -104.99
No. Observations: 23 AIC: 214.0
Df Residuals: 21 BIC: 216.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 1382.3040 280.842 4.922 0.000 798.261 1966.347
expression -128.0309 27.599 -4.639 0.000 -185.427 -70.635
Omnibus: 2.193 Durbin-Watson: 2.144
Prob(Omnibus): 0.334 Jarque-Bera (JB): 1.740
Skew: -0.652 Prob(JB): 0.419
Kurtosis: 2.661 Cond. No. 569.

CP101

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

F-statistic p-value df difference
1.647 0.224 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.523
Model: OLS Adj. R-squared: 0.393
Method: Least Squares F-statistic: 4.016
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0372
Time: 19:55:13 Log-Likelihood: -69.753
No. Observations: 15 AIC: 147.5
Df Residuals: 11 BIC: 150.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -231.0495 236.432 -0.977 0.349 -751.434 289.335
C(dose)[T.1] 224.1830 445.290 0.503 0.625 -755.893 1204.259
expression 33.2096 26.277 1.264 0.232 -24.625 91.045
expression:C(dose)[T.1] -19.9705 48.225 -0.414 0.687 -126.113 86.172
Omnibus: 3.671 Durbin-Watson: 1.229
Prob(Omnibus): 0.160 Jarque-Bera (JB): 2.025
Skew: -0.898 Prob(JB): 0.363
Kurtosis: 3.123 Cond. No. 665.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.515
Model: OLS Adj. R-squared: 0.435
Method: Least Squares F-statistic: 6.378
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0130
Time: 19:55:13 Log-Likelihood: -69.869
No. Observations: 15 AIC: 145.7
Df Residuals: 12 BIC: 147.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -177.7605 191.376 -0.929 0.371 -594.733 239.212
C(dose)[T.1] 39.9188 16.435 2.429 0.032 4.110 75.728
expression 27.2805 21.259 1.283 0.224 -19.039 73.601
Omnibus: 4.673 Durbin-Watson: 1.069
Prob(Omnibus): 0.097 Jarque-Bera (JB): 2.471
Skew: -0.973 Prob(JB): 0.291
Kurtosis: 3.406 Cond. No. 242.

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: Sun, 09 Nov 2025 Prob (F-statistic): 0.00629
Time: 19:55:13 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: 0.277
Model: OLS Adj. R-squared: 0.221
Method: Least Squares F-statistic: 4.980
Date: Sun, 09 Nov 2025 Prob (F-statistic): 0.0439
Time: 19:55:13 Log-Likelihood: -72.868
No. Observations: 15 AIC: 149.7
Df Residuals: 13 BIC: 151.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -364.7447 205.591 -1.774 0.099 -808.897 79.407
expression 49.9954 22.402 2.232 0.044 1.598 98.393
Omnibus: 0.750 Durbin-Watson: 1.878
Prob(Omnibus): 0.687 Jarque-Bera (JB): 0.652
Skew: 0.140 Prob(JB): 0.722
Kurtosis: 2.018 Cond. No. 221.