LID SPN TRAP

1459858_x_at

Crtac1 cartilage acidic 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
0.078 0.783 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.651
Model: OLS Adj. R-squared: 0.596
Method: Least Squares F-statistic: 11.82
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000135
Time: 05:16:20 Log-Likelihood: -100.99
No. Observations: 23 AIC: 210.0
Df Residuals: 19 BIC: 214.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 52.8822 137.144 0.386 0.704 -234.164 339.929
C(dose)[T.1] 17.0233 176.395 0.097 0.924 -352.176 386.223
expression 0.2278 23.533 0.010 0.992 -49.027 49.482
expression:C(dose)[T.1] 6.1069 30.020 0.203 0.841 -56.725 68.939
Omnibus: 0.126 Durbin-Watson: 1.915
Prob(Omnibus): 0.939 Jarque-Bera (JB): 0.345
Skew: 0.059 Prob(JB): 0.842
Kurtosis: 2.412 Cond. No. 326.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.73e-05
Time: 05:16:20 Log-Likelihood: -101.02
No. Observations: 23 AIC: 208.0
Df Residuals: 20 BIC: 211.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 31.0338 83.218 0.373 0.713 -142.557 204.625
C(dose)[T.1] 52.8593 8.919 5.927 0.000 34.256 71.463
expression 3.9806 14.256 0.279 0.783 -25.758 33.719
Omnibus: 0.212 Durbin-Watson: 1.900
Prob(Omnibus): 0.899 Jarque-Bera (JB): 0.414
Skew: 0.008 Prob(JB): 0.813
Kurtosis: 2.343 Cond. No. 116.

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: 05:16:20 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: 0.036
Model: OLS Adj. R-squared: -0.009
Method: Least Squares F-statistic: 0.7935
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.383
Time: 05:16:20 Log-Likelihood: -112.68
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -39.0077 133.467 -0.292 0.773 -316.567 238.552
expression 20.1939 22.669 0.891 0.383 -26.950 67.337
Omnibus: 1.021 Durbin-Watson: 2.576
Prob(Omnibus): 0.600 Jarque-Bera (JB): 0.863
Skew: 0.209 Prob(JB): 0.650
Kurtosis: 2.148 Cond. No. 114.

CP101

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

F-statistic p-value df difference
0.575 0.463 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.331
Method: Least Squares F-statistic: 3.309
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0611
Time: 05:16:20 Log-Likelihood: -70.476
No. Observations: 15 AIC: 149.0
Df Residuals: 11 BIC: 151.8
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -17.0768 168.201 -0.102 0.921 -387.285 353.132
C(dose)[T.1] 68.9683 208.013 0.332 0.746 -388.866 526.802
expression 16.8521 33.461 0.504 0.624 -56.796 90.500
expression:C(dose)[T.1] -3.7689 41.552 -0.091 0.929 -95.224 87.686
Omnibus: 3.292 Durbin-Watson: 0.883
Prob(Omnibus): 0.193 Jarque-Bera (JB): 2.280
Skew: -0.938 Prob(JB): 0.320
Kurtosis: 2.639 Cond. No. 192.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.474
Model: OLS Adj. R-squared: 0.386
Method: Least Squares F-statistic: 5.406
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0212
Time: 05:16:20 Log-Likelihood: -70.482
No. Observations: 15 AIC: 147.0
Df Residuals: 12 BIC: 149.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -4.8208 95.941 -0.050 0.961 -213.858 204.216
C(dose)[T.1] 50.1573 15.428 3.251 0.007 16.543 83.771
expression 14.4080 19.001 0.758 0.463 -26.992 55.808
Omnibus: 3.300 Durbin-Watson: 0.849
Prob(Omnibus): 0.192 Jarque-Bera (JB): 2.273
Skew: -0.938 Prob(JB): 0.321
Kurtosis: 2.654 Cond. No. 65.3

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: 05:16:20 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: 0.011
Model: OLS Adj. R-squared: -0.065
Method: Least Squares F-statistic: 0.1399
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.714
Time: 05:16:20 Log-Likelihood: -75.220
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 47.1935 124.644 0.379 0.711 -222.083 316.470
expression 9.3339 24.952 0.374 0.714 -44.571 63.239
Omnibus: 0.831 Durbin-Watson: 1.739
Prob(Omnibus): 0.660 Jarque-Bera (JB): 0.657
Skew: 0.032 Prob(JB): 0.720
Kurtosis: 1.977 Cond. No. 64.1