LID SPN TRAP

1416589_at

Sparc secreted acidic cysteine rich glycoprotein

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
1.259 0.275 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.683
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 13.65
Date: Thu, 21 Nov 2024 Prob (F-statistic): 5.54e-05
Time: 03:46:35 Log-Likelihood: -99.889
No. Observations: 23 AIC: 207.8
Df Residuals: 19 BIC: 212.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -29.3209 58.998 -0.497 0.625 -152.805 94.163
C(dose)[T.1] 128.2150 89.742 1.429 0.169 -59.618 316.048
expression 15.2964 10.750 1.423 0.171 -7.203 37.796
expression:C(dose)[T.1] -13.8547 15.540 -0.892 0.384 -46.379 18.670
Omnibus: 0.110 Durbin-Watson: 2.129
Prob(Omnibus): 0.946 Jarque-Bera (JB): 0.335
Skew: -0.014 Prob(JB): 0.846
Kurtosis: 2.410 Cond. No. 160.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.637
Method: Least Squares F-statistic: 20.29
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.54e-05
Time: 03:46:35 Log-Likelihood: -100.36
No. Observations: 23 AIC: 206.7
Df Residuals: 20 BIC: 210.1
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 6.8833 42.580 0.162 0.873 -81.937 95.703
C(dose)[T.1] 48.6546 9.474 5.135 0.000 28.891 68.418
expression 8.6664 7.723 1.122 0.275 -7.443 24.776
Omnibus: 0.046 Durbin-Watson: 2.110
Prob(Omnibus): 0.977 Jarque-Bera (JB): 0.122
Skew: 0.074 Prob(JB): 0.941
Kurtosis: 2.675 Cond. No. 59.8

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: 03:46:35 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: 0.235
Model: OLS Adj. R-squared: 0.198
Method: Least Squares F-statistic: 6.433
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0192
Time: 03:46:35 Log-Likelihood: -110.03
No. Observations: 23 AIC: 224.1
Df Residuals: 21 BIC: 226.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -69.7374 59.262 -1.177 0.252 -192.980 53.505
expression 26.1324 10.303 2.536 0.019 4.706 47.559
Omnibus: 1.510 Durbin-Watson: 2.505
Prob(Omnibus): 0.470 Jarque-Bera (JB): 1.208
Skew: 0.534 Prob(JB): 0.547
Kurtosis: 2.652 Cond. No. 55.6

CP101

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

F-statistic p-value df difference
0.002 0.968 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.512
Model: OLS Adj. R-squared: 0.379
Method: Least Squares F-statistic: 3.846
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0418
Time: 03:46:35 Log-Likelihood: -69.920
No. Observations: 15 AIC: 147.8
Df Residuals: 11 BIC: 150.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 212.4071 170.548 1.245 0.239 -162.967 587.781
C(dose)[T.1] -225.8839 231.194 -0.977 0.350 -734.739 282.971
expression -22.0256 25.853 -0.852 0.412 -78.928 34.877
expression:C(dose)[T.1] 41.7540 35.015 1.192 0.258 -35.314 118.822
Omnibus: 1.561 Durbin-Watson: 1.239
Prob(Omnibus): 0.458 Jarque-Bera (JB): 0.988
Skew: -0.610 Prob(JB): 0.610
Kurtosis: 2.695 Cond. No. 274.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.886
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 03:46:35 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 62.5779 117.333 0.533 0.604 -193.069 318.225
C(dose)[T.1] 49.1873 15.740 3.125 0.009 14.893 83.482
expression 0.7369 17.740 0.042 0.968 -37.915 39.389
Omnibus: 2.663 Durbin-Watson: 0.816
Prob(Omnibus): 0.264 Jarque-Bera (JB): 1.835
Skew: -0.835 Prob(JB): 0.400
Kurtosis: 2.616 Cond. No. 101.

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: 03:46:35 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: 0.000
Model: OLS Adj. R-squared: -0.077
Method: Least Squares F-statistic: 0.004335
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.949
Time: 03:46:35 Log-Likelihood: -75.298
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 83.7098 151.569 0.552 0.590 -243.735 411.155
expression 1.5112 22.952 0.066 0.949 -48.074 51.096
Omnibus: 0.529 Durbin-Watson: 1.630
Prob(Omnibus): 0.768 Jarque-Bera (JB): 0.552
Skew: 0.034 Prob(JB): 0.759
Kurtosis: 2.063 Cond. No. 101.