LID SPN TRAP

1459476_s_at

Csrp2bp cysteine and glycine-rich protein 2 binding protein

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
0.285 0.599 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.746
Model: OLS Adj. R-squared: 0.706
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 7.01e-06
Time: 05:11:39 Log-Likelihood: -97.342
No. Observations: 23 AIC: 202.7
Df Residuals: 19 BIC: 207.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 231.3204 114.322 2.023 0.057 -7.957 470.598
C(dose)[T.1] -715.7731 292.086 -2.451 0.024 -1327.116 -104.430
expression -22.9410 14.792 -1.551 0.137 -53.901 8.019
expression:C(dose)[T.1] 103.1545 39.301 2.625 0.017 20.896 185.413
Omnibus: 2.023 Durbin-Watson: 1.712
Prob(Omnibus): 0.364 Jarque-Bera (JB): 1.717
Skew: -0.560 Prob(JB): 0.424
Kurtosis: 2.267 Cond. No. 669.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.654
Model: OLS Adj. R-squared: 0.619
Method: Least Squares F-statistic: 18.90
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.46e-05
Time: 05:11:39 Log-Likelihood: -100.90
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 118.5065 120.525 0.983 0.337 -132.905 369.918
C(dose)[T.1] 50.5052 10.195 4.954 0.000 29.239 71.771
expression -8.3284 15.592 -0.534 0.599 -40.853 24.196
Omnibus: 0.336 Durbin-Watson: 1.896
Prob(Omnibus): 0.845 Jarque-Bera (JB): 0.496
Skew: -0.099 Prob(JB): 0.780
Kurtosis: 2.308 Cond. No. 214.

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: 05:11:39 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: 0.229
Model: OLS Adj. R-squared: 0.193
Method: Least Squares F-statistic: 6.252
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0208
Time: 05:11:39 Log-Likelihood: -110.11
No. Observations: 23 AIC: 224.2
Df Residuals: 21 BIC: 226.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 446.2419 146.724 3.041 0.006 141.113 751.371
expression -48.4969 19.396 -2.500 0.021 -88.833 -8.161
Omnibus: 0.707 Durbin-Watson: 2.273
Prob(Omnibus): 0.702 Jarque-Bera (JB): 0.465
Skew: 0.334 Prob(JB): 0.793
Kurtosis: 2.804 Cond. No. 178.

CP101

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

F-statistic p-value df difference
0.680 0.426 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.479
Model: OLS Adj. R-squared: 0.337
Method: Least Squares F-statistic: 3.367
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0585
Time: 05:11:39 Log-Likelihood: -70.415
No. Observations: 15 AIC: 148.8
Df Residuals: 11 BIC: 151.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -180.6176 554.942 -0.325 0.751 -1402.037 1040.802
C(dose)[T.1] -7.1266 723.082 -0.010 0.992 -1598.619 1584.366
expression 28.6495 64.082 0.447 0.663 -112.394 169.693
expression:C(dose)[T.1] 7.1190 84.098 0.085 0.934 -177.980 192.218
Omnibus: 1.053 Durbin-Watson: 0.855
Prob(Omnibus): 0.591 Jarque-Bera (JB): 0.922
Skew: -0.491 Prob(JB): 0.631
Kurtosis: 2.285 Cond. No. 1.09e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.478
Model: OLS Adj. R-squared: 0.391
Method: Least Squares F-statistic: 5.502
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0202
Time: 05:11:39 Log-Likelihood: -70.419
No. Observations: 15 AIC: 146.8
Df Residuals: 12 BIC: 149.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -216.4053 344.291 -0.629 0.541 -966.552 533.741
C(dose)[T.1] 54.0659 16.410 3.295 0.006 18.311 89.821
expression 32.7831 39.745 0.825 0.426 -53.814 119.380
Omnibus: 1.055 Durbin-Watson: 0.868
Prob(Omnibus): 0.590 Jarque-Bera (JB): 0.921
Skew: -0.495 Prob(JB): 0.631
Kurtosis: 2.296 Cond. No. 393.

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: 05:11:39 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: 0.006
Model: OLS Adj. R-squared: -0.070
Method: Least Squares F-statistic: 0.08487
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.775
Time: 05:11:40 Log-Likelihood: -75.251
No. Observations: 15 AIC: 154.5
Df Residuals: 13 BIC: 155.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 216.5550 421.939 0.513 0.616 -694.989 1128.099
expression -14.3248 49.170 -0.291 0.775 -120.551 91.901
Omnibus: 0.316 Durbin-Watson: 1.603
Prob(Omnibus): 0.854 Jarque-Bera (JB): 0.459
Skew: -0.022 Prob(JB): 0.795
Kurtosis: 2.144 Cond. No. 362.