LID SPN TRAP

1448724_at

Cish cytokine inducible SH2-containing 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
0.501 0.487 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.669
Model: OLS Adj. R-squared: 0.616
Method: Least Squares F-statistic: 12.79
Date: Thu, 21 Nov 2024 Prob (F-statistic): 8.35e-05
Time: 03:58:51 Log-Likelihood: -100.40
No. Observations: 23 AIC: 208.8
Df Residuals: 19 BIC: 213.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -38.6650 99.804 -0.387 0.703 -247.557 170.227
C(dose)[T.1] 286.1798 291.771 0.981 0.339 -324.503 896.863
expression 11.5111 12.347 0.932 0.363 -14.332 37.354
expression:C(dose)[T.1] -28.8286 36.090 -0.799 0.434 -104.366 46.709
Omnibus: 0.460 Durbin-Watson: 1.899
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.582
Skew: 0.178 Prob(JB): 0.748
Kurtosis: 2.307 Cond. No. 626.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.623
Method: Least Squares F-statistic: 19.21
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.21e-05
Time: 03:58:51 Log-Likelihood: -100.78
No. Observations: 23 AIC: 207.6
Df Residuals: 20 BIC: 211.0
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -11.4400 92.951 -0.123 0.903 -205.333 182.453
C(dose)[T.1] 53.2205 8.664 6.143 0.000 35.148 71.292
expression 8.1367 11.497 0.708 0.487 -15.845 32.119
Omnibus: 0.017 Durbin-Watson: 1.907
Prob(Omnibus): 0.992 Jarque-Bera (JB): 0.137
Skew: -0.049 Prob(JB): 0.934
Kurtosis: 2.634 Cond. No. 177.

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: 03:58:51 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: 0.012
Model: OLS Adj. R-squared: -0.035
Method: Least Squares F-statistic: 0.2474
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.624
Time: 03:58:51 Log-Likelihood: -112.97
No. Observations: 23 AIC: 229.9
Df Residuals: 21 BIC: 232.2
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 3.1630 154.074 0.021 0.984 -317.251 323.577
expression 9.4804 19.060 0.497 0.624 -30.156 49.117
Omnibus: 3.375 Durbin-Watson: 2.456
Prob(Omnibus): 0.185 Jarque-Bera (JB): 1.632
Skew: 0.318 Prob(JB): 0.442
Kurtosis: 1.860 Cond. No. 176.

CP101

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

F-statistic p-value df difference
0.007 0.934 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.455
Model: OLS Adj. R-squared: 0.307
Method: Least Squares F-statistic: 3.063
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0733
Time: 03:58:51 Log-Likelihood: -70.746
No. Observations: 15 AIC: 149.5
Df Residuals: 11 BIC: 152.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 46.9586 171.095 0.274 0.789 -329.619 423.537
C(dose)[T.1] 162.2380 322.677 0.503 0.625 -547.969 872.445
expression 2.8241 23.548 0.120 0.907 -49.004 54.652
expression:C(dose)[T.1] -15.3277 43.792 -0.350 0.733 -111.712 81.057
Omnibus: 2.609 Durbin-Watson: 0.969
Prob(Omnibus): 0.271 Jarque-Bera (JB): 1.795
Skew: -0.825 Prob(JB): 0.408
Kurtosis: 2.616 Cond. No. 362.

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: 4.891
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 03:58:51 Log-Likelihood: -70.829
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 79.0822 139.017 0.569 0.580 -223.810 381.974
C(dose)[T.1] 49.4463 16.013 3.088 0.009 14.557 84.336
expression -1.6078 19.114 -0.084 0.934 -43.253 40.038
Omnibus: 2.615 Durbin-Watson: 0.791
Prob(Omnibus): 0.270 Jarque-Bera (JB): 1.814
Skew: -0.828 Prob(JB): 0.404
Kurtosis: 2.600 Cond. No. 133.

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: 03:58:51 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: 0.1494
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.705
Time: 03:58:51 Log-Likelihood: -75.214
No. Observations: 15 AIC: 154.4
Df Residuals: 13 BIC: 155.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 25.1765 177.508 0.142 0.889 -358.306 408.659
expression 9.3424 24.174 0.386 0.705 -42.882 61.567
Omnibus: 0.463 Durbin-Watson: 1.616
Prob(Omnibus): 0.793 Jarque-Bera (JB): 0.525
Skew: 0.007 Prob(JB): 0.769
Kurtosis: 2.084 Cond. No. 131.