LID SPN TRAP

1460577_at

Jmy junction-mediating and regulatory 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
1.049 0.318 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.670
Model: OLS Adj. R-squared: 0.618
Method: Least Squares F-statistic: 12.88
Date: Mon, 03 Nov 2025 Prob (F-statistic): 7.98e-05
Time: 22:30:23 Log-Likelihood: -100.34
No. Observations: 23 AIC: 208.7
Df Residuals: 19 BIC: 213.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -75.9761 118.827 -0.639 0.530 -324.684 172.732
C(dose)[T.1] 156.1715 206.412 0.757 0.459 -275.854 588.196
expression 17.7560 16.186 1.097 0.286 -16.122 51.634
expression:C(dose)[T.1] -13.7959 29.297 -0.471 0.643 -75.116 47.524
Omnibus: 0.090 Durbin-Watson: 2.026
Prob(Omnibus): 0.956 Jarque-Bera (JB): 0.305
Skew: 0.073 Prob(JB): 0.859
Kurtosis: 2.455 Cond. No. 411.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.667
Model: OLS Adj. R-squared: 0.633
Method: Least Squares F-statistic: 19.99
Date: Mon, 03 Nov 2025 Prob (F-statistic): 1.70e-05
Time: 22:30:23 Log-Likelihood: -100.47
No. Observations: 23 AIC: 206.9
Df Residuals: 20 BIC: 210.4
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -45.1025 97.155 -0.464 0.647 -247.764 157.559
C(dose)[T.1] 59.0980 10.233 5.775 0.000 37.751 80.445
expression 13.5451 13.226 1.024 0.318 -14.045 41.135
Omnibus: 0.263 Durbin-Watson: 2.062
Prob(Omnibus): 0.877 Jarque-Bera (JB): 0.444
Skew: 0.153 Prob(JB): 0.801
Kurtosis: 2.392 Cond. No. 166.

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: Mon, 03 Nov 2025 Prob (F-statistic): 3.51e-06
Time: 22:30:23 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: 0.110
Model: OLS Adj. R-squared: 0.068
Method: Least Squares F-statistic: 2.609
Date: Mon, 03 Nov 2025 Prob (F-statistic): 0.121
Time: 22:30:23 Log-Likelihood: -111.76
No. Observations: 23 AIC: 227.5
Df Residuals: 21 BIC: 229.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 282.4715 125.722 2.247 0.036 21.019 543.924
expression -28.4429 17.611 -1.615 0.121 -65.066 8.181
Omnibus: 4.262 Durbin-Watson: 2.304
Prob(Omnibus): 0.119 Jarque-Bera (JB): 1.505
Skew: 0.049 Prob(JB): 0.471
Kurtosis: 1.751 Cond. No. 134.

CP101

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

F-statistic p-value df difference
0.090 0.769 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.610
Model: OLS Adj. R-squared: 0.504
Method: Least Squares F-statistic: 5.742
Date: Mon, 03 Nov 2025 Prob (F-statistic): 0.0130
Time: 22:30:23 Log-Likelihood: -68.233
No. Observations: 15 AIC: 144.5
Df Residuals: 11 BIC: 147.3
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -365.5683 253.846 -1.440 0.178 -924.280 193.144
C(dose)[T.1] 657.4192 288.450 2.279 0.044 22.545 1292.294
expression 57.0496 33.419 1.707 0.116 -16.505 130.605
expression:C(dose)[T.1] -79.5782 37.757 -2.108 0.059 -162.680 3.523
Omnibus: 0.259 Durbin-Watson: 1.690
Prob(Omnibus): 0.879 Jarque-Bera (JB): 0.206
Skew: -0.222 Prob(JB): 0.902
Kurtosis: 2.635 Cond. No. 495.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.453
Model: OLS Adj. R-squared: 0.362
Method: Least Squares F-statistic: 4.967
Date: Mon, 03 Nov 2025 Prob (F-statistic): 0.0268
Time: 22:30:23 Log-Likelihood: -70.777
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.7
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 107.6177 134.389 0.801 0.439 -185.191 400.427
C(dose)[T.1] 50.1923 16.028 3.132 0.009 15.270 85.115
expression -5.2951 17.642 -0.300 0.769 -43.734 33.144
Omnibus: 2.224 Durbin-Watson: 0.792
Prob(Omnibus): 0.329 Jarque-Bera (JB): 1.591
Skew: -0.763 Prob(JB): 0.451
Kurtosis: 2.531 Cond. No. 135.

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: Mon, 03 Nov 2025 Prob (F-statistic): 0.00629
Time: 22:30:23 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: -0.071
Method: Least Squares F-statistic: 0.07549
Date: Mon, 03 Nov 2025 Prob (F-statistic): 0.788
Time: 22:30:23 Log-Likelihood: -75.257
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 46.4359 172.205 0.270 0.792 -325.591 418.463
expression 6.1417 22.354 0.275 0.788 -42.152 54.435
Omnibus: 1.011 Durbin-Watson: 1.616
Prob(Omnibus): 0.603 Jarque-Bera (JB): 0.727
Skew: 0.113 Prob(JB): 0.695
Kurtosis: 1.945 Cond. No. 133.