LID SPN TRAP

1448957_at

Rbpj recombination signal binding protein for immunoglobulin kappa J region

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
0.647 0.431 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.661
Model: OLS Adj. R-squared: 0.607
Method: Least Squares F-statistic: 12.33
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000104
Time: 03:42:43 Log-Likelihood: -100.67
No. Observations: 23 AIC: 209.3
Df Residuals: 19 BIC: 213.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 105.3193 152.484 0.691 0.498 -213.833 424.472
C(dose)[T.1] 95.2676 198.331 0.480 0.636 -319.845 510.380
expression -6.9007 20.571 -0.335 0.741 -49.956 36.155
expression:C(dose)[T.1] -4.9619 26.152 -0.190 0.852 -59.700 49.776
Omnibus: 1.330 Durbin-Watson: 1.884
Prob(Omnibus): 0.514 Jarque-Bera (JB): 0.900
Skew: 0.091 Prob(JB): 0.638
Kurtosis: 2.048 Cond. No. 476.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.660
Model: OLS Adj. R-squared: 0.626
Method: Least Squares F-statistic: 19.42
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.06e-05
Time: 03:42:43 Log-Likelihood: -100.70
No. Observations: 23 AIC: 207.4
Df Residuals: 20 BIC: 210.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 128.0571 91.981 1.392 0.179 -63.812 319.926
C(dose)[T.1] 57.6905 10.187 5.663 0.000 36.441 78.940
expression -9.9706 12.393 -0.805 0.431 -35.821 15.880
Omnibus: 1.287 Durbin-Watson: 1.949
Prob(Omnibus): 0.525 Jarque-Bera (JB): 0.880
Skew: 0.072 Prob(JB): 0.644
Kurtosis: 2.052 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: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 03:42:43 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: 0.115
Model: OLS Adj. R-squared: 0.073
Method: Least Squares F-statistic: 2.727
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.114
Time: 03:42:43 Log-Likelihood: -111.70
No. Observations: 23 AIC: 227.4
Df Residuals: 21 BIC: 229.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -128.2284 126.095 -1.017 0.321 -390.458 134.001
expression 27.3057 16.534 1.652 0.114 -7.078 61.690
Omnibus: 1.485 Durbin-Watson: 2.440
Prob(Omnibus): 0.476 Jarque-Bera (JB): 1.238
Skew: 0.396 Prob(JB): 0.539
Kurtosis: 2.184 Cond. No. 144.

CP101

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

F-statistic p-value df difference
0.069 0.797 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.483
Model: OLS Adj. R-squared: 0.342
Method: Least Squares F-statistic: 3.421
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0563
Time: 03:42:43 Log-Likelihood: -70.357
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 124.2819 167.819 0.741 0.474 -245.086 493.650
C(dose)[T.1] -148.1570 246.826 -0.600 0.561 -691.418 395.104
expression -8.0337 23.657 -0.340 0.741 -60.102 44.035
expression:C(dose)[T.1] 28.9522 35.822 0.808 0.436 -49.891 107.796
Omnibus: 2.273 Durbin-Watson: 0.793
Prob(Omnibus): 0.321 Jarque-Bera (JB): 1.531
Skew: -0.578 Prob(JB): 0.465
Kurtosis: 1.946 Cond. No. 283.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.361
Method: Least Squares F-statistic: 4.947
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0271
Time: 03:42:43 Log-Likelihood: -70.790
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 34.9234 124.414 0.281 0.784 -236.152 305.999
C(dose)[T.1] 50.8514 16.915 3.006 0.011 13.997 87.705
expression 4.5932 17.506 0.262 0.797 -33.548 42.735
Omnibus: 2.283 Durbin-Watson: 0.872
Prob(Omnibus): 0.319 Jarque-Bera (JB): 1.725
Skew: -0.772 Prob(JB): 0.422
Kurtosis: 2.386 Cond. No. 112.

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: 03:42:43 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: 0.039
Model: OLS Adj. R-squared: -0.035
Method: Least Squares F-statistic: 0.5292
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.480
Time: 03:42:43 Log-Likelihood: -75.001
No. Observations: 15 AIC: 154.0
Df Residuals: 13 BIC: 155.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 197.1561 142.608 1.383 0.190 -110.930 505.242
expression -15.0318 20.663 -0.727 0.480 -59.672 29.608
Omnibus: 0.695 Durbin-Watson: 1.463
Prob(Omnibus): 0.706 Jarque-Bera (JB): 0.614
Skew: -0.046 Prob(JB): 0.736
Kurtosis: 2.013 Cond. No. 101.