LID SPN TRAP

1450914_at

Ppp1r14b protein phosphatase 1, regulatory (inhibitor) subunit 14B

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
0.275 0.606 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.743
Model: OLS Adj. R-squared: 0.703
Method: Least Squares F-statistic: 18.34
Date: Thu, 03 Apr 2025 Prob (F-statistic): 7.76e-06
Time: 22:53:06 Log-Likelihood: -97.466
No. Observations: 23 AIC: 202.9
Df Residuals: 19 BIC: 207.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -27.1377 102.584 -0.265 0.794 -241.848 187.573
C(dose)[T.1] 568.7749 200.608 2.835 0.011 148.897 988.653
expression 10.4212 13.124 0.794 0.437 -17.048 37.891
expression:C(dose)[T.1] -66.5893 25.871 -2.574 0.019 -120.738 -12.440
Omnibus: 0.317 Durbin-Watson: 1.713
Prob(Omnibus): 0.853 Jarque-Bera (JB): 0.243
Skew: -0.218 Prob(JB): 0.886
Kurtosis: 2.748 Cond. No. 485.

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: 18.89
Date: Thu, 03 Apr 2025 Prob (F-statistic): 2.47e-05
Time: 22:53:06 Log-Likelihood: -100.91
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 106.6279 100.113 1.065 0.300 -102.204 315.460
C(dose)[T.1] 52.8177 8.766 6.025 0.000 34.532 71.104
expression -6.7155 12.802 -0.525 0.606 -33.420 19.989
Omnibus: 0.015 Durbin-Watson: 1.780
Prob(Omnibus): 0.993 Jarque-Bera (JB): 0.212
Skew: 0.019 Prob(JB): 0.899
Kurtosis: 2.531 Cond. No. 182.

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, 03 Apr 2025 Prob (F-statistic): 3.51e-06
Time: 22:53:06 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: 0.025
Model: OLS Adj. R-squared: -0.021
Method: Least Squares F-statistic: 0.5488
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.467
Time: 22:53:06 Log-Likelihood: -112.81
No. Observations: 23 AIC: 229.6
Df Residuals: 21 BIC: 231.9
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 199.5823 161.966 1.232 0.231 -137.244 536.409
expression -15.4290 20.828 -0.741 0.467 -58.743 27.885
Omnibus: 2.871 Durbin-Watson: 2.327
Prob(Omnibus): 0.238 Jarque-Bera (JB): 1.265
Skew: 0.063 Prob(JB): 0.531
Kurtosis: 1.858 Cond. No. 180.

CP101

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

F-statistic p-value df difference
0.019 0.893 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.482
Model: OLS Adj. R-squared: 0.340
Method: Least Squares F-statistic: 3.409
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0568
Time: 22:53:06 Log-Likelihood: -70.369
No. Observations: 15 AIC: 148.7
Df Residuals: 11 BIC: 151.6
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 163.7050 235.186 0.696 0.501 -353.935 681.345
C(dose)[T.1] -263.4574 378.401 -0.696 0.501 -1096.311 569.397
expression -11.2291 27.397 -0.410 0.690 -71.529 49.071
expression:C(dose)[T.1] 36.3064 43.924 0.827 0.426 -60.370 132.983
Omnibus: 1.053 Durbin-Watson: 0.876
Prob(Omnibus): 0.591 Jarque-Bera (JB): 0.770
Skew: -0.510 Prob(JB): 0.680
Kurtosis: 2.562 Cond. No. 526.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.450
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.902
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.0278
Time: 22:53:06 Log-Likelihood: -70.821
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 42.6012 181.529 0.235 0.818 -352.916 438.118
C(dose)[T.1] 49.0384 15.770 3.110 0.009 14.680 83.397
expression 2.8957 21.130 0.137 0.893 -43.142 48.934
Omnibus: 2.658 Durbin-Watson: 0.820
Prob(Omnibus): 0.265 Jarque-Bera (JB): 1.835
Skew: -0.834 Prob(JB): 0.400
Kurtosis: 2.612 Cond. No. 202.

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, 03 Apr 2025 Prob (F-statistic): 0.00629
Time: 22:53:06 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: 0.08007
Date: Thu, 03 Apr 2025 Prob (F-statistic): 0.782
Time: 22:53:06 Log-Likelihood: -75.254
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 27.4325 234.287 0.117 0.909 -478.714 533.579
expression 7.6990 27.208 0.283 0.782 -51.080 66.478
Omnibus: 0.232 Durbin-Watson: 1.592
Prob(Omnibus): 0.890 Jarque-Bera (JB): 0.415
Skew: 0.002 Prob(JB): 0.813
Kurtosis: 2.185 Cond. No. 202.