LID SPN TRAP

1460394_a_at

Inppl1 inositol polyphosphate phosphatase-like 1

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
0.166 0.688 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.658
Model: OLS Adj. R-squared: 0.604
Method: Least Squares F-statistic: 12.18
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.000112
Time: 04:46:41 Log-Likelihood: -100.77
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.1
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 56.4940 59.198 0.954 0.352 -67.409 180.398
C(dose)[T.1] 116.4062 110.474 1.054 0.305 -114.819 347.632
expression -0.4366 11.248 -0.039 0.969 -23.978 23.105
expression:C(dose)[T.1] -12.4417 21.511 -0.578 0.570 -57.465 32.582
Omnibus: 0.883 Durbin-Watson: 1.893
Prob(Omnibus): 0.643 Jarque-Bera (JB): 0.753
Skew: 0.102 Prob(JB): 0.686
Kurtosis: 2.137 Cond. No. 158.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.652
Model: OLS Adj. R-squared: 0.617
Method: Least Squares F-statistic: 18.73
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.61e-05
Time: 04:46:41 Log-Likelihood: -100.97
No. Observations: 23 AIC: 207.9
Df Residuals: 20 BIC: 211.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 74.3000 49.715 1.495 0.151 -29.404 178.004
C(dose)[T.1] 52.7229 8.863 5.949 0.000 34.235 71.211
expression -3.8381 9.427 -0.407 0.688 -23.502 15.826
Omnibus: 0.498 Durbin-Watson: 1.916
Prob(Omnibus): 0.780 Jarque-Bera (JB): 0.576
Skew: 0.028 Prob(JB): 0.750
Kurtosis: 2.227 Cond. No. 61.5

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: 04:46:41 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: 0.036
Model: OLS Adj. R-squared: -0.010
Method: Least Squares F-statistic: 0.7869
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.385
Time: 04:46:41 Log-Likelihood: -112.68
No. Observations: 23 AIC: 229.4
Df Residuals: 21 BIC: 231.6
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 148.7466 78.138 1.904 0.071 -13.751 311.244
expression -13.3822 15.086 -0.887 0.385 -44.755 17.990
Omnibus: 1.820 Durbin-Watson: 2.656
Prob(Omnibus): 0.402 Jarque-Bera (JB): 1.098
Skew: 0.184 Prob(JB): 0.577
Kurtosis: 1.994 Cond. No. 59.2

CP101

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

F-statistic p-value df difference
0.064 0.804 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.302
Method: Least Squares F-statistic: 3.023
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0755
Time: 04:46:41 Log-Likelihood: -70.791
No. Observations: 15 AIC: 149.6
Df Residuals: 11 BIC: 152.4
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 30.7479 157.511 0.195 0.849 -315.931 377.427
C(dose)[T.1] 66.0686 276.009 0.239 0.815 -541.422 673.560
expression 6.6878 28.635 0.234 0.820 -56.338 69.713
expression:C(dose)[T.1] -2.9773 51.168 -0.058 0.955 -115.596 109.642
Omnibus: 2.780 Durbin-Watson: 0.780
Prob(Omnibus): 0.249 Jarque-Bera (JB): 1.984
Skew: -0.861 Prob(JB): 0.371
Kurtosis: 2.542 Cond. No. 232.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.452
Model: OLS Adj. R-squared: 0.360
Method: Least Squares F-statistic: 4.943
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0272
Time: 04:46:41 Log-Likelihood: -70.793
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 35.8622 125.162 0.287 0.779 -236.842 308.566
C(dose)[T.1] 50.0380 16.046 3.118 0.009 15.078 84.998
expression 5.7554 22.724 0.253 0.804 -43.757 55.267
Omnibus: 2.653 Durbin-Watson: 0.772
Prob(Omnibus): 0.265 Jarque-Bera (JB): 1.933
Skew: -0.841 Prob(JB): 0.380
Kurtosis: 2.486 Cond. No. 89.9

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: 04:46:41 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: 0.007
Model: OLS Adj. R-squared: -0.069
Method: Least Squares F-statistic: 0.09634
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.761
Time: 04:46:41 Log-Likelihood: -75.245
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 141.8965 155.716 0.911 0.379 -194.507 478.300
expression -8.9204 28.740 -0.310 0.761 -71.008 53.168
Omnibus: 0.830 Durbin-Watson: 1.600
Prob(Omnibus): 0.661 Jarque-Bera (JB): 0.656
Skew: 0.025 Prob(JB): 0.720
Kurtosis: 1.976 Cond. No. 86.2