LID SPN TRAP

1455140_at

Pitpnm3 PITPNM family member 3

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
0.042 0.840 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: 0.605
Method: Least Squares F-statistic: 12.21
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.000111
Time: 19:46:37 Log-Likelihood: -100.75
No. Observations: 23 AIC: 209.5
Df Residuals: 19 BIC: 214.0
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -138.5232 268.437 -0.516 0.612 -700.368 423.321
C(dose)[T.1] 254.4703 289.508 0.879 0.390 -351.478 860.419
expression 23.6814 32.975 0.718 0.481 -45.336 92.699
expression:C(dose)[T.1] -24.7353 35.661 -0.694 0.496 -99.374 49.904
Omnibus: 0.752 Durbin-Watson: 1.842
Prob(Omnibus): 0.687 Jarque-Bera (JB): 0.697
Skew: -0.088 Prob(JB): 0.706
Kurtosis: 2.166 Cond. No. 808.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.55
Date: Tue, 28 Jan 2025 Prob (F-statistic): 2.78e-05
Time: 19:46:37 Log-Likelihood: -101.04
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 33.6036 101.025 0.333 0.743 -177.130 244.338
C(dose)[T.1] 53.7586 9.000 5.973 0.000 34.984 72.533
expression 2.5318 12.391 0.204 0.840 -23.315 28.379
Omnibus: 0.477 Durbin-Watson: 1.840
Prob(Omnibus): 0.788 Jarque-Bera (JB): 0.569
Skew: 0.061 Prob(JB): 0.752
Kurtosis: 2.239 Cond. No. 189.

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: Tue, 28 Jan 2025 Prob (F-statistic): 3.51e-06
Time: 19:46:37 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: 0.5400
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.471
Time: 19:46:37 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 196.0140 158.426 1.237 0.230 -133.450 525.478
expression -14.4309 19.639 -0.735 0.471 -55.272 26.410
Omnibus: 3.760 Durbin-Watson: 2.474
Prob(Omnibus): 0.153 Jarque-Bera (JB): 1.672
Skew: 0.299 Prob(JB): 0.433
Kurtosis: 1.822 Cond. No. 182.

CP101

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

F-statistic p-value df difference
0.397 0.540 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.469
Model: OLS Adj. R-squared: 0.324
Method: Least Squares F-statistic: 3.239
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0643
Time: 19:46:37 Log-Likelihood: -70.553
No. Observations: 15 AIC: 149.1
Df Residuals: 11 BIC: 151.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 216.0810 281.576 0.767 0.459 -403.663 835.826
C(dose)[T.1] -31.9857 334.589 -0.096 0.926 -768.411 704.439
expression -18.9260 35.818 -0.528 0.608 -97.761 59.909
expression:C(dose)[T.1] 9.9526 43.111 0.231 0.822 -84.934 104.840
Omnibus: 3.731 Durbin-Watson: 0.839
Prob(Omnibus): 0.155 Jarque-Bera (JB): 2.399
Skew: -0.976 Prob(JB): 0.301
Kurtosis: 2.843 Cond. No. 469.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.466
Model: OLS Adj. R-squared: 0.378
Method: Least Squares F-statistic: 5.245
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.0231
Time: 19:46:37 Log-Likelihood: -70.589
No. Observations: 15 AIC: 147.2
Df Residuals: 12 BIC: 149.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 162.1211 150.690 1.076 0.303 -166.203 490.446
C(dose)[T.1] 45.1514 16.763 2.693 0.020 8.628 81.675
expression -12.0560 19.131 -0.630 0.540 -53.739 29.627
Omnibus: 4.149 Durbin-Watson: 0.814
Prob(Omnibus): 0.126 Jarque-Bera (JB): 2.599
Skew: -1.019 Prob(JB): 0.273
Kurtosis: 2.948 Cond. No. 153.

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: Tue, 28 Jan 2025 Prob (F-statistic): 0.00629
Time: 19:46:38 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: 0.144
Model: OLS Adj. R-squared: 0.078
Method: Least Squares F-statistic: 2.184
Date: Tue, 28 Jan 2025 Prob (F-statistic): 0.163
Time: 19:46:38 Log-Likelihood: -74.135
No. Observations: 15 AIC: 152.3
Df Residuals: 13 BIC: 153.7
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 337.6512 165.356 2.042 0.062 -19.579 694.882
expression -31.7876 21.509 -1.478 0.163 -78.254 14.679
Omnibus: 0.520 Durbin-Watson: 1.557
Prob(Omnibus): 0.771 Jarque-Bera (JB): 0.548
Skew: -0.028 Prob(JB): 0.760
Kurtosis: 2.065 Cond. No. 137.